Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

Two-dimensional (2D) materials are traditionally associated with the sheets forming bulk layered
compounds bonded by weak van der Waals (vdW) forces with graphene derived from bulk
graphite being the most prominent example. The weak inter-layer interaction leads to a natural
structural separation of the 2D subunits in the crystals, giving rise to the possibility of mechanical
and liquid-phase exfoliation. The anisotropic interaction also provided suitable structural criteria
for the computational search for such traditional 2D materials which predicted about 2000
exfoliable compounds [1].
However, the unexpected experimental realization of atomically thin sheets from non-vdW
bonded compounds, for which the previously formulated descriptors are not applicable, recently
opened up a new direction in the research on 2D materials [2]. These non-vdW 2D compounds
exhibit qualitatively new features due to the unsaturated bonds at their surfaces. Here, we present
our recent data-driven search for representatives of this novel materials class [3]. By screening
the AFLOW database according to structural prototype information 28 new, potentially
synthesizable candidates are outlined. The oxidation state of the surface cations is found to
regulate the exfoliation energy with low oxidation numbers giving rise to weak bonding – thus
providing an enabling descriptor to obtain novel 2D materials. The candidates showcase a
versatile spectrum of appealing electronic, optical and magnetic properties suggesting in
particular spintronic applications.
[1] N. Mounet et al., Nat. Nanotechnol. 13, 246 (2018).
[2] A. Puthirath Balan et al., Nat. Nanotechnol. 13, 602 (2018).
[3] R. Friedrich et al., Nano Lett. 22, 989 (2022).
Acknowledgements:
The authors thank the HZDR Computing Center, HLRS, Stuttgart, Germany, and TU Dresden
Cluster “Taurus” for generous grants of CPU time. R.F. acknowledges support from the
Alexander von Humboldt foundation under the Feodor Lynen research fellowship. A.V.K. thanks
the German Research Foundation (DFG) for the support through Project KR 4866/2-1 and the
collaborative research center “Chemistry of Synthetic 2D Materials” SFB-1415-417590517.

A study of acinide amidinate complexes is presented with an emphasis on magnetic properties. Main method is paramagnetic NMR spectroscopy highlighting pseudo-contact shifts and minor Fermi contact contributions to the observed NMR chemical shifts. As an outlook EPR spectroscopy and upcoming SQUID magnetometry is advertised.

The Biotechnology Division at HIF uses diverse biomolecules for their application in resource recovery. This talk gives an overview on running projects and available biomolecules.

Objectives and Introduction
Parkinson’s disease is caused by degeneration of nigro-striatal dopaminergic neurons and denervation of the target neurons in the neostriatum. The resulting disruption of dopaminergic modulation produces an imbalance between antagonistic pathways in the basal ganglia leading to rigidity, tremor, and bradykinesia [1]. One of several treatment options is the so called deep-brain-stimulation (DBS), whereby an electrode is implanted to re-equilibrate the nervous pathways and rescue the pathological imbalance. Though highly effective, DBS is linked to a very complex surgical procedure and can lead to adverse neurological effects [2,3].
The goal of this project is to enable a selective stimulation of striatal dopaminoceptive neurons from outside the brain through polymeric photovoltaic nanoparticles which are transported to the neostriatum using an engineered M13 bacteriophage as nanocarrier. These phages were chosen as a biovector since their filamentous envelop, formed by the major coat protein P8, offers a large surface area which can be modified easily. To monitor its biodistribution in the organism, the engineered bacteriophage is being with [64Cu]CuCl2 enabling PET imaging.

Methods:

Methods for analysis of the phages by TLC, HPLC and MALDI-TOF MS have been developed. To allow labeling with 64Cu, the bacteriophages were functionalized with 1,4,7‑triazacyclononane,1‑glutaric acid‑4,7-acetic acid (NODA-GA) and the conjugation reaction was analyzed by MALDI-TOF MS. The M13-NODA-GA conjugates were purified using HPLC-SEC. After labeling of the M13-NODA-GA conjugates with inhouse produced [64Cu]CuCl2 and purification by spin filtration, the radiolabeling efficiency was analyzed by radio-TLC and radio-HPLC-SEC. PET imaging was carried out in mice and scans were taken every 15 min up to 1 h after intravenous injection. The mice were sacrificed 70 min post-injection and the radioactivity accumulated in different organs was measured.

Results:

Conjugation of the NODA-GA chelator to the major capsid protein P8 of the phages was confirmed by MALDI-TOF MS. Subsequent radiolabeling of the bioconjugates was achieved with a specific activity of 17 MBq/pmol and a radiochemical purity of 98.5% was obtained as determined by radio-TLC as well as radio-HPLC-SEC. A rapid accumulation of the radiolabeled M13-NODA-GA conjugates in the murine liver was observed by PET imaging 15 min post-injection. According to ex vivo analysis, approximately 80% of the injected dose was accumulated in the liver, and smaller amounts were detected in spleen (~ 5%) and in the gastrointestinal tract (<1%).

Conclusions:

The present study shows for the first time the successful chemical modification and 64Cu-labeling of NODA-GA-functionalized M13 bacteriophages as well as their biodistribution. Furthermore, a set of analytical methods is presented allowing the assessment of bacteriophage purity, integrity as well as stability in future studies.

Acknowledgements:

The research work was financially supported by the EU Joint Programme – Neurodegenerative Disease Research (NeuroPhage, Project ID: JPND2020-568-126). The financial support (Project ID: 01ED2108) by the German Federal Ministry of Education and Research (BMBF) is gratefully acknowledged.

References:

[1] Balestrino R and Schapira A H V, Eur. J. Neurol. 2020; 27: 27–42.
[2] Stoker T B et al., Front. Neurosci. 2018; 12:693.
[3] Krack P et al., Mov. Disord., 2019; 34: 12

The previous investigations showed that the pulsed aeration leads to higher oxygen mass transfer rates in specific cases. In narrow aeration columns positive effects could be shown for higher pulsation frequencies whereas in the DN900 column these effects were only pre-sent for higher flow rates in low frequencies. Investigations of bubble size distributions showed that the bubble size is not the only factor leading to increased oxygen mass transfer rates and that the positive effects depend on the geometry and the liquid flow behavior in the reactor. Further experiments therefore are focused on fundamental investigation the liquid flow behavior during pulsed aeration. A new experimental setup was constructed to study these hydrodynamic effects in 2D to enable a better understand of the underlying effects.

Objectives:

The Ca2+-dependent transamidase activity of transglutaminase 2 (TGase2) is tightly regulated in healthy cells but can be utilized by various cancer cells to support their survival and progression. Therefore, molecules targeting this enzyme are promising candidates for the functional characterization of TGase2 in tumors. Recently, we developed an 18F-labeled irreversible inhibitor and highlighted its potential as radiometric tool for the in vitro characterization of TGase2. Herein, we report on the kinetic characterization of a 123I-labeled Nε-acryloyllysine, [123I]1, and its use for quantifying the functional expression of TGase2 in tissues
Methods:
[123I]1 was synthesized as recently presented [1]. The inhibitory potency of [123I]1 by means of its kinact/KI value was determined by a radio-TLC method using recombinant human TGase2. In vitro autoradiography was performed with fresh-frozen sections (12 µm) of several organs (heart, kidney, liver, spleen, and muscle), extracted from healthy NMRI nude mice. Binding experiments with [123I]1 were conducted at 0.7 MBq/mL in MOPS buffer at pH 7.4 containing 3 mM CaCl2 and 5 mM DTT. Non-specific binding was assessed in the presence of the TGase2 inhibitor Z006. Different molar activities (Am) were adjusted by the addition of compound 1.
Results:
[123I]1 was reliably obtained in high (radio)chemical purities of >99% and radiochemical yields of 79±6% (n=8). The Am was determined to be >6 TBq/µmol and the kinact/KI value to be 10,200 M-1s-1 (±1,000). Association (Figure 1) of n.c.a. [123I]1 at 37°C over 4 h to tissue sections furnished a high binding capacity and excellent ratios of total binding (TB) to non-specific binding (NSB). However, assessment of the quantitative TGase2 expression is limited as the inhibition rate at n.c.a. level is too low to achieve complete radioligand binding. Therefore, Am values of 70, 14, 7, and 1 GBq/µmol were adjusted to increase the association rates. A value of 7 GBq/µmol appeared to be optimal based on the extent of binding and the TB/NSB ratios. Higher Am values of 70 and 14 GBq/µmol still led to incomplete reaction and thus a lower apparent TGase2 concentration. In contrast, a Am value of 1 GBq/µmol resulted in extensive self-block, as indicated by an increased NSB (Figure 1). The highest TGase2 concentration has been observed in the heart and was lowest in muscle, with values of 1.3 and 0.2 pmol/mm3, respectively. Dissociation of [123I]1 under similar conditions proved the irreversible binding to TGase2 as only a minimal amount (<10%) of total bound radioligand dissociates over 4 h.
Conclusions:
A detailed in vitro and ex vivo evaluation of the TGase2-inhibitor [123I]1 proved its applicability as radiometric tool for quantifying the functional expression of that enzyme. The observed low reaction rate of [123I]1 at high Am values was compensated by standard addition which might also have implications for the in vivo application of this compound.
Acknowledgements:
The authors thank ROTOP Radiopharmacy for continuously providing [123I]iodide. Financial support by European Regional Development Fund (EFRE) for ML, HJP and RW is gratefully acknowledged.
References:
[1] Laube et al, Nucl. Med. Biol., 2021, 96–97S, S79-S80,

In addition to classic functions of proteins such as acting as biocatalyst or binding partner, the conformational states of proteins and their remodeling upon stimulation needs to be considered. A prominent example that undergoes comprehensive conformational remodeling, is transglutaminase 2 (TGase 2), whose distinct conformational states are closely related to particular functions. Its involvement in various pathophysiological processes, including fibrosis and cancer, motivates the development of theranostic agents, particularly based on inhibitors that are directed towards the transamidase activity. In this context, the ability of such inhibitors to control the conformational dynamics of TGase 2 emerges as an important parameter, and methods to assess this property are in great demand. Herein, we describe the application of the switchSENSE® principle to detect conformational changes caused by three irreversibly binding Nε-acryloyllysine piperazides, which are suitable radiotracers candidates of TGase 2. The switchSENSE® technique is based on DNA levers actuated by alternating electric fields. These levers are immobilized on gold electrodes with one end, and at the other, distal end of the lever, the TGase 2 is covalently bound. A novel computational method is introduced for describing the resulting lever motion to quantify the extent of stimulated conformational TGase 2 changes. Moreover, as a complementary biophysical method, native polyacrylamide gel electrophoresis was performed under similar conditions to validate the results. Both methods prove the occurrence of an irreversible shift in the conformational equilibrium of TGase 2, caused by the binding of the three studied Nε-acryloyllysine piperazides.

Peptide receptor radionuclide therapy (PRRT) or radioligand therapy (RLT) represent valuable nuclear medical approaches for the treatment of tumors. It can lead to an unparalleled therapeutic success with [177Lu]Lu-DOTA-TATE1 and [177Lu]Lu-PSMA-6172 being the most striking examples, which were recently approved as Lutathera and Pluvicto, respectively. Besides stimulating the search for further targeted radiopharmaceuticals,3 there are ongoing efforts for optimizing PRRT and RLT apart from the tumor targeting itself. A non-negligible aspect for PRRT and RLT is radiation induced toxicity to healthy tissue, in particular bone marrow and kidneys but also other organs such as salivary glands in case of RLT with [177Lu]Lu-PSMA-617,4,5,6 that also limits the height of the applied activity amount. Due to the high hydrophilicity of somatostatin and PSMA ligands, their primary route of excretion proceeds via the kidney into the urine. This can be accompanied by a significant receptor-mediated reabsorption of the radiopharmaceuticals into the proximal tubular cells followed by lysosomal degradation, which ultimately result in a prolonged retention of the radiolabel and thus, a high dose exposure to the kidneys.7,8 Several nephroprotective strategies are pursued to reduce the tubular reabsorption during PRRT or RLT either by modifying the radiopharmaceutical itself or by co-injection of blocking substances.4,7 The talk will give an overview about the different strategies for reducing the renal uptake with a special emphasis on the targeting of renal brush border enzymes by the introduction of cleavable peptide linkers into targeted radiopharmaceuticals.

References:

1. Strosberg et al. Phase 3 trial of 177Lu-Dotatate for midgut neuroendocrine tumors. N. Engl. J. Med. 2017, 376, 125-135.
2. Sartor et al. Lutetium-177-PSMA-617 for metastatic castration-resistant prostate cancer. N. Engl. J. Med. 2021, 385, 1091-1103.
3. Nicolas et al. New Developments in peptide receptor radionuclide therapy. J. Nucl. Med. 2019, 60, 167-171.
4. Geenen et al. Overcoming nephrotoxicity in peptide receptor radionuclide therapy using [177Lu]Lu-DOTA-TATE for the treatment of neuroendocrine tumours. Nucl. Med. Biol. 2021, 102-103, 1-11.
5. Gallyamov et al. Renal outcomes of radioligand therapy: experience of 177lutetium-prostate-specific membrane antigen therapy in metastatic castrate-resistant prostate cancer. 2020, 13, 1049-1055.
6. Kratochwil et al. EANM procedure guidelines for radionuclide therapy with 177Lu-labelled PSMA ligands (177-PSMA-RLT). 2019, 46, 2536-2544.
7. Vegt et al. Renal toxicity of radiolabeled peptides and antibody fragments: Mechanisms, impact on radionuclide therapy, and strategies for prevention. J. Nucl. Med. 2010, 51, 1049-1058.
8. Vegt et al. Renal uptake of different radiolabelled peptides is mediated by megalin: SPECT and biodistribution studies in megalin-deficient mice. Eur. J. Nucl. Med. Mol. Imaging 2011, 38, 623-632.

Besides optimizing the vector molecule for its interaction toward the respective target protein, a modern tool in the field of radiopharmaceutical cancer therapy is the introduction of albumin-binding moieties to modulate the pharmacokinetic properties [1]. Basically, the approach aims at increasing the time-integral uptake of radioactivity in the tumor, which consequently increases the total radiation dose delivered to the tumor and thus, might improve the therapeutic outcome. In contrast, binding to albumin goes along with a prolonged blood circulation time and thus, a higher radiation dose to healthy tissues, in particular the red bone marrow. Vector molecules of various targets, including folate receptor, prostate specific membrane antigen (PSMA), and fibroblast activation protein (FAP), were equipped with albumin-binding moieties and promising preclinical studies were reported. However, the actual implications of binding to albumin appear less or even erroneously understood. In this context, the protracted tumor uptake is an important aspect, which originates from lowering the unbound fraction of the radioligand in the blood. Moreover, considering the free drug hypothesis [2], the usually observed gain in tumor uptake requires another tumor uptake mechanism of the albumin-bound radioligand to be operational and is not a result of the prolonged blood circulation time.
Based on our own data to albumin-binding radioligands of the somatostatin receptor subtype 2 (SST2) [3], the talk will give insight into the pharmacokinetic implications of albumin binding with a special focus on the relation of the binding affinity to albumin and the resulting biodistribution of the radioligand, which is also of importance for the radiation-induced toxicity to healthy tissues.

References:

1. Brandt M et al.: Nucl. Med. Biol. 2019, 70: 46–52.
2. Smith D A, Di L, Kerns E H: Nat. Rev. Drug Discov. 2010, 9(12): 929–939.
3. Brandt F et al.: J. Med. Chem. 2022, 65(1): 710–733.

As instalações de campo pulsado são conhecidas pelos altos campos magnéticos que podem produzir (até 100 Tesla). Por outro lado, para aplicações em refrigeração magnética, os campos magnéticos de até 2 T entram em questão. Portanto, a realização de experimentos com campos magnéticos pulsados para estudar materiais multicalóricos parece ser, pelo menos à primeira vista, supérfluo. No Dresden High Magnetic Field Laboratory, desenvolvemos a técnica para medir diretamente a variação de temperatura da amostra sob campos aplicados que podem ir além de 50 T. A curta duração do pulso (normalmente entre 10 a alguns 100 ms) proporciona boas condições adiabáticas durante o experimento permitindo a medição direta da variação adiabática de temperatura de um material, ΔTad, sem qualquer perda de calor. Além de medir o ΔTad de nossas amostras, mostramos que os campos magnéticos pulsados são uma ferramenta poderosa para estudar e caracterizar materiais multicalóricos. O regime de campos altos permite determinar, por exemplo, o valor de saturação do efeito magnetocalórico e sua máxima extensão em temperature ou podemos induzir a transição do material em uma ampla faixa de temperatura. Efeitos irreversíveis devido à histerese, dinâmica de transição ou a dependência do protocolo de medição do efeito magnetocalórico são geralmente estudados em detalhes em nossas medições. Além disso, a possibilidade de combinar diferentes técnicas e medir simultaneamente a magnetostricção, magnetização e variações de temperatura de uma amostra dá uma visão completa das propriedades do material. Em minha palestra, apresentarei a técnica para determinar diretament o efeito magnetocalórico em campos magnéticos pulsados e mostrarei alguns exemplos de ligas de Heusler.

Symposium FM
State-of-the-art Research and Applications of Shape Memory Alloys

Virtual conference contribution by invitation

No short version available

Zu diesem eingeladenen Vortrag auf der Calorics 2022 (Kälte-Klima-Tag 2022) in Wien
lag keine Kurzfassung vor.

Purpose/Objective
Studies within the European Particle Therapy Network (EPTN) have shown a large variation in the
estimation of proton stopping-power ratio (SPR) from computed tomography (CT) scans across European
proton centres. To standardise the SPR prediction process, we present a step-by-step guide on the
Hounsfield look-up table (HLUT) specification process. This consensus guide was created within the ESTRO
Physics Workshop 2021 on CT in radiotherapy in a joint effort with the EPTN Work Package 5 (WP5).
Material/Methods
The HLUT specification procedure is divided into six steps (Figure 1): 1) phantom setup, 2) CT scanning, 3)
CT number extraction, 4) SPR determination, 5) HLUT specification, 6) HLUT evaluation. For each step,
considerations and recommendations are given based on literature and additional experimental
evaluations. Appropriate phantom inserts are tissue-equivalent for both X-ray and proton interactions
and are scanned in head- and body-sized phantoms to mimic different beam hardening conditions. Soft
tissue inserts can be scanned together, while bone inserts are scanned individually to avoid imaging
artefacts. CT numbers are extracted in material-specific regions-of-interest covering the inner 70% of each

phantom insert in-plane and several axial CT slices in scan direction. For an appropriate HLUT specification,
the SPR of phantom inserts is experimentally determined in proton range measurements at an energy
>200 MeV, and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. By
including both phantom inserts and tabulated human tissues in the HLUT specification, the influence of
the respective dataset-specific uncertainties are mitigated and thus the HLUT accuracy is increased.
Piecewise linear regressions are performed between CT numbers and SPRs for four individual tissue
segments (lung, adipose, soft tissue and bone) and then connected with straight lines. A thorough but
simple validation is finally performed.
Results
The individual challenges and best practices are explained comprehensively for each step. A well-defined
strategy for specifying the connection points between the individual line segments of the HLUT is
presented. The guide was exemplarily performed on three CT scanners from different vendors, proving its
feasibility for SPR prediction on both single-energy CT scans and virtual monoenergetic CT images derived
from dual-energy CT (Figure 2).
Conclusion
A comprehensive step-by-step guide on CT-based HLUT specification is described, representing a
consensus found within the ESTRO Physics Workshop and the EPTN WP5. The presented
recommendations and examples can contribute to increase the accuracy in proton range prediction for
treatment planning in individual proton centres and, following from this, reduced inter-centre variations
in SPR prediction and thus a better comparability of treatment data between different centres for multi-
centre clinical studies.

We use experiments to explore the effect of surfactants on bubble-induced turbulence (BIT) at different scales, considering how the bubbles affect the flow kinetic energy, anisotropy and extreme events. To this end, high-resolution Particle Shadow Velocimetry measurements are carried out in a bubble column in which the flow is generated by bubble swarms rising in water for two different bubble diameters ($3$ mm $\&$ $4$ mm) and moderate gas volume fractions ($0.5\%\sim1.3\%$). To contaminate the flow, different amounts of 1-Pentanol were added to the flow, leading to different bubble shapes and surface boundary conditions. The results reveal that with increasing surfactant concentration, the BIT generated increases in strength, even though bubbles of a given size rise more slowly with surfactants. We also find that the level of anisotropy in the flow is enhanced with increasing surfactant concentration for bubbles of the same size, and that for the same surfactant concentration, smaller bubbles generate stronger anisotropy in the flow. Concerning the intermittency quantified by the normalized probability density functions of the fluid velocity increments, our results indicate that extreme values in the velocity increments become more probable with decreasing surfactant concentration for cases with smaller bubbles and low gas void fraction, while the effect of the surfactant is much weaker for cases with larger bubble and higher void fractions.

The multicaloric effect is described by a temperature or entropy change of a material triggered by external stimuli applied or removed simultaneously or sequentially. The prerequisite for this is a material exhibiting multiple ferroic states. However, direct measurements of the effect are rarely reported. Now, for this reason, we built a measurement device allowing to determine the adiabatic temperature change in pulsed magnetic fields and, simultaneously, under the influence of a uniaxial load. We selected the all-d-metal Heusler alloy Ni–Mn–Ti–Co for our first test because of its enhanced mechanical properties and enormous magneto- and elastocaloric effects. Ni–Mn–Ti–Co was exposed to pulsed magnetic fields up to 10 T and uniaxial stresses up to 80 MPa, and the corresponding adiabatic temperature changes were measured. With our new experimental tool, we are able to better understand multicaloric materials and determine their cross-coupling responses to different stimuli.

In the present contribution, we demonstrate the application of a hybrid multiphase CFD approach, which allows for simulating dispersed phases as well as resolved interfaces within an Eulerian framework, for the flow on distillation trays for the first time. The morphology adaptive multifield two-fluid model is exemplified for a generic tray setup with a single trapezoid fixed valve. Instead of fully resolving its geometry in the computational grid, the gas inlets are emulated by implementing mass and momentum sources that are applied to local cell zones. Different zone types in terms of volume and curtain area are tested and compared. The simulation results are verified with experimental data from a lab-scale test rig with air-water flow. Local phase fractions were measured using a conductivity sensor array. The comparison of simulated and experimental results reveals that the relevant time-averaged and transient flow characteristics can be predicted satisfactorily if at least an approximate representation of the valve's geometry in the computational grid is given. However, local differences are observed among the simulated phase distributions due to the varying cell zone volume and hence maximum intensity of injected momentum.

Doped IrTe2 is considered a platform for topological superconductivity and therefore receives currently a lot of interest. In addition, the superconductivity in these materials exists in close vicinity to electronic order and the formation of molecular orbital crystals, which we explore here by means of high-pressure single crystal x-ray diffraction in combination with density functional theory. Our crystallographic refinements provide detailed information about the structural evolution as a function of applied pressure up to 42 GPa. Using this structural information for density functional theory calculations, we show that the local multicenter bonding in IrTe2 is driven by changes in the Ir-Te-Ir bond angle. When the electronic order sets in, this bond angle decreases drastically, leading to a stabilization of a multicenter molecular orbital bond. This unusual local mechanism of bond formation in an itinerant material provides a natural explanation for the different electronic orders in IrTe2. It further illustrates the strong coupling of the electrons with the lattice and is most likely relevant for the superconductivity in this material.

Recent advancements in hyperspectral imaging systems have opened up possibilities for identifying and distinguishing materials based on their spectral characteristics, as every material has its unique spectral signature. In our work, we present a novel approach for detecting and distinguishing copper and aluminum foils present in shredded lithium-ion batteries (LIBs) using convolutional autoencoder for hyperspectral unmixing. In hyperspectral applications, unmixing is a key procedure for estimating spectral signatures of pure materials (endmembers) as well as the corresponding fractional spatial extent (abundances) of endmembers in mixed pixels of hyperspectral images (HSIs). We perform hyperspectral unmixing on a real hyperspectral dataset using a convolutional autoencoder with sparse regularization. We evaluate the performance of the autoencoder framework using VNIR (visible and near-infrared) HSI data acquired with the Specim FX10 hyperspectral sensor. Our experimental unmixing results demonstrate that convolutional autoencoder showed a significant improvement in unmixing performance compared with competing unmixing methods. To the best of our knowledge, this work is the first to implement hyperspectral unmixing using autoencoder in LIB recycling, which is highly significant for automated sorting of valuable metals in LIB recycling industrial applications.

Uncrewed aerial vehicles (UAVs), also referred to as drones, have become a major developing branch in the field of autonomous vehicles. Lightweight, flexible, and inexpensive, UAVs can offer individual solutions for a wide range of applications. Important assets are the fast turnaround times and high customizability of UAV platforms and their respective payloads. This targeted and adapted surveying allows us to map chemical and physical properties of complex or even inaccessible terrains. Regulatory and technical barriers, however, limit the product of take-off weight and endurance for civil and research use. Common compromises are light-weight systems with high ground coverage and small payloads (e.g., small, fixed-wing drones), and heavy-duty UAV (e.g., multi-copters) with shorter flight times. The latter in turn provide the opportunity to deploy heavier, highly technological equipment to gather more information on the depicted scene.
This trade-off causes a dilemma, in particular for drone-borne material mapping with spectral imaging sensors. Light-weight systems can achieve sufficient aerial coverage within a reasonable time, however, light-weight cameras are mostly limited to uncooled systems covering the visible and near-infrared range of the electromagnetic spectrum. Such sensors allow characterization only for a limited number of materials with often low confidence. The lack of subsurface information acquired with these sensors further limits the provided data value, especially in regions with extensive vegetation or soil coverage.
Systems analyzing subsurface geophysical properties or providing enhanced spectroscopic information (e.g., by extending the detection range towards longer wavelengths) are often heavier and/or require adapted drone design and flight planning. Using such systems to cover a full prospect area at the required detail is tedious. Slow flight speeds, repeated battery changes, and a tremendous amount of data to process cause often intolerable delays. Short turnaround times, however, are key in the respective application fields, as either environmental conditions or the mission itself may offer a limited time-window for data acquisition and initial result delivery. This is a major hurdle for many potential UAV applications such as greenfield mineral exploration, search & rescue, or leak/pollution detection, where targets of interest are often remote, small-scaled and of unknown exact location.
We present an innovative concept capable of performing rapid and reliable target characterization via a domain approach. The core idea is the development of a task-distributed drone network, combining the strengths of light-weight and heavy-duty systems. As high-detail data is only acquired where it matters, long flight-times and large volumes of superfluous data can be avoided from the start. This also reduces processing time, computational requirements, as well as the impact of the survey on the environment. As a first step, we demonstrate the challenges and opportunities provided by such multi-modal, drone-based mapping in the framework of mineral exploration. In several case studies, we also showcase the added value of integrating surface (spectral imaging) and subsurface (geophysical) data for better target characterization and give an outlook on autonomous and multi-drone data acquisition for a targeted and more efficient characterization.

Uncrewed aerial vehicles (UAVs), also known as drones, have become an important branch of development in the field of innovative exploration technology. Short turnaround times, the highly adaptable nature of drone platforms, and the growing variety of sensors that can be deployed are driving increased interest in implementing drone-based mapping into mineral exploration workflows. Drone-based adaptation of technologies previously applied in airborne or ground-based campaigns now enables rapid mapping of geologic targets in unprecedented detail. The ability to objectively map topography and surface mineral composition using imaging sensors (including RGB, multi- and hyperspectral cameras) and subsurface physical properties using geophysical sensors such as magnetics and radiometrics has shown to add impressive value to conventional mapping workflows.
Regulatory and technical barriers, however, often force a difficult trade-off between sensor payload and flight time on drone-based surveys. In an applicational field, where short turnaround times are key and targets of interest are remote and difficult to access, this often results in the use of light-weight sensors and single-sensor acquisitions.
In this contribution, we demonstrate the challenges and opportunities provided by multi-modal drone-based data in the framework of mineral exploration. In several case studies, we showcase the added value of integrating surface (spectral imaging) and subsurface (geophysical) data for better target characterization. We finally give an outlook on autonomous and multi-drone data acquisition for a targeted and more efficient characterization.

Conventional geological mapping is limited by survey size, access, complex target geometries and public acceptance. We develop drone-based mapping platforms to mitigate these challenges.
Drones provide unique platforms for lightweight sensors, allowing:

• Rapid deployment and objective data collection
• Accurate 3D reconstruction using photogrammetry or Lidar sensors
• Large coverage at high spatial (or temporal) resolution
• Safe and practical access to complex relief (e.g., cliffs, mines)
• Mapping of the surface and subsurface

The talk gives an insight into the current trends in non-invasive and efficient exploration technologies, showcasing actual ongoing research examples on drone-based mapping, hyperspectral imaging, outcrop-sensing and drill core analysis.

Spent lithium-ion batteries (LIBs) contain critical raw materials that needs to be recirculated in the battery supply chain. In this work, the joint recovery of cathode and anode materials by froth flotation is proposed. Flotation is a water-intensive process, additionally, the water quality affects the flotation efficiency. In prospect of water-saving strategy, the process water characteristic and the effect of process water recirculation are also investigated. In this work, a pyrolyzed black mass(< 100 μm) is used, containing 43.8% C, 2.5% Li, and 39.4% Co, Ni and Mn as metal oxide. After flotation, a graphite recovery of 95% in the O/F product and a metal recovery of 80% in U/F product are achieved. The process water characterization reveals accumulation of Li ions to a potential value, up to 2600 mg/L.

In Europe, an era of battery recycling is shaping new industries as spent lithium-ion batteries (LIBs) are considered, in addition to mining, as a potential source of battery raw materials with high prospect of environmental and economic incentives. Several recycling routes are being proposed combining hydrometallurgy and pyrometallurgy techniques and with emphasis on the mechanical pre-treatment (i.e., sorting, shredding, sieving) to pre-concentrate the LIBs component into a coarse metal fraction (Fe/plastics, Al, and Cu rich) and a fine electrode powder fraction (graphite and cathode active material - CAM). During LIB recycling, particularly in wet operations, specific LIB components such as Li and F easily dissolve into the water which results to material losses. For instance, immersion of an INR18650 battery in 1L water yielded a 100 mg/L Li and 140 mg/L F concentration. Moreover, the implementation of thermal treatment to liberate the electrode powder from the metal foils causes a carbothermic reduction of CAMs creating a more soluble Li compound that can be recovered by water leaching. Recently, froth flotation of pyrolyzed black mass aiming to separate graphite and CAM revealed a rather high concentration of Li in process water of 1,000 mg/L representing a 45% Li dissolution. A concentration of 2,600 mg/L Li was also reached during water recirculation in the flotation experiment. Hence, this work aims at the recovery of lithium from process water of battery recycling processes through the ion-exchange processes. Using commercial IX resins, the preliminary result shows a recovery of ~80% Li from flotation process water can be achieved after 15-min contact time. Precipitation experiments were also performed which produced a ~94-99% purity Li2CO3 powder.

Digital outcrop models have become a powerful tool for detailed structural mapping (Bemis et al., 2014), as they allow geological exposures to be characterized in unprecedented detail while simultaneously mitigating access limitations that hinder conventional mapping approaches. In this contribution we present an emerging workflow that fuses digital outcrop data with high resolution ground- and UAV- based hyperspectral imaging products to better discriminate key lithological units (marker horizons) and alteration trends (Lorenz et al., 2018; Kirsch et al., 2019). In some settings, hyperspectral data allows key mineral abundances to be mapped directly to create qualitative mineral maps (e.g., Thiele et al., 2022), however for structural mapping purposes the identification of distinctive marker horizons can be sufficient (e.g., Thiele et al., 2021). We illustrate this workflow with several examples from the Iberian Pyrite Belt (Spain), where the hyperspectral data helped constrain the geometry of deformed volcanic units hosting massive sulphide mineralization. Finally, a preliminary approach for combining (hyperspectral) digital outcrop data and 3-D interpolation algorithms to derive 3-D structural models of open-pit mines is discussed.

Acknowledgements: This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 776487.

Bemis, S.P., Micklethwaite, S., Turner, S., James, M.R., Akciz, S., Thiele, S.T., & Ali Bangash, H. (2014): Ground-Based and UAV-Based Photogrammetry: A Multi-Scale, High-Resolution Mapping Tool for Structural Geology and Paleoseismology. Journal of Structural Geology 69 163–78. https://doi.org/10.1016/j.jsg.2014.10.007.
Kirsch, M., Lorenz, S., Zimmermann, R., Andreani, L., Tusa, L., Pospiech, S., Jackisch, R., et al. (2019): Hyperspectral Outcrop Models for Palaeoseismic Studies. The Photogrammetric Record 34, no. 168 385–407. https://doi.org/10.1111/phor.12300.
Lorenz, S., Salehi, S., Kirsch, M., Zimmermann, R., Unger, G., Sørensen, E.V., & Gloaguen, R. (2018): Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops. Remote Sensing 10, no. 2:176. https://doi.org/10.3390/rs10020176.
Thiele, S.T., Lorenz, S., Kirsch, M., Acosta, I.C.C., Tusa, L., Hermann, E., Möckel, R., & Gloaguen, R. (2021): Multi-Scale, Multi-Sensor Data Integration for Automated 3-D Geological Mapping Using Hylite. Ore Geology Reviews 136. https://doi.org/10.1016/j.oregeorev.2021.104252.
Thiele, S.T., Bnoulkacem, Z., Lorenz, S., Bordenave, A., Menegoni, N., Madriz, Y., Dujoncquoy, E., Gloaguen, R., & Kenter, J. (2022): Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs. Remote Sensing 14, no. 1 https://doi.org/10.3390/rs14010005.

Uncrewed aerial vehicles (UAVs) have rapidly become integrated into the mining lifecycle, with applications in exploration, production and post-mining management. Although mostly used for photogrammetric surveying, a variety of additional sensors are increasingly being deployed. Of particular relevance to mineral exploration, these include geophysical instruments (e.g., magnetometers, radiometers) and imaging spectrometers (e.g., multi- and hyperspectral cameras), that can be deployed to rapidly and accurately map structure, lithology and alteration. Hyperspectral sensors are especially sensitive to subtle mineralogical changes that can guide exploration and mining operations, albeit in well exposed areas (e.g., cliffs, open-pit workings, mountains or coastal outcrops). In this contribution, we present an overview of our current workflow for collecting and correcting UAV hyperspectral data for geological applications, and outline some of the important caveats and challenges when deriving geometrically and spectrally corrected data in topographically complex environments. We emphasise the importance of three dimensional topographic data, collected using photogrammetric techniques, and highlight the potential of combined digital outcrop and hyperspectral remote sensing workflows. An open-source implementation of this workflow (hylite) is introduced, and current challenges identified. Specifically, we highlight the need for rapid, robust and easy to use tools for processing data in the field, to facilitate QAQC and optimised survey planning and targeting. Finally, we present several case studies that apply hyperspectral UAV data to advance exploration for primary and secondary raw materials.

Cliffs present some of the most spectacular geological exposures, and can provide detailed and spatially continuous geological data for research and industry applications. However, challenging access has until recently limited our ability to get full value from these valuable outcrops. The surge in uncrewed aerial vehicle (UAV) technology has palliated some of these limitations, allowing for rapid and unprecedentedly detailed (sub-cm resolution) surveying with visible-near (VNIR) and shortwave (SWIR) infrared hyperspectral sensors. UAV-based SWIR-sensors typically use a pushbroom acquisition mode that results in significant distortions due to UAV movement. These must be corrected to derive geometrically accurate results. In this contribution we present an open-source workflow for (1) the geometric correction and back-projection of pushbroom hyperspectral data to derive dense 3-D hyperclouds; (2) removal of illumination effects to derive estimates of reflectance spectra and (3) the application of various hyperspectral mapping techniques to extract lithological and mineralogical information. This workflow is implemented in the open-source python package hylite to facilitate and encourage future research and open access science by researchers and industry.
Our approach is different to the correction workflows implemented by camera vendors (e.g., PARGE) as it directly associates points in a photogrammetric point cloud with pixels in the hyperspectral pushbroom image. The resulting mapping matrix captures the “many to many” relationship between points and pixels. For pushbroom imagery a single point can be visible from several pixels, and each pixel will contain multiple points, and that facilitates the transfer and fusion of hyperspectral data onto the geometrically accurate point cloud. This true-3D approach is essential in areas of complex relief, such as cliffs or open-pit mines, as these geometries cannot be projected onto a 2-D image plane (orthomosaic) without significant distortion and geometric errors.
Additional advantages of this approach are: (1) high resolution panchromatic data from the photogrammetric point cloud can be used to automatically correct for sensor boresight, and (2) the topographic information captured by the point cloud provides the geometric information (e.g., surface orientation and skyview factor) required to correct for illumination effects and derive reflectance spectra. The resulting reflectance hypercloud can then be analysed using a variety of methods implemented in hylite (e.g., minimum wavelength mapping, band ratio calculation or spectral unmixing) to create objective and reproducible maps of lithology or mineralogy.

Dykes are one of the most widespread mechanisms of magma transport in the brittle crust [1]. Some reach the surface to cause eruptions, but many also propagate laterally over large distances without breaching the surface. Among the most striking and widespread examples of these are giant continental dyke swarms, thought to originate from mantle-plume driven large igneous provinces [2–4]. Individual swarms contain hundreds to thousands of individual dykes that apparently grew laterally from a common source to attain lengths on the order of hundreds to thousands of kilometers. More than 100 of these dyke swarms are known on Earth and when combined, occur more than 300 times on Earth, Venus, and Mars [5]. Structure and geochemistry have been extensively studied for both giant dyke swarms (eg [4–12]) and smaller-scale swarms associated with local magma chambers and volcanic centers (eg [9, 13–17]).

Diagenetic modification of carbonate depositional systems is a dominant process changing their pore systems away from primary texture and responsible for their challenging multi-modal and multi-scale behavior. It is these pore system characteristics that control dynamic behavior across many scales from plug – to log – to reservoir scale. One common diagenetic product in many Middle East reservoirs is dolomite and is invoked to be associated with improved storage and excess permeability. Despite these observations, reliable spatial models of dolomite distribution are rare, especially at field or seismic scale. This paper documents how the dolomite distribution across an outcrop in Morocco was captured and validated using high resolution 3D photogrammetry combined with hyperspectral acquisition. It suggested that these, “remote” attributes can be combined and not only provide spatial rules but also point to scenarios for reconstruction of timing and process of dolomitization.

The Black Angel Zn-Pb ore deposit is hosted in folded Paleoproterozoic marbles of the Mârmorilik Formation. It is exposed in the southern part of the steep and inaccessible alpine terrain of the Rinkian Orogen, in central West Greenland. Drill-core data integrated with 3D-photogeology and hyperspectral imagery of the rock face allow us to identify stratigraphic units and extract structural information that contains the geological setting of this important deposit. The integrated stratigraphy distinguishes chemical/mineralogical contrast within lithologies dominated by minerals that are difficult to distinguish with the naked eye, with a similar color of dolomitic and scapolite-rich marbles and calcitic, graphite-rich marbles. These results strengthen our understanding of the deformation style in the marbles and allow a subdivision between evaporite-carbonate platform facies and carbonate slope facies. Ore formation appears to have been mainly controlled by stratigraphy, with mineralizing fluids accumulating within permeable carbonate platform facies underneath carbonate slope facies and shales as cap rock. Later, folding and shearing were responsible for the remobilization and improvement of ore grades along the axial planes of shear folds. The contact between dolomitic scapolite-rich and calcitic graphite-rich marbles probably represents a direct stratigraphic marker, recognizable in the drill-cores, to be addressed for further 3D-modeling and exploration in this area.

Magma transport through the Earth’s crust is commonly described to occur through interconnected planar sheet intrusions such as dykes and sills, which form so called magma plumbing systems. Elongate intrusion geometries (i.e., magma fingers and segments), hereafter referred to as elements, may form during magma transport due to viscous and/or elastic instabilities at the propagating intrusion tip, and they are often observed at the outer margin of solidified sheet intrusions. Field observations, geophysical datasets, and analogue models further show that when elements grow in width, they can coalesce, indicating that planar sheet intrusions can form and grow by the amalgamation of individual elements. Previous studies suggest that the emplacement and growth of elements is accommodated by one dominating emplacement end-member process, namely: i) tensile-elastic fracturing, ii) shear failure, or iii) viscous deformation (e.g., host rock fluidisation). However, the interplay between individual end-member processes remains poorly understood. Here we present field observations of elongate magma fingers located at the SE margin of the Paleogene Shonkin Sag laccolith (Montana, USA) to assess how host rocks (Cretaceous Eagle Sandstone) deform to make space for the magma. We combine drone photogrammetry surveys with field mapping and microstructural analyses to describe and quantify host rock deformation in the vicinity of 37 magma fingers, and we conduct thermal modelling to further evaluate the conditions at which viscous deformation due to host rock fluidisation is feasible.

Our field observations show that all three proposed end-member processes accommodated the emplacement of magma fingers at the SE margin of the Shonkin Sag laccolith. Brittle deformation, shear failure, and folding of host rock mainly occurs in the compressional regime between two adjacent magma fingers, whereas host rock fluidisation and mobilisation is predominantly observed at the cross-sectional, lateral finger tips. Our photogrammetric analyses show that up to 40 % of the finger thickness is accommodated by elastic host rock uplift. Critically, this range of host rock deformation mechanisms is observed in one outcrop at metre scale, and in some cases associated with an individual magma finger. Thermal modelling of temperatures ahead of a propagating intrusion tip indicates that intrusion induced host rock fluidisation is only possible at low tip velocities of ≤ 10-5 m/s, which can vary depending on the emplacement depth, magma temperature, and the thermal diffusivity of the host rock.

Overall, we conclude that the emplacement of magma fingers at the outer margin of the Shonkin Sag laccolith was accommodated by a combination of elastic host rock uplift and both brittle and ductile host rock deformation. Based on our field observations and thermal modelling results, we suggest that intrusion tip velocities and the resulting strain rate are key parameters that control the dominating space-making mechanisms during magma emplacement. Due to the elongate geometry of elements and the resulting different strain rates at their lateral and frontal tips, we further propose that deformation mechanisms observed at lateral tips in cross sectional outcrops are likely decoupled from those at frontal tips such that they may not be equivalent.

Laser plasma-based proton accelerators (LPA) can contribute to research of ultra-high dose rate radiobiology as they provide pulse dose rates unprecedented at medical proton sources. Yet, LPAs pose challenges regarding precise dosimetry due to the high pulse dose rates, but also due to the sources' lower spectral stability and pulsed operation mode. For in-vivo models, further challenges arise from the necessary small field dosimetry for volumetric dose distributions.
In this work, we present a dosimetry and beam monitoring concept for in-vivo irradiations of small target volumes with LPA protons, solving aforementioned challenges. The volumetric dose distribution in a sample (mean dose value and lateral/depth dose inhomogeneity) is provided by combining two independent dose measurements using radiochromic films (dose-rate independent) and ionization chambers (dose-rate dependent), respectively. The unique feature of the dosimetric setup is beam monitoring with a transmission time-of-flight spectrometer to quantify spectral fluctuations of the irradiating proton pulses. The resulting changes in the depth dose profile during irradiation of an in-vivo sample are hence accessible and enable pulse-resolved depth dose correction for each dose measurement.
A first successful small animal pilot study using an LPA proton source serves as a testcase for the presented dosimetry approach and proves its performance in a realistic setting.

The uptake of niobium by hardened cement paste (HCP) a calcium silicate hydrate (C-S-H) phases was investigated with ⁹³Nb and ⁹⁵Nb (t₁_/₂=35.0 days). Structural materials used in nuclear reactors as well as cements contain the naturally occurring isotope ⁹³Nb, while radioactive ⁹⁴Nb with t₁_/₂=2×10⁴ years is relevant in the context of nuclear waste disposal. Strong uptake of Nb was observed for both materials, confirming that C-S-H are the main sink of Nb in cement. Isotopic exchange with ⁹³Nb in cement can play a role in the uptake of ⁹⁴Nb under repository conditions. The formation of complexes with isosaccharinic acid (ISA) decreases the Nb uptake, although sorption remains strong up to [ISA]tot=0.1 M. Chloride has a negligible effect on the uptake of Nb up to [NaCl] = 2 M. This work provides a sound basis for the quantitative description and mechanistic understanding of ⁹⁴Nb retention in L/ILW repositories.

With advancement of technology and global shift toward clean energy, the need for rare-earth metalsis increasing. Scandium, a rare earth metal, has been extensively used over decades in solid oxide fuel cells and aluminum-scandium alloys that have vast evolving market in aerospace, automobiles and 3D printing. However, the market struggles to maintain the supply chain due to expensive recovery processes and absence of uniform global distribution of primary sources. Therefore, identification of alternative sources and technological advancement for scandium recovery is needed. To this context, an effort has been made to provide a list of the advances in different technologies applied in scandium recovery from diverse sources. Emphasis has been given on the improvements and up-gradation of technologies in terms of environmental impact and percentage recovery. An attempt has been made to discuss and deliver a clear representation of the challenges associated with every source for scandium recovery and major developments done in them. The environmental impact of scandium recovery and recycling has also been discussed.

Artificial intelligence (AI) and machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. AI and ML are being used to not only predict the structures of the proteins but to edit the protein sequence to give them desired properties and enhance their functions. Thus, there is a need to study how these proteins are interacting with other components in the experimental setup or the human body. With the increasing interest in the above-mentioned research gaps, scientists are working on several wet-lab techniques and adding to the knowledge pool. However, this information is scattered and enormous. Hence, AI and ML come to the rescue. It can handle bulk data and organize and produce models that can make sense of the information. Therefore, the involvement of AI and ML is inevitable, and this review highlights these points.

Cadmium chalcogenide nanoplatelets (NPLs) are established as promising materials for a wide variety of optoelectronic applications due to their properties surpassing in many aspects their counterpart nanocrystals (NCs) with other shapes. Most of these features arise from strong quantum confinement in the direction of thickness which can be tuned with precision down to one monolayer. However, atomic smoothness of their basal planes and hence the ability to change the NPL thickness only in discrete steps prevent precise tuning of absorption and photoluminescence spectra unlike in the case of quantum dots. Preparation of alloyed NCs provides a potential solution to this problem, but it is complicated by the different reactivities of chalcogenide sources, which becomes even more restrictive in the case of NPLs because they are more sensitive to alterations of reaction conditions. In this work, we overcome this obstacle by employing highly reactive stearoyl sulfide and selenide as chalcogen sources, which enable straightforward variation of the NPL composition and thickness by changing the ratio of chalcogen precursors and reaction temperature, respectively. Alloyed CdSe_xS_1−x NPLs
obtained exhibit tunable absorption and photoluminescence bands covering the blue-green region from 380 to 520 nm with bright band-edge emission and quantum yields of ∼30−50% due to their relatively small lateral size enabled by a much finer control of the lateral growth.

Machine Learning and Artificial intelligence are quickly becoming impending game changers for bioprocessing development. However, its true potential has not been harnessed, and real-time application is still in its interim stage to control most cognitive tasks. Hence, it is imperative to know the state of technology to identify the gaps in the knowledge. In this review, we first give an insight into the basic understanding of the machine learning domain and discuss its complexities for more comprehensive applications. Subsequently, we outline how relevant machine learning models are used to statistically and logically analyze the big datasets generated in the bioprocessing industries to control process operations. While doing so, we provide the state of technology applied in different subfields of the bioprocessing industry. Further, this review also discusses the adoption of hybrid modeling strategies for combining mechanistic models with historical data-driven machine learning models to develop new digital biotechnologies.

Fibroblast activation protein (FAP), mainly expressed by cancer-associated fibroblasts (CAFs) in the tumour stroma, promotes tumour growth, metastasis, and immunosuppression and, therefore, has been studied as a target for cancer diagnosis and treatment. With regard to immunotherapy, the innovative modular universal CAR (UniCAR) platform developed by our group is one of the most promising approach due to the reduced risk for e.g. on-target/off-tumour toxicities and cytokine release syndrome. Thereby, chimeric antigen receptor (CAR) T-cells (UniCAR T cells) are exclusively activated in the presence of a target module (TM) that specifically establishes the crosslinking between target cells and UniCAR T-cells. FAP specific TMs are hypothesized to be not only immunotherapeutics with increased safety but in addition to be suitable as radionuclide-based theranostic agents.
For that, low molecular weight TMs that are rapidly eliminated allowing a specific and recurrent on/off switch of UniCAR T-cell activity via TM dosing were developed by fusion of the single-chain variable fragment (scFv) of an anti-human FAP mAb to the peptide epitope E5B9 that is recognized by the UniCAR T-cells. To ease the clinical TM administration at later stages of tumour therapy and for targeted radionuclide therapy, however, TMs with extended half-life may be advantageous. Therefore, anti-FAP TMs based on the human IgG4 Fc-domain, including a mutated version, were created. All TMs were tested (i) in vitro based on naturally and artificially overexpressing 2D and 3D models and (ii) in vivo by positron emission tomography (PET) and single-photon emission tomography (SPECT) in NMRI nude mice bearing both mock transfected and FAP overexpressing HT1080 tumor xenografts.
In vitro, all TMs were proven to specifically redirect UniCAR T-cells to FAP-expressing target cells. Moreover, FAP specific TMs could be conjugated to different chelators, e.g. Bispidines, NODAGA, and CHX-A-DTPA and, afterwards, radiolabelled with either Copper-64 or Lutetium-177. PET imaging with 64Cu radiolabelled anti-FAP IgG4 TMs revealed an excellent FAP specific tracer enrichment at the tumour site already 6h p.i. After 24 to 48h p.i. tumor SUVmean increased up to 20 with almost no background. SPECT imaging with 177Lu radiolabelled anti-FAP IgG4 TMs confirmed the high FAP-dependent tumour uptake and, thereby, offers possibility for targeted radionuclide therapy.
In conclusion, we designed novel FAP specific TMs with different molecular weight that can be used for immunotherapeutic approaches using UniCAR T-cells, diagnostic imaging, and targeted radionuclide therapy and, thereby, have the potential to improve cancer treatment allowing an individualized treatment of cancer patients with increased clinical safety.

Metallic Mn-based alloys with a nearest-neighbor Mn-Mn distance greater than 0.4 nm exhibit large, welllocalized magnetic moments. Here we investigate the magnetism of tetragonal Au₄Mn with a Curie temperature of 385 K, where manganese has a spin moment of 4.1 μ_B and its orbital moment is quenched. Since 80% of the atoms are gold, the spin-orbit interaction is strong and Au₄Mn exhibits uniaxial magnetocrystalline anisotropy with surface maze domains at room temperature. The magnetic hardness parameter of 1.0 is sufficient to maintain the magnetization along the c axis for a sample of any shape. Au also reduces the spin moment of Mn through 5d-3d orbital hybridization. An induced moment of 0.05 μ_B was found on Au under a pulsed field of 40 T. Density functional theory calculations indicate that the Mn-Mn exchange is mediated by spin-polarized gold 5d and 6p electrons. The distance dependence shows that it is ferromagnetic or zero for the first ten shells of Mn neighbors out to 1.041 nm (64 atoms), and very weak and oscillatory thereafter.

Secondary metabolites such as siderophores produced by microorganisms and plants bind not only Fe3+ but also commercially important elements such as Ga3+, Ge4+ and Ti4+. As numerous complexation studies suggest, siderophores are potential candidates for sustainable and environmentally friendly metal recovery technologies. However, the native and heterologous production of these siderophores is limited for many reasons, including iron inhibition, highly regulated production, the tendency to recycle siderophores and the simultaneous production of different siderophores. The most studied siderophore for metal recovery is desferrioxamine B, but since it is only produced chemically, its industrial application is limited. To solve this problem, we decided to use biological production in a natural host. In this project, we conducted experiments to optimise the media for the production of desferrioxamine B in a native host, Streptomyces pilosus, using minimal media and complex media. Streptomyces pilosus grows filamentous and therefore forms clusters mainly in minimal media, which poses a challenge for effective media optimisation. Initially, efforts were made to achieve homogeneous growth of Streptomyces pilosus, especially in minimal media without iron for desferrioxamine B production. When the growth of S. pilosus was switched from cluster to homogeneous growth, a phenotypic switch in siderophore production was observed. These studies will help to understand desferrioxamine B production in its native host. Furthermore, we look forward to optimize production of desferrioxamine in native host-Streptomyces pilosus.

Imidazolium salts were prepared which possess 2-ethoxyethyl pivalate or 2-(2-ethoxyethoxy)ethyl pivalate groups as amphiphilic side chains with oxygen donors as well as n-butyl substituents as hydrophobic groups. The N-heterocyclic carbenes of the salts, characterized by 7Li and 13C NMR spectroscopy as well as by Rh and Ir complex formation, were used as starting materials for the preparation of the corresponding imidazole-2-thiones and imidazole-2-selenones. Flotation experiments in Hallimond tubes under variation of the air flow, pH, concentration and flotation time were performed. The title compounds proved to be suitable collectors for the flotation of lithium aluminate and spodumene for lithium recovery. Recovery rates up to 88.9% were obtained when the imidazole-2-thione was used as collector.

Mineral dissolution is a dynamic process that involves reacting a surface with a fluid. Therefore, the kinetics of dissolution depends not only on the solution and the environment (concentration and temperature) but also on the mineral properties (reactive surface area, orientation and geometry). Several studies show that the dissolution rate is not a constant value but a spectra that depends on the reactivity of the different types of surface features. However, available experimental evidence comes either from observations from single surface (e.g. view from top) or from flow through in situ studies (where flow affects the observation). In this work the dissolution behavior of galena particles in deep eutectic solvent (DES) is shown using X-Ray Computed Tomography (CT). Two cases were evaluated: 1) only one surface of the particle was reactive and 2) the particle reacts from 5 different directions. The particle was leached in a stirred batch reactor to avoid the effect of directional flow. These are the first result showing the effect of large scale particle geometry in the dissolution rate spectra, which was used to developed a code to localize micron-scale evolution of the surface of a particle based on neighborhood of each point of the surface.

Precise measurements of the mineralogical composition and 3D geometry of particles in mineral samples unlock the ability to systematically optimize ore processing procedures and thus paves the way for more efficient industrial ore processing and recycling of complex composite materials such as electronic waste. X-ray Computed tomography (CT) is a widely used method to acquire 3D images of such samples but so far lacks standardized methods to enable their interpretation. Here we introduce a new workflow to standardize the measurement of the 3D geometrical and mineralogical properties of particles. Importantly, our method is able to correct biases arising from partial volume imaging artefacts.

Specifically, our method consists of a combination of a deep neural algorithm known as nnU-Net [1], a state-of-the-art ready to use framework for segmentation of particles in the CT images, and MSPaCMAn [2], an automated method to extract precise mineralogical and geometrical properties on the particle level. We demonstrate that our method can be used out of the box to produce the particle segmentations independent of user biasness. These segmented images are used to calculate the 3D spatial properties of the particles including the mineralogical composition, surface liberation and a comprehensive list of geometrical properties. Results are validated using reference samples of known compositions. The proposed workflow is the first to enable a precise, unbiased and standardized semi-automated 3D analysis of particles using CT. The more comprehensive and standardized characterization is critical for the use of 3D particle properties in advanced ore processing techniques. Moreover, these 3D properties can be applied in the field of sedimentology for example to study the sediment transport and deposition.

Mineralogical and 3D geometrical properties of particles affect their intrinsic separation behaviour. Artefacts from x-ray computed tomography (CT) images hinder the interpretation to determine the mineralogical and 3D geometrical information of the particles. Here we introduce a new workflow, a combination of a deep neural algorithm known as nnU-Net [1] and MSPaCMAn [2]. The workflow accounts for the partial volume artefacts in CT images. The 3D properties and the mineralogical composition of the particles are derived from the mask of the particles and individual particle histograms. The new workflow will unlock the ability to standardize and automate the mineral phase classification and quantification, determining liberation and calculation of 3D properties of the particles. This will pave the way to optimize the separation processes by finding the link between 3D properties, mineralogy and intrinsic separation properties at the particle level.

Five Helmholtz Centers are participating in the Research Field Energy, three of them are directly contributing to Hub Energy. To be well prepared for their supporting tasks in establishing a FAIR data ecosystem within the energy research community at Helmholtz, the team members of Hub Energy study relevant use cases and develop software tools in close cooperation with FAIR Data Commons. This poster presents four examples for this work: A photovoltaic system requires ontology development and data models based on standards like IEC 61850 or SensorML as well as on FAIR Digital Objects (FDO). In another use case, RO-Crates are automatically generated for data of the KIT Campus North energy and water consumption. The aim is to study methods for a detailed metadata desciption in data publication processes. In the field of software development, an FDO browser offers cascading search for metadata and application data entities and a metadata editor supports users in creating and editing schemas and instances as well. The presented activities foster close contact between Hub Energy and Helmholtz energy researchers and, thus, essentially support the formation of a FAIR energy data management. Use cases feed technical details into the Hub's energy knowledge pool and they are also a nearly perfect training programme for the Hub personnel. In doing the presented software development work, deep insights into energy data landscapes and an improved sense for user requirements are induced, even if in the end more elaborated and harmonized solutions from FAIR Data Commons may be adopted.

To study aerodynamic transport of aerosol particles within closed room dynamic situations, the Cottbus Aerosol particle Reference Experiment (CARE) was built and equipped, which includes thermal manikins and a spreader dummy. For various flow configurations (location of spreader, heating bodies, windows opened, air ventilation with and without air purification systems) flow visualisation is performed, particulate matter sensors (PMS) measure local particle concentrations, head mounted camera systems count particle concentrations of individuals and finally, large field of view Shake-The-Box Particle Tracking
delivers velocity fields. The comprehensive experimental configuration of different measurement systems are discussed in terms of their quantitative results, effective application and comparative efficiency explaining the flow dynamics. The findings from these experiments also provide information under which circumstances particularly high concentrations of aerosol particles can be found on which locations.

Connected-component analysis (CCA) is a central part of many image processing applications. To process image data at ever increasing image resolutions and frame rates, parallel CCA 2
algorithms are essential. Such algorithms targeting GPUs typically store the extracted features in arrays large enough to potentially hold the maximum possible number of objects for the given image size. Transferring these large arrays to the host requires large portions of the overall execution time. Therefore, we propose an algorithm which uses a CUDA kernel to merge trees of connected component feature structs. During the tree merging, various connected-component properties, such as total area, centroid and bounding box, are extracted and accumulated. The tree structure then enables us to only transfer features of valid objects to the host for further processing or storing. Our benchmarks show that this implementation drastically reduces memory transfer volume for processing results on the host whilst maintaining similar performance to state-of-the-art CCA algorithms.

Skyrmionic materials hold the potential for future information technologies, such as racetrack memories. Key to that advancement are systems that exhibit high tunability and scalability, with stored information being easy to read and write by means of all-electrical techniques. Topological magnetic excitations such as skyrmions and antiskyrmions, give rise to a characteristic topological Hall effect. However, the electrical detection of antiskyrmions, in both thin films and bulk samples has been challenging to date. Here, we apply magneto-optical microscopy combined with electrical transport to explore the antiskyrmion phase as it emerges in crystalline mesoscale structures of the Heusler magnet Mn_1.4PtSn. We reveal the Hall signature of antiskyrmions in line with our theoretical model, comprising anomalous and topological components. We examine its dependence on the vertical device thickness, field orientation, and temperature. Our atomistic simulations and experimental anisotropy studies demonstrate the link between antiskyrmions and a complex magnetism that consists of competing ferromagnetic, antiferromagnetic, and chiral exchange interactions, not captured by micromagnetic simulations.

The separation of aerosol particles by a moving gas-liquid fluidic interface is central to a wide variety of industrial and natural applications, among which stand out air purification systems and precipitation scavenging. The particle size significantly affects the separation rate. The diffusion of particles in the nanometer range is largely dominated by molecular diffusion. In this regime, predictive models accurately estimate the separation rates. Model inaccuracy increases, however, significantly when the particle size ranges from 0.1 μm to 2.5 μm. In this impaction-dominated regime, the complex interplay between the flow dynamics on both sides of the fluidic interface and the particle inertia makes it difficult to develop suitable models.
In this work, the preliminary work on the the bubble shape and the numerical simulation is presented, besides indication of relevant investigations in the particle separation on rising bubbles.

We discuss the Heisenberg-Wigner phase-space formalism in quantum electrodynamics as well as scalar quantum electrodynamics with respect to transverse fields. In regard to the special characteristics of such field types we derive modified transport equations such that particle momenta perpendicular to the propagation direction of the waves show up as external parameters only. In case of spatially oscillating fields we further demonstrate how to transform momentum derivative operators of infinite order into simple coupling terms.

Ferromagnetic resonance (FMR) and the measurement of magnetization dynamics in general have become sophisticated tools for the study of magnetic systems at the nanoscale. Nanosystems, such as the nanodots of this study, are technologically important structures, which find applications in a number of devices, such as magnetic storage and spintronic systems. In this work, we describe the detailed investigation of cobalt nanodots with a \SI{200}{\nm} diameter arranged in a square pitch array with a periodicity of \SI{400}{\nm}. Due to their size, such structures can support standing spin-wave modes, which can have complex spectral responses. To interpret the experimentally measured broadband FMR, we are comparing the spectra of the nanoarray structure with the unpatterned film of identical thickness. This allows us to obtain the general magnetic properties of the system, such as the magnetization, $g$-factor and magnetic anisotropy. We then use state-of-the-art simulations of the dynamic response to identify the nature of the excitation modes. This allows us to assess the boundary conditions for the system. We then proceed to calculate the spectral response of our system, for which we obtained good agreement. Indeed, our procedure provides a high degree of confidence, since we have interpreted all the experimental data to a good degree of accuracy. In presenting this work, we provide a full description of the theoretical framework and its application to our system, and we also describe in detail the novel simulation method used.

Fully characterizing high energy density (HED) phenomena using pulsed power facilities (Z machine) and coherent light sources is possible only with complementary numerical modeling for design, diagnostic development, and data interpretation. The exercise of creating numerical tests, that match experimental conditions, builds critical insight that is crucial for the development of a strong fundamental understanding of the physics behind HED phenomena and for the design of next generation pulsed power facilities. The persistence of electron correlation in HED materials arising from Coulomb interactions and the Pauli exclusion principle is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. An exemplar is a ferromagnetic material like iron, while familiar and widely used, we lack a simulation capability to characterize the interplay of structure and magnetic effects that govern material strength, kinetics of phase transitions and other transport properties. Herein we construct and demonstrate the Molecular-Spin Dynamics (MSD) simulation capability for iron from ambient to earth core conditions, all software advances are open source and presently available for broad usage. These methods are multi-scale in nature, direct comparisons between high fidelity density functional theory (DFT) and linear-scaling MSD simulations is done throughout this work, with advancements made to MSD allowing for electronic structure changes being reflected in classical dynamics. Main takeaways for the project include insight into the role of magnetic spins on mechanical properties and thermal conductivity, development of accurate interatomic potentials paired with spin Hamiltonians, and characterization of the high pressure melt boundary that is of critical importance to planetary modeling efforts.

Accurate modeling and simulation of radionuclide migration in clay rocks such as the Opalinus Clay play a key role in the safety assessment of deep geological repositories for nuclear wastes. At the continuum scale, the representative elementary volume (REV) is a fundamental constraint to quantify the effective diffusivity, which is a key parameter in reactive transport (RT) models. Therefore, an accurate estimation of the REV is essential for a meaningful continuum-scale RT simulation in heterogeneous clay rocks. This study presents a comprehensive analysis of the heterogeneities of porosity and effective diffusivity in clay rocks by using the classical sampling theory and pore-scale simulations. First, in this study, the two-dimensional representative elementary area (REA) is correlated with the REV for porosity via a characteristic length. Next, it is shown that the REV for diffusivity is larger than the REV for porosity. Moreover, these two REVs can be correlated using Archie’s law. In such a way, the REV for diffusivity can be determined by the developed correlations through analyzing two-dimensional microstructures, thus significantly reducing the computational cost. Finally, the applicability of our approach for clay rocks is validated by experimental data on the diffusion of tritiated water in the heterogeneous sandy facies of Opalinus Clay. From both the experimental data and the modeling prediction, the REV for diffusivity in the sandy facies of Opalinus Clay is in the order of cubic centimeters. This study provides critical insights into the diffusion in heterogeneous clay rocks towards an enhanced predictability of radionuclide migration.

According to the most recent listing reported by the European Commission, rare-earth elements (REEs) are the critical raw materials with the highest supply risk, whereas their recycling rates remain very low in the European Union [1]. End-of-life fluorescent lamps are a promising secondary source of REEs, but their recycling requires innovative separation processes [2,3]. By using phage surface display, Lederer and co-workers identified selectively surface-binding peptides that specifically bind to fluorescent lamp phosphors [4]. In a following study, Schrader et al. immobilized these peptides on coated well plates to investigate their binding to various REE phosphors [5]. The immobilization was facilitated by an activation with benzotriazole-1-yl-oxytripyrrolidinophosphonium-hexafluorophosphate (PyBOP) in the aprotic solvent N-Methyl-2-pyrrolidone (NMP) in the presence of the sterically hindered base diisopropylethylamine (DiPEA), a coupling reaction commonly used for chemical peptide synthesis. Recently, we investigated the immobilization method presented by Schrader et al. for the functionalization of Dynabeads [6]. Dynabeads are highly spherical and monodisperse composite magnetic beads, consisting of superparamagnetic iron oxide nanoparticles dispersed in a polystyrene matrix. They are commercially available with various surface coatings. The functionalization of amine coated Dynabeads with phosphor binding peptides, immobilized with the coupling reaction described above, did not change the Dynabeads’ zeta potential and had no significant effect on the interaction with REE phosphors [6]. On the other hand, we found that the immobilization onto carboxylic acid coated Dynabeads changed the Dynabeads’ zeta potential and isoelectric point.
We also observed that this immobilization had a detrimental effect on the interaction of the beads with the targeted phosphor particles and suggested that this may be an indication of polymerization of the peptides on the Dynabeads’ surfaces. In this work, we present a quantitative analysis of the total bound nitrogen (TNb) for the quantification of the immobilized peptides on the Dynabeads.

References
1. European Commission, Study on the EU’s list of Critical Raw Materials - Final Report (2020).
2. Patil, A.B., Paetzel, V., Struis, R.P.W.J., Ludwig, C. Separations 9 (2022), https://doi.org/10.3390/separations9030056
3. Binnemans, K., Jones, P. Journal of Rare Earths 32, 195-200 (2014), https://doi.org/10.1016/S1002-0721(14)60051-X
4. Lederer, F., Curtis, S., Bachmann, S., Dunbar, S., MacGillivray, R. Biotechnol. Bioeng. 114, (2016), https://doi.org/10.1002/bit.26240
5. Schrader, M., Bobeth, C., Lederer, F. ACS Omega XXXX, (2021), https://doi.org/10.1021/acsomega.1c04343
6. Boelens, P., Bobeth, C., Hinman, N., Weiss, S., Zhou, S., Vogel, M., Drobot, B., Azzam, S.S.A., Pollmann, K., Lederer, F. J. Magn. Magn. Mater. 169956 (2022), https://doi.org/10.1016/j.jmmm.2022.169956

Silicon carbide (SiC) is a suitable candidate for nanoelectromechanical systems due to its superior mechanical properties. It is also an interesting material platform to study the coupling of mechanical modes with localized spins associated with irradiation-induced defects. Such a spin-mechanical system can be used for quantum sensing applications [1].
The nanomechanical resonators in 3C-SiC are fabricated by standard semiconductor processing techniques such as electron beam lithography and reactive ion etching. They are characterized using Fabry-Pérot interferometer. In the preliminary experiments, we focus on the material modification by helium ion broad beam implantation on strained 3C-SiC resonators. The effect of varying fluence on resonance frequencies and quality factors is studied (see contribution of Philipp Bredol).
[1] A. V. Poshakinskiy and G. V. Astakhov, "Optically detected spin-mechanical resonance in silicon carbide membranes”, PhysRevB.100.094104 (2019)

Urinary tract infections (UTI) belong to the most common clinically relevant bacterial infections. 1 in 3 women worldwide will have at least one UTI by 24 years of age and 40 - 50% of women will experience one UTI during their lifetime with 44% experiencing recurrences. In this project, using a clinical dataset of brightfield microscopy of patients’ urine with few annotated samples,
we aim to develop a diagnostic phenotype quantification workflow using label-efficient machine learning (ML) approaches. There are several challenges to the clinical dataset at hand. Firstly, in the absence of specific labeling for phenotype-relevant objects in the micrographs ground truth is ambiguous. Secondly, obtaining manual annotations is laborious and requires highly-skilled annotators. Thirdly, the variation in scale and shape of a particular type of phenotype-relevant object is challenging for instance segmentation.

Distillation is the most frequently used thermal separation process and accounts for a remarkable share of the global energy consumption. Therefore, design of distillation columns needs to target highly efficient operation. The present contribution describes current achievements of modeling and predicting the complex two-phase flow in tray columns, which strongly affects the heat and mass transfer processes and thus the separation efficiency. The strategy for creating a digital twin is based on combining a morphology adaptive multifield two-fluid model with reasonable abstraction of the vapor injection through fixed and push valves. Validation is carried out against experimental data of threedimensional phase distribution around single valves. In a second stage a pre-processing tool was developed to automatically set up simulation cases for industrial-scale applications. The simulations will be compared against phase fraction and velocity measurements of a conductivity sensor array that was applied in a large-scale column mockup.

Several ore materials were characterized by 2D mineral liberation analysis (MLA), by X-ray computed tomography (CT) and by other standard bulk techniques like X-ray diffraction (XRD) and laser scattering. A comparison of the different properties show that particle properties measured with CT were often different from the other techniques. This is interpreted not as a failure of CT but as a natural consequence of it’s strengths and limitations relative to the other techniques. This raises important questions, how can CT be validated relative to standardized techniques in order to be more broadly applied for particle characterization? Which 3D properties from CT can we trust? And how can those 3D properties be used to new discoveries in process mineralogy? These questions will be answered in light of specific case studies analysed using a new automated and standardized workflow for 3D particle analysis.

This article proposes a blind nonlinear unmixing technique for intimate mixtures using the Hapke model and convolutional neural networks (HapkeCNN). We use the Hapke model and a fully convolutional encoder–decoder deep network for the nonlinear unmixing. Additionally, we propose a novel loss function that includes three terms; 1) a quadratic term based on the Hapke model, that captures the nonlinearity; 2) the reconstruction error of the reflectances, to ensure the fidelity of the reconstructed reflectance; and 3) a minimum volume total variation (TV) term that exploits the geometrical information to estimate the endmembers in the absence of pure pixels in the hyperspectral data. The proposed method is evaluated using two simulated and two real datasets. We compare the results of endmember and abundance estimation with a number of nonlinear, and projection-based linear unmixing techniques. The experimental results confirm that HapkeCNN considerably outperforms the state-of-the-art nonlinear approaches. The proposed method was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available at: https://github.com/BehnoodRasti/HapkeCNN .

Multiphase flows are to be found in many production processes in the process industry. Examples are bubble column reactors, distillation columns, fluidized beds and many more. Measuring process parameters in such systems is very difficult because most sensors are disturbed in their fundamental measuring principles by the presence of particles and interfaces. Especially in fundamental fluid mechanics science, there is a growing need for imaging techniques for studying multiphase flows. There, the derivation of so-called CFD-grade data from fluid dynamics experiments requires imaging techniques with high spatial resolution, non-intrusiveness and ability to deal with the opacity of multiphase mixtures, walls and inserts in process vessels and their mock-ups. The presentation will give an overview of the state of the art of selected tomographic imaging techniques and discuss their application in solving process engineering problems.

The gas flow modulation technique is a recently proposed approach for measuring the axial gas dispersion coefficient in bubble columns. This study presents a quantitative analysis of the experimental uncertainty associated with gamma-ray densitometry and ensemble-averaging of the data. The considered uncertainty sources are the statistics of the photon counting process, a mismatch between the modelled and the real radiation propagation due to the spatial extent of the detector, and a potential mismatch between modulation and sampling frequencies. The analysis is based on a numerical gamma-ray propagation model and a Monte Carlo approach to account for statistical uncertainty. The proposed algorithm supports the selection of an optimal total scanning time based on detector size, modulation parameters, involved fluids and column and source parameters. The analysis reveals that a mismatch between the modulation and sampling frequencies is most critical while the impact of the other considered uncertainty sources is rather marginal.

Mine waste is a large waste stream and typically contains significant amounts of metal(loid)s, which can pose environmental risks, especially when poorly managed. Reprocessing of mine waste can offer both economic and environmental benefits by contributing to the ever-growing global demands for valuable metals, as well as reducing the environmental risks associated with mine waste. Bioleaching is a global biotechnology that exploits the abilities of some microorganisms to catalyze the oxidative dissolution of sulfidic minerals, thereby expediting the extraction of metal(loid)s. Chemolithoautotrophic acidophilic microorganisms have been the focus of bioleaching studies for many decades and can effectively catalyze the solubilization of metals from ores or waste materials. However, bioleaching with acidophilic organisms is performed at low pH (pH ≤ 2), which could lead to the acidification of the environment. In addition, the tolerance of many acidophilic microorganisms to high chloride concentrations is limited, therefore freshwater is mainly used. There is a growing interest in the use of seawater for leaching purposes, especially in regions with less access to fresh water. Hence, this study investigated the bioleaching potentials of four halophilic (marine), moderately-halophilic sulfur-oxidizing bacteria: Thiomicrospira cyclica, Thiohalobacter thiocyanaticus, Thioclava electrotropha and Thioclava pacifica in shake flasks at room temperature. Results revealed T. electrotropha and T. pacifica as the most promising for bioleaching. In comparison to an acidophilic consortium which leached 95% Co, 0% Pb, 85% Zn, 80% As, 100% Cd, and 55% Mn from a sulfidic mine waste rock sample from the Neves Corvo mine Portugal, a pure cultures of T. electrotropha and T. pacifica solubilized 30-40% Co, and 10-20% Cu, Zn, K, Cd, Mn and Ag at a higher pH (pH ≥ 4) and high chloride concentration. Though still requiring process optimization, this new biotechnology seems promising and offers remarkable benefits such as preventing extreme acidification of the environment while also being applicable in seawater.

For many years, research on microbial-dissolution of metals from ores or waste materials mainly focused on the study of acidophilic organisms. However, most acidophilic bioleaching microorganisms have limited tolerance to high chloride concentrations, thereby requiring fresh water for bioleaching operations. There is a growing interest in the use of seawater for leaching purposes, especially in regions with less access to fresh water. Consequently, there is a need to find halophilic organisms with bioleaching potentials. This study investigated the bioleaching potentials of four moderately halophilic sulfur-oxidising bacteria: Thiomicrospira cyclica, Thiohalobacter thiocyanaticus, Thioclava electrotropha and Thioclava pacifica. Results revealed T. electrotropha and T. pacifica as the most promising for bioleaching. Pure cultures of the two Thioclava strains liberated about 30% Co, and between 8-17% Cu, Pb, Zn, K, Cd, and Mn from a mine waste rock sample from the Neves Corvo mine, Portugal. Microwave roasting of the waste rock at 400 and 500 °C improved the bioleaching efficiency of T. electrotropha for Pb (13.7 to 45.7%), Ag (5.3 to 36%) and In (0 to 27.4%). Mineralogical analysis of the bioleached residues using SEM/MLA-GXMAP showed no major difference in the mineral compositions before and after bioleaching by the Thioclava spp. Generally, the bioleaching rates of the Thioclava spp. are quite low compared to that of the conventional acidophilic bioleaching bacteria. Nevertheless, their ability to liberate potential pollutants (metal(loid)s) into solution from mine waste raises environmental concerns. This is due to their relevance in the biogeochemistry of mine waste dumps, as similar neutrophile halophilic sulfur-oxidising organisms (e.g. Halothiobacillus spp.) have been isolated from mine wastes. On the other hand, the use of competent halophilic microorganisms could be the future of bioleaching due to their high tolerance to Cl- ions and their potential to catalyse mineral dissolution in seawater media, instead of fresh water.

Recent advances in Machine Learning (ML) and Deep Learning (DL) are revolutionizing our abilities to analyze biomedical images and deepen our understanding of infection and disease. Among other host-pathogen interactions may be readily deciphered from microscopy data using convolutional neural networks (CNN). ML/DL algorithms may allow unambiguous scoring of virus-infected and uninfected cells in absence of specific labeling. Furthermore, accompanied by interpretability approaches, the ability of CNNs to learn representations, without explicit feature engineering, may allow uncovering yet unknown phenotypes in microscopy. One such example is our recent tandem segmentation-classification algorithm aimed to uncover morphological markers of Caenorhabditis elegans lifespan and motility. Taken together these novel approaches may facilitate novel discoveries in Infection and Disease Biology.

Coinciding with the global pandemic of SARS-CoV-2 and the resulting global public health crisis caused by COVID-19, artificial intelligence methods started playing an ever more important role in Infectious Medicine. On one hand this was a result of a continuous digital transformation of Infectious Medicine—a trend started decades ago. On the other hand, the pandemic catalyzed the adoption of artificial intelligence and other digital and quantitative techniques by Infectious Medicine. In this chapter we review recent works touching upon aspects of COVID-19 patient journey and how it interconnects with big data and artificial intelligence. These include early and clinical research, epidemiology and detection, diagnostics, clinical care and decision support, as well as long-term care and prevention. We cross-compare the published works and assess their maturity. Finally, we provide a conclusion on the state of artificial intelligence in the Infectious Medicine of COVID-19 and attempt a future perspective.

Particle-based process models offer a promising avenue towards greater predictability in geometallurgy, i.e., the ability to predict the outcomes of specific mineral processing routes from the mineralogical and microstructural ore characteristics. While the particle-based prediction of separation processes is already possible with acceptable levels of accuracy, the ability to predict the outcomes of comminution processes is currently limited to particle size distributions. Expanding comminution modelling tools to include particle microstructures would enable the full particle-based modelling of mineral processing flowsheets. As a step towards the inclusion of microstructure in comminution modelling, Laguerre tessellations are proposed to represent both the microstructure and the successive comminution steps. In contrast to the PARGEN library of simulated particles, our goal is to provide a low-parametric, dynamic, and efficient generator of parent and progeny material to inform forward and backward modelling efforts.

The idea is to follow a hierarchical decision structure in the simulation procedure. We first define an intensity field in 3D for the occurrence cell nuclei, which are then realised by a marked Poisson process. The first mark corresponds to realisations of a multinomial variable, and defines the mineral of each potential cell. Conditional on the mineral, the second mark follows a normal distribution, defining the weight of each cell, related to its size. A communition step is defined by a Voronoi mosaic, with a (t+1)-step exhibiting a higher intensity of its Poisson process than the previous t-step. To model preferential breakage, we inhibit some of the potential breakage surfaces with a probability depending on the weighted average hardness and the cleavage quality of the minerals that each surface cuts. Two consecutive comminution steps generate the corresponding parent and progeny particles, Each independently cut by a random plane to generate the equivalent of a 2D SEM-based automated mineralogy dataset.

Cross-validation and performance measures are standard components in the evaluation of a geostatistical model. These are well established in the univariate case, but measures for multivariate geostatistical modeling have not received as much attention. In the case of a single target variable, the univariate approaches remain valid, but in the fully multivariate case where a vector of variables needs to be estimated the evaluation needs to be based on all estimates simultaneously. An extension of cross-validation and associated performance measures to the fully multivariate case is presented and discussed for the case of regionalized compositions. The method is demonstrated by validating geostatistical models for two case studies: a sample drawn from a geochemical survey data set estimated with cokriging, and an application of direct sampling multiple point simulation.

Three-dimensional information is crucial to our understanding of biological phenomena. The vast majority of biological microscopy specimens are inherently three-dimensional. However, conventional light microscopy is largely geared towards 2D images, while 3D microscopy and image reconstruction remain feasible only with specialized equipment and techniques. Inspired by the working principles of one such technique - confocal microscopy, we propose a novel approach to 3D widefield microscopy reconstruction through semantic segmentation of in-focus and out-of-focus pixels. For this, we explore a number of rule-based algorithms commonly used for software-based autofocusing and apply them to a dataset of widefield focal stacks. We propose a computation scheme allowing the calculation of lateral focus score maps of the slices of each stack using these algorithms. Furthermore, we identify algorithms preferable for obtaining such maps. Finally, to ensure the practicality of our approach, we propose a surrogate model based on a deep neural network, capable of segmenting in-focus pixels from the out-of-focus background in a fast and reliable fashion. The deep-neural-network-based approach allows a major speedup for data processing making it usable for online data processing.

Understanding the 3D structure of biological entities is crucial for gaining mechanistic biomedical knowledge. A confocal light microscope is a well-established tool used to obtain 3D data from biological specimens. Yet, it comes with the drawbacks of high equipment prices and heavy human labor. In this project, we introduce a 3D focal stacking solution using deep neural networks (DNN). Instead of restoring 3D models from confocal microscopes, our model produces in-focus images by inputting widefield microscope images, which may be obtained with significantly simpler equipment. This enables the translation from widefield microscope images into the 3D model by segmenting the in-focus pixels, allowing the image of 3D biological specimens in vivo.

Two versions of a machine learning (ML1 & ML2) based modeling framework have been successfully used to provide operational forecasts of O3 at Kennewick, WA. This paper shows the ML system performance when applied to all available observation locations in the Pacific Northwest to predict O3 and PM2.5 concentrations. We used historical O3 and PM2.5 concentrations, Weather Research and Forecasting (WRF) meteorological forecast data (including temperature, surface pressure, relative humidity, wind speed, wind direction, and planetary boundary layer height) and time information (including hour, weekday, and month) to train the model. A 10-time, 10-fold cross-validation method was used to evaluate the model performance. Similar to our previous study, ML1 correctly captures more high-O3 events, but also generates more false alarms, and ML2 performs better overall (R2 = 0.79), especially for low-O3 events. Our ML modeling framework utilizes both ML1 and ML2 results to achieve the best forecast performance. Compared to the WRF-CMAQ based forecast (i.e., AIRPACT), our final ML forecasts reduce the normalized mean bias (NMB) from 7.6% to 2.6% when evaluating against the observed mixing ratios. Our ML-based forecasts also show clear improvements on Air Quality Index (AQI) forecasts; more accurate O3 AQI index predictions for each AQI index including high-O3 AQI events. For PM2.5, ML1 and ML2 demonstrate similar capabilities to predict high-PM2.5 events and ML2 keeps its accuracy for low-PM2.5 predictions, so ML2 is used to provide the final forecast values, instead of combining the two ML models that we are using for O3. During wildfire seasons (May to September) and cold, winter seasons (November to February) from 2017 to 2020, our ML model clearly performs better than AIRPACT. AIRPACT under-predicts the wildfire season PM2.5 concentrations in the PNW (NMB = -27%) and over-predicts at some sites in the cold season up to 200%, while ML2 has a lower NMB in both seasons (NMB = 7.9% in the wildfire season and 2.2% in the cold season) and correctly captures more high-PM2.5 events.

The size and shape of the primary dendrite tips determine the principal length scale of the microstructure evolving during solidification of alloys. In-situ X-ray measurements of the tip shape in metals have been unsuccessful so far due to insufficient spatial resolution or high image noise. To overcome these limitations, high-resolution synchrotron radiography and advanced image processing techniques are applied to a thin sample of a solidifying Ga-35wt.%In alloy. Quantitative in-situ measurements are performed of the growth of dendrite tips during the fast initial transient and the subsequent steady growth period, with tip velocities ranging over almost two orders of magnitude. The value of the dendrite tip shape selection parameter is found to be σ^*=0.0768, which suggests an interface energy anisotropy of ε_4=0.015 for the present Ga-In alloy. The non-axisymmetric dendrite tip shape amplitude coefficient is measured to be A_4≈0.004, which is in excellent agreement with the universal value previously established for dendrites.

Although uranium-cerium dioxides are frequently used as surrogate material for (U,Pu)O2-δ, there is currently no reliable data regarding the oxygen stoichiometry and the redox speciation of the cations in such samples. In order to fill this gap, this manuscript proposes a synchrotron study of highly homogeneous (U,Ce)O2±δ sintered samples prepared by wet-chemistry route. HERFD-XANES spectroscopy led to determine accurately the O/M ratios (with M = U + Ce). Under reducing atmosphere (PO2 ~ 610-29 atm), the oxides were found close to O/M = 2.00 while the O/M ratio varied with the sintering conditions under argon (PO2 ~ 210-6 atm). They globally appear to be hyper-stoichiometric (i.e. O/M > 2.00), the departure from the dioxide stoichiometry decreasing with both the cerium content in the sample, and the sintering temperature. Nevertheless, such deviation from the ideal O/M = 2.00 ratio was found to generate only moderate structural disorder from EXAFS data at the U-L3 edge. Indeed, all the samples retained the fluorite-type structure of the UO2 and CeO2 parent compounds. The determination of accurate lattice parameters thanks to SPXRD measurements led to complete the data already reported in the literature, and to propose a mathematic expression linking the unit cell parameter, the chemical composition and the deviation from the stoichiometry. Such relation can now be used as a first approximation to estimate the O/M stoichiometry of uranium-cerium mixed oxides on a large composition range.

The growing use of geographic information systems (GIS) and geographical analyses in different areas of the digital humanities highlights the need for geocoding, i.e. assigning geographic coordinates to records in a dataset. Such spatially-referenced datasets are a precondition for any spatial analysis and visualization. While GIS in general is a dynamically evolving branch of software development, there is a need for specialized applications which would assist researchers in geocoding datasets in history, archaeology, and the digital humanities. Therefore, we developed the “Historical Geocoding Assistant”, an open-source web tool that meets the specific needs of historical research and brings a solution to geocoding historical data in a convenient, fast, and reliable way.

The properties of rotating turbulence driven by precession are studied using direct numerical simulations and analysis of the underlying dynamical processes in Fourier space. The study is carried out in the local rotating coordinate frame, where precession gives rise to a background shear flow, which becomes linearly unstable and breaks down into turbulence. We observe that this precession-driven turbulence is in general characterized by coexisting two dimensional (2D) columnar vortices and three dimensional (3D) inertial waves, whose relative energies depend on the precession parameter Po. The vortices resemble the typical condensates of geostrophic turbulence, are aligned along the rotation axis (with zero wavenumber in this direction, kz = 0) and are fed by the 3D waves through nonlinear transfer of energy, while the waves (with kz ≠ 0) in turn are directly fed by the precessional instability of the background flow. The vortices themselves undergo inverse cascade of energy and exhibit anisotropy in Fourier space. For small Po < 0.1 and sufficiently high Reynolds numbers, the typical regime for most geo-and astrophysical applications, the flow exhibits strongly oscillatory (bursty) evolution due to the alternation of vortices and small-scale waves. On the other hand, at larger Po > 0.1 turbulence is quasi-steady with only mild fluctuations, the coexisting columnar vortices and waves in this state give rise to a split (simultaneous inverse and forward) cascade. Increasing the precession magnitude causes a reinforcement of waves relative to vortices with the energy spectra approaching the Kolmogorov scaling and, therefore, the precession mechanism counteracts the effects of the rotation.

The recomine-concept deals with a variable flowsheet that comprises three major modules: (I) processing of tailings, (II) processing of solid residues and (III) water recovery. The first module deals with the output of BHP’s processing route. A bulk sulfide flotation produces sulfide concentrates and a silicate residue. Two potential processes represent the concentrate’s subsequent treatment: (a) leaching and (b) roasting of the concentrate.
The major aim of treating the concentrate by leaching is to produce schwertmannite (SMT) as a product after precipitation. Alternatively, roasting the sulphide concentrate may result in other economic products: sulphuric acid and ferric sulphate. Silicate residues from module one will appear in a sequence of processing steps in module two. This module has a twofold aim: (1) separating ferro- and paramagnetic fractions and (2) dewatering the residues. Distinct steps will accomplish a separation of concentrated, iron-rich garnet. Novel membrane technologies will treat wastewater streams from modules one and two and represent module three. The major purpose of water treatment is to maximize the amount of clean water for nearby and downstream communities.
The flotation campaign applied an overall flotation time of 20 min. Tailings contain only 0.4 % of pyrite at a recovery of 6.3 %. Bioleaching experiments were able to turnover 75 % of the pyrite maximum. SMT precipitation was successful. Infrared spectroscopy and XRD proofed a pure and fine crystalline schwertmannite. Its’ ability to adsorb arsenates has been proven in according tests and schwertmannite from Antamina Tailings is potentially suitable for decontamination of mine water from arsenates, phosphates, and vanadates. Interest from chemical industry exist in designing schwertmannite as a pigment in colour chemistry (see section C in appendix). A crude product of garnet sand was separated magnetically. The recomine-team speculates that a potential annual production of crude garnet could provide the largest deposit for industrial garnet on the planet.

BHP with the support of Fundacion Chile, through its open innovation program EXPANDE, has launched “BHP Tailings Challenge”, an initiative that seeks to promote the development of innovative solutions for repurposing copper tailings. The BHP Tailings Challenge is a global competition aimed at identifying the most innovative companies, startups, consortia, research centers and universities to help transform fresh tailings and create innovative business models. The recomine-proposal, which was one of the 10 selected proposals out of 153, suggests a variable flowsheet that comprises three major modules: (I) processing of tailings, (II) processing of solid residues and (III) water recovery. The following paragraphs describe the modules individually but outline the material flow from one module to the next. The first module deals with the output of BHP’s processing route. The recomine-proposal starts with a bulk sulfide flotation that produces a sulfide concentrate and a silicate residue as an output. Two potential processes represent the concentrate’s subsequent treatment: (a) leaching and (b) roasting of the concentrate. The major aim of treating the concentrate by leaching is to produce schwertmannite as a product after precipitation. Alternatively, roasting the sulfide concentrate may result in other economic products: sulfuric acid, ferric sulfate and residual calcine. The exothermal roasting may furthermore provide heat emissions as an energy source for usage in BHP’s processing routine. The silicate residue from module one will appear in a sequence of processing steps in module two. This module has a twofold aim: (1) separating ferro- and paramagnetic fractions and (2) dewatering the residues. Several steps will accomplish a separation of concentrated, iron-rich garnet. Novel membrane technologies will treat wastewater streams from modules one and two and represent the key-technology in module three. The major purpose of water treatment is to maximize the amount of clean water for nearby and downstream communities.

The recomine alliance works on several concepts for remediation of mining waste. With 5 different fiel laboratories they work on remediation technologies for mining legacies. However, these technologies work as well in active mining and may improve current mining activities. The constant interaction of companies from the recomine alliance with leading research institutions allowed to upscale several technologies for remediating mining waste and gaining raw materials at the same time. The talk provides an overview of the recomine activities and their overarching fourfold strategy in significantly reducing the volume of mining waste by (1) analyzing, avoiding (2), re-mine, (3) and (4) transforming mining waste.

BHP with the support of Fundacion Chile, through its open innovation program EXPANDE, has launched “BHP Tailings Challenge”, an initiative that seeks to promote the development of innovative solutions for repurposing copper tailings. The BHP Tailings Challenge is a global competition aimed at identifying the most innovative companies, startups, consortia, research centers and universities to help transform fresh tailings and create innovative business models. The recomine-proposal, which was one of the 10 selected proposals out of 153, suggests a variable flowsheet that comprises three major modules: (I) processing of tailings, (II) processing of solid residues and (III) water recovery. The following paragraphs describe the modules individually but outline the material flow from one module to the next. The first module deals with the output of BHP’s processing route. The recomine-proposal starts with a bulk sulfide flotation that produces a sulfide concentrate and a silicate residue as an output. Two potential processes represent the concentrate’s subsequent treatment: (a) leaching and (b) roasting of the concentrate. The major aim of treating the concentrate by leaching is to produce schwertmannite as a product after precipitation. Alternatively, roasting the sulfide concentrate may result in other economic products: sulfuric acid, ferric sulfate and residual calcine. The exothermal roasting may furthermore provide heat emissions as an energy source for usage in BHP’s processing routine. The silicate residue from module one will appear in a sequence of processing steps in module two. This module has a twofold aim: (1) separating ferro- and paramagnetic fractions and (2) dewatering the residues. Several steps will accomplish a separation of concentrated, iron-rich garnet. Novel membrane technologies will treat wastewater streams from modules one and two and represent the key-technology in module three. The major purpose of water treatment is to maximize the amount of clean water for nearby and downstream communities.

The recent waves of COVID-19 highlighted the importance of understanding and quantifying spatiotemporal interactions to infer, model, and predict disease spread in real time. In this demonstration paper, we present a robust infrastructure for interactive exploration of interregional and international spatiotemporal interactions via time-lagged correlations of increases in COVID-19 incidence. This infrastructure consists of: (i) an operational data store (ODS) coupled with automated scripts for downloading, cleaning, and processing data from heterogeneous sources; (ii) a server application handling on-demand analyses of the database data through a RESTful API; and (iii) a web application providing the interactive dashboard to explore various correlation and geostatistical metrics of the integrated data in spacetime. The environment allows users to study focal spatiotemporal trends and the potential of regions to export and import the virus. Moreover, the application has the potential to reveal the effect of the national border to mitigate the interaction, particularly the spread of the virus. The infrastructure serves COVID-19 data from Germany, Poland, and Czechia, with the possibility of extension to other regions and topics. The dashboard is under active development and accessible on www.where2test.de/correlation.

In this paper, we present Computer-Assisted Semantic Text Modelling (CASTEMO), a novel approach to transformation of textual resources into deeply structured data stored in JSON-based document databases. We also present the InkVisitor application which assists this data collection workflow and helps validate the data. Both the workflow and the application were developed within the Dissident Networks Project (DISSINET, https://dissinet.cz).

CASTEMO is based on widespread ideas, such as the idea of semantic data (e.g. Semantic Web) and the syntactic structure of natural language sentences (in our case, subject-verb-object1-object2 quadruples), and we acknowledge convergent developments (mainly Roberto Franzosi’s Quantitative Narrative Analysis). Nevertheless, we follow our own path towards deeply structured and deeply semantic data drawn from texts which allow us to preserve, and thus quantitatively analyse, e.g.:

• the order and syntactic embeddedness of information;

• the textual embeddedness of information (i.e. who is speaking, to whom, and in what context);

• the original language, expression, and discourse;

• the distinction between epistemic levels.

CASTEMO thus offers a time-intensive but extremely powerful alternative to (1) text mining, which often fails to answer fine-grained questions, and (2) Computer-Assisted Qualitative Data Analysis Software (CAQDAS), where opportunities of quantification are too incidental and severely limited by the original hypothesis. CASTEMO should be of interest to projects interested in quantitatively analysing information strictly in the context of its production (“source criticism 2.0”), and looking at the discourse of texts.

In this paper, we present the foundations of this data collection workflow, its selling points, as well as caveats for potential users. We also provide a first public presentation of InkVisitor, an open-source browser-based application implementing the CASTEMO workflow.

Ziel der Masterarbeit soll es sein, eine Vorstufe (Präkursor) eines Radiotracers zu synthetisieren, welcher es ermöglichen soll, ^197(m)Hg zu binden und so als metallorganisches Radiopharmakon vorzuliegen. Diese Substanz soll letztlich dazu dienen, Gammastrahlung für die Diagnostik und Konversionselektronen für die Therapie auszusenden, sodass der fertige Radiotracer für theranostische Zwecke Anwendung finden kann. Dabei soll als Grundkörper ein Bispidin dienen, wobei die bisher verwendeten Phenylgruppen an Position C-1 und C-5 durch Methylgruppen ausgetauscht werden sollen, um die Lipophilie der Verbindung zu senken.
Dazu sollen mittels nucleophiler Substitution zwei neue Funktionalitäten an die sekundären Amine angebracht werden, mit denen es letztlich ermöglicht wird, eine Di-Aryl-quecksilberverbindung auszubilden. Die Synthese und Untersuchung unterschiedlicher Seitenarme sollen dabei ebenfalls genauer betrachtet werden.
Ein weiter Punkt soll sein, die C-9-Position am Bispidingerüst zu modifizieren, um ein entsprechendes Vektormolekül anbringen zu können. Dadurch wird ermöglicht, dass sich der resultierende Radiotracer selektiv an Zellen anlagert. Dabei kann das Vektormolekül auch vor der Anbringung der Seitenarme an der C-9-Position des Bispidins gebunden werden.

The cause of the rapid and geographically impressive spread of Mithraism in the Roman empire is still only partially understood. Scholars had speculated about the influence of the Roman army and the popularity of Mithraism among Roman soldiers. However, a meticulously conducted demographical study of Mithraic epigraphy problematized this view. To demonstrate the possible influence of the Roman military infrastructure on the spread of Mithraism in the Roman empire, we coded all sites of documented Mithraic presence and locations of the major Roman legionary fortresses, positioned them on the transportation network and used statistical analysis to detect a possible relationship between these datasets, both at the level of the whole Roman empire and regionally. Although we did not find, at the level of the Roman empire, a statistically significant overlap between the locations of the Roman legionary fortresses and Mithraic sites, we discovered the statistically significant presence of Mithraic evidence in nodes important on thresholded military subnetworks connecting the Roman legionary fortresses. These results support the view that the Roman army infrastructure contributed to the spread of Mithraism and can partially explain the geographical distribution of archaeologically attested Mithraic evidence in the Roman Empire but cannot be seen as a single factor playing a role in the transmission of Mithraism.

Isonicotinic acid (INA), as a prototypical N, O-donor bifunctional ligand, has demonstrated its ability to differentiate Th4+ from representative ions for products in spent nuclear fuels (Cs+, Ba2+, Mn2+, Fe2+, Fe3+, Co2+, Ni2+, Cu2+, Pd2+, ReO4-, La3+, Ce3+, Ce4+, UO22+), yielding an actinide metal-organic framework (An-MOF), Th-INA-1, by selective crystallization. This unprecedented motif with the highest ligand-binding number (i.e., 16) shows promise as a primary waste form due to its structural integrity, especially upon irradiation up to 6 MGy γ-or β-irradiation.

This paper presents an approach to the collection of structured relational data which we have developed over the last 2.5 years in the Dissident Networks Project (DISSINET, https://www.dissinet.cz/). Our goal has been to devise a data model and environment capable of capturing the detail of inquisitorial records: the persons, groups, events, attitudes and physical objects they describe, the reported social, spatial and temporal relations between them, but also the modality of speech (negation, question, possibility etc.), the chain of information flow in inquisitorial records (e.g. who is reporting what and when, who is inculpating whom), and the different modes of trial interaction and recording. We thus preserve the semantic structure and detail of our sources. The data thus collected then allows us to analyze the social, spatial, and discursive patterns of inquisitorial records, heresy trials, and medieval religious dissent using a variety of computational and quantitative methods, such as social and spatial network analysis, geographic information science, and natural language processing. In addition, our data model and the experience gained from devising it will be of interest even beyond heresy and inquisition research, above all to historians keen to explore the possibilities of analysis of structured data while preserving the detail and the discursive patterns of their sources.

Small actinium-225-labeled prostate-specific membrane antigen (PSMA)-targeted radioconjugates have been described for targeted alpha therapy of metastatic castration-resistant prostate cancer. Transient binding to serum albumin as a highly abundant, inherent transport protein represents a commonly applied strategy to modulate the tissue distribution profile of such low-molecular-weight radiotherapeutics and to enhance radioactivity uptake into tumor lesions with the ultimate objective of improved therapeutic outcome. Two ligands mcp-M-alb-PSMA and mcp-D-alb-PSMA were synthesized by combining a macropa-derived chelator with either one or two lysine-ureido-glutamate-based PSMA- and 4-(p-iodophenyl)butyrate albumin-binding entities using multistep peptide-coupling chemistry. Both compounds were labeled with [²²⁵Ac]Ac³⁺ under mild conditions and their reversible binding to serum albumin was analyzed by an ultrafiltration assay as well as microscale thermophoresis measurements. Saturation binding studies and clonogenic survival assays using PSMA-expressing LNCaP cells were performed to evaluate PSMA-mediated cell binding and to assess the cytotoxic potency of the novel radioconjugates [²²⁵Ac]Ac-mcp-M-alb-PSMA and [²²⁵Ac]Ac-mcp-D-alb-PSMA, respectively. Biodistributions of both 225Ac-radioconjugates were investigated using LNCaP tumor-bearing SCID mice.

The coupled DYN3D/ATHLET code system was recently adapted for Sodium cooled Fast Reactors (SFRs) applications. The main objective of this study is to validate further the DYN3D/ATHLET code system by performing a coupled 3D neutron kinetics/thermal-hydraulics analysis of six transient start-up tests conducted at the French Superphenix (SPX) reactor. The tests were a part of the startup test program aiming at evaluation of the core reactivity feedback characteristics. Peculiarity of these transients is the necessity of accounting for the thermal expansions of the primary system structural elements influencing the position of control rods in the core.
The paper includes a brief summary on the benchmark specification, description of the neutronics and thermal-hydraulics models, and comparison of the simulation results to the available experimental data. For all six transients, a good agreement between simulations and experiments was observed confirming a reasonable performance of DYN3D/ATHLET.

Adoptive therapy using CAR-T cells as "living drugs" is an emerging field in cancer immunotherapy. Our group particularly focusses on the development and clinical translation of novel adaptor CAR-T platforms (UniCAR & RevCAR), in which CAR T-cell activity is controlled by tumor-specific adaptor molecules. Apart from their immunotherapeutic potential demonstrated by numerous preclinical and an initial clinical proof-of-concept study, our adaptor CAR technologies represent also an ideal starting point for the development of cancer theranostics. Preclinical studies have shown that adaptor molecules can be easily radiolabeled for tumor therapy and diagnostics. Conversely, clinically used PET tracers have been successfully converted into highly efficient adaptor molecules for CAR T-cell immunotherapy. Overall, adaptor molecules are versatile tools for (i) CAR-T cell therapy, (ii) noninvasive diagnostic imaging, and (iii) targeted radioimmunotherapy, underscoring that combinatorial theranostic adaptor CAR-T approaches may open new avenues for effective cancer therapy.

Modification of autologous T cells with tumor-specific chimeric antigen receptors (CARs) has been shown to be an effective tool for treatment of hematologic malignancies. However, clinical translation towards solid tumors is still limited as demonstrated by the suboptimal performance of CAR T cells in first in-human studies. Combining advances in CAR-T cell therapy and cancer theranostics may provide a promising multimodal approach to increase therapeutic efficacy and achieve durable responses in cancer patients.
Here we present the development and functional characterization of different peptide- or antibody constructs targeting tumor-associated antigens or different structures of the tumor microenvironment. Equipment of these molecules with the E5B9 peptide epitope, enabled their use in the well-established UniCAR system. Accordingly, the constructs were able to specifically activate UniCAR T cells for efficient killing of both hematological and solid tumors in vitro and in vivo. Upon conjugation of chelators like DOTAGA and NODAGA, the peptide- or antibody derivates were further successfully applied for cancer theranostics, using e.g. 68Ga, 64Cu as diagnostic radionuclides and e.g. 67Cu, 225Ac as therapeutic radionuclides. PET imaging in xenotransplanted mice has demonstrated high contrast tumor accumulation. Consistent with the stable tumor accumulation, 225Ac-labeled molecules demonstrated therapeutic efficacy in a xenograft mouse model.
In summary, E5B9-tagged peptide- or antibody constructs open a new era of cancer immunotheranostics as they can be used multimodally for (i) UniCAR-T cell therapy, (ii) non-invasive diagnostic imaging and (iii) targeted radioimmunotherapy. Prospectively, they could thus bridge the gap between the fields of CAR-T cells and cancer theranostics.

CAR T-cell therapy achieved unparalleled clinical success rates for treatment of patients with hematological
malignancies. However, progress and clinical translation towards solid tumors is slow and hampered by many
factors e.g. increased complexity, high heterogeneity and an immunosuppressive tumor microenvironment.
Thus, CAR T-cell therapy alone might not result in durable antitumor responses. Combinatorial approaches are
promising strategies to improve CAR T-cell efficacy in solid tumor treatment. In this regard, we here aim to
combine conventional cancer theranostics with CAR T-cell immunotherapy in one single approach.

By using the well-established UniCAR system, a novel, multifunctional tool termed PSCA-IgG4 target module
(TM) was developed for dual prostate cancer theranostics. It comprises a human PSCA-specific binding
domain, the hinge and Fc-domain of human IgG4 molecules as well as the UniCAR epitope E5B9. As shown by
in vitro assays with PSCA-positive and PSCA-negative prostate cancer cells, the novel TM redirected UniCAR T
cells for efficient tumor cell lysis in a strictly antigen- and TM-dependent manner. After radiolabeling with
copper-64 or actinium-225, the novel PSCA-IgG4 TM was successfully applied for diagnostic imaging and
targeted radioimmunotherapy. The 64Cu-labeled PSCA-IgG4 TM showed maximal tumor accumulation with
optimal tumor-to-background ratios after 1.5 days. Furthermore, targeted alpha-therapy with the 225Aclabeled
TM significantly delayed the outgrowth of established tumors in mice.

In summary, the here presented, novel PSCA-IgG4 TM is a promising candidate for dual theranostics of
prostate cancer that may help to overcome present hurdles in solid tumor therapy. After radiolabeling it
facilitates not only targeted alpha-therapy and diagnostic imaging of PSCA, but can be also repurposed as a
TM for UniCAR T-cell immunotherapy.

Hyperspectral imaging is an important technology in the field of geosciences and remote sensing. However, the high-dimensional nature of hyperspectral images (HSIs) together with the limited availability of training/labeled samples challenge an efficient processing of HSIs. To alleviate these challenges, we propose a deep multi-resolution clustering network (DMC-Net) to analyze HSIs. DMC-Net, without requiring training/labeled samples for the training process, captures the non-linear intrinsic relation within data points in an HSI and analyzes the image at various resolutions by applying atrous convolutions. Furthermore, DMC-Net preserves the spectral information by directly incorporating extracted features from the original HSI into the reconstruction phase. In terms of clustering accuracy, experimental results on two real HSIs demonstrate the superior performance of DMC-Net compared to the state-of-the-art deep learning-based clustering approaches.