Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA.

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases. IMPORTANCE: We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality. Existing deep learning-based methods that account for inter-modality misalignment perform better, but still share two main common limitations: (1) The spatial alignment task is not adaptively integrated with the reconstruction process, resulting in insufficient complementarity between the two tasks; (2) the entire framework has weak interpretability. In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process. Concretely, we derive a novel joint alignment-reconstruction model with a specially designed aligned cross-modal prior term. By relaxing the model into cross-modal spatial alignment and multi-modal reconstruction tasks, we propose an effective algorithm to solve this model alternatively. Then, we unfold the iterative stages of the proposed algorithm and design corresponding network modules to build DUN-SA with interpretability. Through end-to-end training, we effectively compensate for spatial misalignment using only reconstruction loss, and utilize the progressively aligned reference modality to provide inter-modality prior to improve the reconstruction of the target modality. Comprehensive experiments on four real datasets demonstrate that our method exhibits superior reconstruction performance compared to state-of-the-art methods.

A Simple and User-Friendly Method for High-Quality Preparation of Pollen Grains for Scanning Electron Microscopy (SEM).

Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.

Probing the structural stability of R-phycocyanin under pressure.

The red macroalgae Porphyra, commonly known as Nori, is widely used as food around the world due to its high nutrient content, including the significant abundance of colored phycobiliproteins (PBPs). Among these, R-phycocyanin (R-PC) stands out for its vibrant purple color and numerous bioactive properties, making it a valuable protein for the food industry. However, R-PC's limited thermal stability necessitates alternative processing methods to preserve its color and bioactive properties. Our study aimed to investigate the in-situ stability of oligomeric R-PC under high pressure (HP) conditions (up to 4000 bar) using a combination of absorption, fluorescence, and small-angle X-ray scattering (SAXS) techniques. The unfolding of R-PC is a multiphase process. Initially, low pressure induces conformational changes in the R-PC oligomeric form (trimers). As pressure increases above 1600 bar, these trimers dissociate into monomers, and at pressures above 3000 bar, the subunits begin to unfold. When returned to atmospheric pressure, R-PC partially refolds, retaining 50% of its original color absorbance. In contrast, heat treatment causes irreversible and detrimental effects on R-PC color, highlighting the advantages of HP treatment in preserving both the color and bioactive properties of R-PC compared to heat treatment.

Order/Disorder Transitions Upon Protein Binding: A Unifying Perspective.

When two proteins bind to each other, this process is often accompanied by a change in their structural states (from disordered to ordered or vice versa). As it turns out, there are 10 distinct possibilities for such binding-related order/disorder transitions. Out of this number, seven scenarios have been experimentally observed, while another three remain hitherto unreported. As an example, we discuss the so-called mutual synergistic folding, whereby two disordered proteins come together to form a fully structured complex. Our bioinformatics analysis of the Protein Databank found potential new examples of this remarkable binding mechanism.

The linkage between functional traits and drone-derived phenology of 74 Northern Hemisphere tree species.

Tree phenology is a major component of the global carbon and water cycle, serving as a fingerprint of climate change, and exhibiting significant variability both within and between species. In the emerging field of drone monitoring, it remains unclear whether this phenological variability can be effectively captured across numerous tree species. Additionally, the drivers behind interspecific variations in the phenology of deciduous trees are poorly understood, although they may be linked to plant functional traits. In this study, we derived the start of season (SOS), end of season (EOS), and length of season (LOS) for 3099 individuals from 74 deciduous tree species of the Northern Hemisphere at a unique study site in southeast Germany using drone imagery. We validated these phenological metrics with in-situ data and analyzed the interspecific variability in terms of plant functional traits. The drone-derived SOS and EOS showed high agreement with ground observations of leaf unfolding (R2 = 0.49) and leaf discoloration (R2 = 0.79), indicating that this methodology robustly captures phenology at the individual level with low temporal and human effort. Both intra- and interspecific phenological variability were high in spring and autumn, leading to differences in the LOS of up to two months under almost identical environmental conditions. Functional traits such as seed dry mass, chromosome number, and continent of origin played significant roles in explaining interspecific phenological differences in SOS, EOS, and LOS, respectively. In total, 55 %, 39 %, and 45 % of interspecific variation in SOS, EOS, and LOS could be explained by the Boosted Regression Tree (BRT) models based on functional traits. Our findings encourage new research avenues in tree phenology and advance our understanding of the growth strategies of key tree species in the Northern Hemisphere.

Atomic resolution map of the solvent interactions driving SOD1 unfolding in CAPRIN1 condensates.

Biomolecules can be sequestered into membrane-less compartments, referred to as biomolecular condensates. Experimental and computational methods have helped define the physical-chemical properties of condensates. Less is known about how the high macromolecule concentrations in condensed phases contribute "solvent" interactions that can remodel the free-energy landscape of other condensate-resident proteins, altering thermally accessible conformations and, in turn, modulating function. Here, we use solution NMR spectroscopy to obtain atomic resolution insights into the interactions between the immature form of superoxide dismutase 1 (SOD1), which can mislocalize and aggregate in stress granules, and the RNA-binding protein CAPRIN1, a component of stress granules. NMR studies of CAPRIN1:SOD1 interactions, focused on both unfolded and folded SOD1 states in mixed phase and demixed CAPRIN1-based condensates, establish that CAPRIN1 shifts the SOD1 folding equilibrium toward the unfolded state through preferential interactions with the unfolded ensemble, with little change to the structure of the folded conformation. Key contacts between CAPRIN1 and the H80-H120 region of unfolded SOD1 are identified, as well as SOD1 interaction sites near both the arginine-rich and aromatic-rich regions of CAPRIN1. Unfolding of immature SOD1 in the CAPRIN1 condensed phase is shown to be coupled to aggregation, while a more stable zinc-bound, dimeric form of SOD1 is less susceptible to unfolding when solvated by CAPRIN1. Our work underscores the impact of the condensate solvent environment on the conformational states of resident proteins and supports the hypothesis that ALS mutations that decrease metal binding or dimerization function as drivers of aggregation in condensates.

Alternating binding and p97-mediated dissociation of SDS22 and I3 recycles active PP1 between holophosphatases.

Protein phosphatase-1 catalytic subunit (PP1) joins diverse targeting subunits to form holophosphatases that regulate many cellular processes. Newly synthesized PP1 is known to be transiently sequestered in an inhibitory complex with Suppressor-of-Dis2-number-2 (SDS22) and Inhibitor-3 (I3), which is disassembled by the ATPases Associated with diverse cellular Activities plus (AAA+) protein p97. Here, we show that the SDS22-PP1-I3 complex also acts as a thermodynamic sink for mature PP1 and that cycles of SDS22-PP1-I3 formation and p97-driven disassembly regulate PP1 function and subunit exchange beyond PP1 biogenesis. Förster Resonance energy transfer (FRET) analysis of labeled proteins in vitro revealed that in the p97-mediated disassembly step, both SDS22 and I3 dissociate concomitantly, releasing PP1. In presence of a targeting subunit, for instance Growth Arrest and DNA Damage-inducible protein 34 (GADD34), liberated PP1 formed an active holophosphatase that dephosphorylated its substrate, eukaryotic translation initiation factor 2 alpha (eIF2α). Inhibition of p97 results in displacement of the GADD34 targeting subunit by rebinding of PP1 to SDS22 and I3 indicating that the SDS22-PP1-I3 complex is thermodynamically favored. Likewise, p97 inhibition in cells causes rapid sequestration of PP1 by free SDS22 and I3 at the expense of other subunits. This suggests that PP1 exists in a steady state maintained by spontaneous SDS22-PP1-I3 formation and adenosine triphosphate (ATP) hydrolysis, p97-driven disassembly that recycles active PP1 between different holophosphatase complexes to warrant a dynamic holophosphatase landscape.

Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models.

Two research streams on responses to Likert-type items have been developing in parallel: (a) unfolding models and (b) individual response styles (RSs). To accurately understand Likert-type item responding, it is vital to parse unfolding responses from RSs. Therefore, we propose the Unfolding Item Response Tree (UIRTree) model. First, we conducted a Monte Carlo simulation study to examine the performance of the UIRTree model compared to three other models - Samejima's Graded Response Model, Generalized Graded Unfolding Model, and Dominance Item Response Tree model, for Likert-type responses. Results showed that when data followed an unfolding response process and contained RSs, AIC was able to select the UIRTree model, while BIC was biased toward the DIRTree model in many conditions. In addition, model parameters in the UIRTree model could be accurately recovered under realistic conditions, and mis-specifying item response process or wrongly ignoring RSs was detrimental to the estimation of key parameters. Then, we used datasets from empirical studies to show that the UIRTree model could fit personality datasets well and produced more reasonable parameter estimates compared to competing models. A strong presence of RS(s) was also revealed by the UIRTree model. Finally, we provided examples with R code for UIRTree model estimation to facilitate the modeling of responses to Likert-type items in future studies.

Utilizing Involuntary Unfolding Phenomena as Catalyst for Adaptive Responsiveness and Reorganization.

This paper explores the complex area of so-called unfolding processes and unfolding phenomena in hypnotherapy, emphasizing on unfolding movements and unfolding images, identified as complex involuntary phenomena, which emerge spontaneously without suggestions for these phenomena. Unfolding phenomena are examined as foundational elements of the unfolding process. On the one hand, the study unveils the macro-structure of the unfolding process by categorizing it into involuntary unfolding sets, phases, and unfolding subunits, all of which are analogous among patients and across different organizational levels. On the other hand, detailing the micro-structure aids in elucidating the functionality and elements of the unfolding phenomena. These elements encompass unfolding schemas and instruments. They are emphasized for their role in intrapsychic ideodynamics and responsiveness, aiding recurrent induction aligned and being promoted via an intuitive state of consciousness. Unfolding phenomena elucidate resource elicitation, and adaptation, contributing to the phenotypic organization and reorganization. In this context, the mechanisms of responsiveness, characterized by an unfolding process during hypnotherapy with distinct phases, are examined to help therapists and researchers understand how hypnotherapy can induce phenotypic changes, thereby facilitating treatment, personal growth, and the emergence of a new form of consciousness.

Conservation of kinetic stability, but not the unfolding mechanism, between human transthyretin and a transthyretin-related enzyme.

Kinetic stability is thought to be an attribute of proteins that require a long lifetime, such as the transporter of thyroxine and holo retinol-binding protein or transthyretin (TTR) functioning in the bloodstream, cerebrospinal fluid, and vitreous humor. TTR evolved from ancestral enzymes known as TTR-related proteins (TRPs). Here, we develop a rate-expansion approach that allows unfolding rates to be measured directly at low denaturant concentration, revealing that kinetic stability exists in the Escherichia coli TRP (EcTRP), even though the enzyme structure is more energetically frustrated and has a more mutation-sensitive folding mechanism than human TTR. Thus, the ancient tetrameric enzyme may already have been poised to mutate into a kinetically stable human transporter. An extensive mutational study that exchanges residues at key sites within the TTR and EcTRP dimer-dimer interface shows that tyrosine 111, replaced by a threonine in TTR, is the gatekeeper of frustration in EcTRP because it is critical for function. Frustration, virtually absent in TTR, occurs at multiple sites in EcTRP and even cooperatively for certain pairs of mutations. We present evidence that evolution at the C terminus of TTR was a compensatory event to maintain the preexisting kinetic stability while reducing frustration and sensitivity to mutation. We propose an "overcompensation" pathway from EcTRPs to functional hybrids to modern TTRs that is consistent with the biophysics discussed here. An alternative plausible pathway is also presented.

Stopping criteria for neutron spectrum unfolding algorithms.

Neutron spectrum unfolding is a crucial process in radiation protection and dosimetry. Unfolding codes using iterative algorithms require a criterion to stop the iterations. One approach often relies on the Root Mean Square Error (RMSE) criterion to assess the convergence of iterative algorithms. The aim of this work is to present a new criteria: Average Ratio Scaled (AVGS) and Relative Change in AVGS (dAVGS) to address specific challenges associated with RMSE. Extensive validation tests were conducted, covering a range of scenarios with results showing high level of agreement between the unfolded spectra and the reference.

Insulin fibrillation under physicochemical parameters of bioprocessing and intervention by peptides and surface-active agents.

Even after the centenary celebration of insulin discovery, there prevail challenges concerning insulin aggregation, not only after repeated administration but also during industrial production, storage, transport, and delivery, significantly impacting protein quality, efficacy, and effectiveness. The aggregation reduces insulin bioavailability, increasing the risk of heightened immunogenicity, posing a threat to patient health, and creating a dent in the golden success story of insulin therapy. Insulin experiences various physicochemical and mechanical stresses due to modulations in pH, temperature, ionic strength, agitation, shear, and surface chemistry, during the upstream and downstream bioprocessing, resulting in insulin unfolding and subsequent fibrillation. This has fueled research in the pharmaceutical industry and academia to unveil the mechanistic insights of insulin aggregation in an attempt to devise rational strategies to regulate this unwanted phenomenon. The present review briefly describes the impacts of environmental factors of bioprocessing on the stability of insulin and correlates with various intermolecular interactions, particularly hydrophobic and electrostatic forces. The aggregation-prone regions of insulin are identified and interrelated with biophysical changes during stress conditions. The quest for novel additives, surface-active agents, and bioderived peptides in decelerating insulin aggregation, which results in overall structural stability, is described. We hope this review will help tackle the real-world challenges of insulin aggregation encountered during bioprocessing, ensuring safer, stable, and globally accessible insulin for efficient management of diabetes.

Quantum dot-protein interface: Interaction of the CdS quantum dot with human hemoglobin for the study of the energy transfer process and binding mechanism along with detection of the unfolding of hemoglobin.

In this study, the interaction of the human hemoglobin with cost effective and chemically fabricated CdS quantum dots (QDs) (average sizes ≈3nm) has been investigated. The semiconductor QDs showed maximum visible absorption at 445 nm with excitonic formation and band gap of ≈ 2.88 eV along with hexagonal crystalline phase. The binding of QDs-Hb occurs through corona formation to the ground sate complex formation. The life time of the heme pocket binding and reorganization were found to be t1 = 43 min and t2 = 642 min, respectively. The emission quenching of the Hb has been indicated large energy transfer between CdS QDs and Hb with tertiary deformation of Hb. The binding thermodynamics showed highly exothermic nature. The ultrafast decay during corona formation was studied from TCSPC. The results showed that the energy transfer efficiency increases with the increase of the QDs concentration and maximum ≈71.5 % energy transfer occurs and average ultrafast lifetime varies from 5.45 ns to1.51 ns. The deformation and unfolding of the secondary structure of Hb with changes of the α-helix (≈74 % to ≈51.07 %) and ß-sheets (≈8.63 % to ≈10.25 %) have been observed from circular dichroism spectrum. The SAXS spectrum showed that the radius of gyration of CdS QDs-Hb bioconjugate increased (up to 23 ± 0.45 nm) with the increase of the concentration of QDs compare with pure Hb (11 ± 0.23 nm) and Hb becoming more unfolded.

A rapid method to monitor structural perturbations of high-concentrated therapeutic antibody solutions using Intrinsic Tryptophan Fluorescence Emission spectroscopy.

Drug product development of therapeutic antibody formulations is still dictated by the risk of protein particle formation during processing or storage, which can lead to loss of potency and potential immunogenic reactions. Since structural perturbations are the main driver for irreversible protein aggregation, the conformational integrity of antibodies should be closely monitored. The present study evaluated the applicability of a plate reader-based high throughput method for Intrinsic Tryptophan Fluorescence Emission (ITFE) spectroscopy to detect protein aggregation due to protein unfolding in high-concentrated therapeutic antibody samples. The impact of fluorophore concentration on the ITFE signal in microplate readers was investigated by analysis of dilution series of two therapeutic antibodies and pure tryptophan. At low antibody concentrations (< 5 mg/mL, equivalent to 0.8 mM tryptophan), the low inner filter effect suggests a quasi-linear relationship between antibody concentration and ITFE intensity. In contrast, the constant ITFE intensity at high protein concentrations (> 40 mg/mL, equivalent to 6.1 mM tryptophan) indicate that ITFE spectroscopy measurements of IgG1 antibodies are feasible in therapeutically relevant concentrations (up to 223 mg/mL). Furthermore, the capability of the method to detect low levels of unfolding (around 1 %) was confirmed by limit of detection (LOD) determination with temperature-stressed antibody samples as degradation standards. Change of fluorescence intensity at the maximum (ΔIaM) was identified as sensitive descriptor for protein degradation, providing the lowest LOD values. The results demonstrate that ITFE spectroscopy performed in a microplate reader is a valuable tool for high-throughput monitoring of protein degradation in therapeutic antibody formulations.

Job embeddedness, career adaptability, and perceived overqualification under career shocks-findings from PLS and NCA.

BACKGROUND: Amidst the post-COVID-19 economic downturn and the expanding higher education landscape in China, employee employment challenges have given rise to the widespread overqualification issue. This phenomenon has attracted extensive attention and is prompting a need for an in-depth exploration of perceived overqualification. However, existing studies predominantly concentrate on its outcomes rather than antecedents, leaving a notable gap in understanding the influence mechanism between individual advantageous resources (e.g., job embeddedness, career adaptability) and overqualification, particularly in specific events such as career shocks. OBJECTIVE: This study aims to examine the interplay between employees' career adaptability, job embeddedness, and the mediating role of relative deprivation in shaping perceived overqualification, particularly in the aftermath of career shocks. METHODS: A comprehensive analysis was conducted using data gathered from 339 questionnaire responses. Partial Least Square (PLS) path analysis, R's necessary condition analysis (NCA), and the Random Forest (RF) algorithm were employed to scrutinize the relationships and identify critical factors influencing perceived overqualification. RESULTS: The findings indicate that after encountering career shocks, career adaptability and job embeddedness not only directly impact perceived overqualification but also exert their influence indirectly through the mediation of relative deprivation; Career adaptability, job embeddedness, and relative deprivation are necessary conditions for perceived overqualification, with relative deprivation having the most significant impact. CONCLUSIONS: Based on the results, focusing on the psychological changes of employees after suffering career shocks provides valuable guidance for managers in channelling the emotional and cognitive responses of their employees.

Efficient leather spreading operations by dual-arm robotic systems.

To achieve precise grasping and spreading of irregular sheet-like soft objects (such as leather) by robots, this study addresses several challenges, including the irregularity of leather edges and the ambiguity of feature recognition points. To tackle these issues, this paper proposes an innovative method that involves alternately grasping the lowest point twice and using planar techniques to effectively spread the leather. We improved the YOLOV8 algorithm by incorporating the BIFPN network structure and the WIOU loss function, and trained a dedicated dataset for the lowest grasping points and planar grasping points, thereby achieving high-precision recognition. Additionally, we determined the optimal posture for grasping the lowest point and constructed an experimental platform, successfully conducting multiple rounds of leather grasping and spreading experiments with a success rate of 72%. Through an in-depth analysis of the failed experiments, this study reveals the limitations of the current methods and provides valuable guidance for future research.

Investigating Directional Invariance in an Item Response Tree Model for Extreme Response Style and Trait-Based Unfolding Responses.

Item response tree (IRTree) approaches have received increasing attention in the response style literature due to their capability to partial out response style latent traits from content-related latent traits by considering separate decisions for agreement and level of agreement. Additionally, it has shown that the functioning of the intensity of agreement decision may depend upon the agreement decision with an item, so that the item parameters and person parameters may differ by direction of agreement; when the parameters across direction are the same, this is called directional invariance. Furthermore, for non-cognitive psychological constructs, it has been argued that the response process may be best described as following an unfolding process. In this study, a family of IRTree models to handle unfolding responses with the agreement decision following the hyperbolic cosine model and the intensity of agreement decision following a graded response model is investigated. This model family also allows for investigation of item- and person-level directional invariance. A simulation study is conducted to evaluate parameter recovery; model parameters are estimated with a fully Bayesian approach using JAGS (Just Another Gibbs Sampler). The proposed modeling scheme is demonstrated with two data examples with multiple model comparisons allowing for varying levels of directional invariance and unfolding versus dominance processes. An approach to visualizing the final model item response functioning is also developed. The article closes with a short discussion about the results.

A Hierarchical Low-Rank Denoising Model for Remote Sensing Images Based on Deep Unfolding.

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results.

A tomographic spatial-unfolding method for Compton gamma imaging measurements.

An advanced spatial-unfolding technique capable of reconstructing the activity distribution within an exclusion zone from Compton gamma imager measurements taken outside of it is introduced. Although the method is generally applicable to extended sources, we demonstrate it here on a calibrated Cs-137 point source through Monte Carlo simulation studies as well as with measurements made using a Silicon Compton Telescope for Safety and Security (SCoTSS) gamma imager. For synthetic data the method accurately reconstructs the total activity contained within the mapped zone of interest, even when the size of the basis elements used to reconstruct the activity distribution is larger than the source itself. For experimental data, the method reliably located the source but underestimated its activity by up to 17%. This is accurate enough for real-world security applications. The underestimation is likely due to effects not yet included in the simulated response of the detector. The method has widespread applicability in the radiological/nuclear safety and security field, particularly for scenarios in which a threat material or contaminated area lies within a no-entry or no-fly zone.