Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward organ shortage resilient allocation policies using real-time queueing models for liver transplantation

We report in this paper on the potential interest of real-time queueing models to optimize organ allocation policies. We especially focus on building organ shortage resilient policies in terms of equity, as we experienced differential impact of the COVID epidemic organ shortage on transplant access, according to the cause of liver failure. Patient's death on the waiting list or dropout for being too sick, resulting from the absence of a timely available organ, is chosen as the main equity metric. Results obtained with the composite allocation score used in France is challenged against the so-called Early Simulated Deadline First (ESDF) real-time queueing discipline, under increasing levels of organ shortage, by extensive simulations. The ESDF policy is a variant of the well-know Earliest Deadline First (EDF) policy, which was shown as optimal in various contexts in the queueing literature. In the present case, the time to the deadline represents the remaining life duration of patients - which is of course unknown. So we propose to simulate a fictional life-duration, and give priority to the earliest simulated deadline. This leads to a simple and comprehensive representation of the system at hand by a Markov process. Our simulation results clearly show that the ESDF policy allows to maintain equity between indications, conversely to the scoring policy, which was not resilient to increasing levels of organ shortage.
Distribution Testing with a Confused Collector

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learning classifier that has not learned to distinguish all elements of the domain. The confused collector holds an unknown clustering of the domain and an input distribution $\mu$, and provides two oracles: a sample oracle which produces a sample from $\mu$ that has been labeled according to the clustering; and a label-query oracle which returns the label of a query point $x$ according to the clustering. Our first set of results shows that identity, uniformity, and equivalence of distributions can be tested efficiently, under the earth-mover distance, with remarkably weak conditions on the confused collector, even when the unknown clustering is adversarial. This requires defining a variant of the distribution testing task (inspired by the recent testable learning framework of Rubinfeld&Vasilyan), where the algorithm should test a joint property of the distribution and its clustering. As an example, we get efficient testers when the distribution tester is allowed to reject if it detects that the confused collector clustering is"far"from being a decision tree. The second set of results shows that we can sometimes do significantly better when the clustering is random instead of adversarial. For certain one-dimensional random clusterings, we show that uniformity can be tested under the TV distance using $\widetilde O\left(\frac{\sqrt n}{\rho^{3/2} \epsilon^2}\right)$ samples and zero queries, where $\rho \in (0,1]$ controls the"resolution"of the clustering. We improve this to $O\left(\frac{\sqrt n}{\rho \epsilon^2}\right)$ when queries are allowed.
One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images

Modern research increasingly relies on automated methods to assist researchers. An example of this is Optical Chemical Structure Recognition (OCSR), which aids chemists in retrieving information about chemicals from large amounts of documents. Markush structures are chemical structures that cannot be parsed correctly by OCSR and cause errors. The focus of this research was to propose and test a novel method for classifying Markush structures. Within this method, a comparison was made between fixed-feature extraction and end-to-end learning (CNN). The end-to-end method performed significantly better than the fixed-feature method, achieving 0.928 (0.035 SD) Macro F1 compared to the fixed-feature method's 0.701 (0.052 SD). Because of the nature of the experiment, these figures are a lower bound and can be improved further. These results suggest that Markush structures can be filtered out effectively and accurately using the proposed method. When implemented into OCSR pipelines, this method can improve their performance and use to other researchers.
Computer-assisted proofs for the many steady states of a chemotaxis model with local sensing

We study the steady states of a system of cross-diffusion equations arising from the modeling of chemotaxis with local sensing, where the motility is a decreasing function of the concentration of the chemical. In order to capture the many different equilibria that sometimes co-exist, we use computer-assisted proofs: Given an approximate solution obtained numerically, we apply a fixed-point argument in a small neighborhood of this approximate solution to prove the existence of an exact solution nearby. This allows us to rigorously study the steady states of this crossdiffusion system much more extensively than what previously possible with purely pen-and-paper techniques. Our computer-assisted argument makes use of Fourier series decomposition, which is common in the literature, but usually restricted to systems with polynomial nonlinearities. This is not the case for the model considered in this paper, and we develop a new way of dealing with some nonpolynomial nonlinearities in the context of computer-assisted proofs with Fourier series.
Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization

Adversarial machine learning (AML) studies attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen model robustness. Specifically for image classification, it is challenging to understand adversarial attacks due to their use of subtle perturbations that are not human-interpretable, as well as the variability of attack impacts influenced by diverse methodologies, instance differences, and model architectures. Through a design study with AML learners and teachers, we introduce AdvEx, a multi-level interactive visualization system that comprehensively presents the properties and impacts of evasion attacks on different image classifiers for novice AML learners. We quantitatively and qualitatively assessed AdvEx in a two-part evaluation including user studies and expert interviews. Our results show that AdvEx is not only highly effective as a visualization tool for understanding AML mechanisms, but also provides an engaging and enjoyable learning experience, thus demonstrating its overall benefits for AML learners.
MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis

Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness. These models are inherently prone to errors, potentially posing serious risks to patient health and could even cause irreparable harm. Traditional software assurance techniques rely on fixed code and do not directly apply to ML models since these algorithms are adaptable and learn from curated datasets through a training process. However, adapting established principles, such as boundary testing using synthetic test data can effectively bridge this gap. To this end, we present a novel technique called Mix-Up Boundary Analysis (MUBA) that facilitates evaluating image classifiers in terms of prediction fairness. We evaluated MUBA for two important medical imaging tasks -- brain tumour classification and breast cancer classification -- and achieved promising results. This research aims to showcase the importance of adapting traditional assurance principles for assessing ML models to enhance the safety and reliability of healthcare technologies. To facilitate future research, we plan to publicly release our code for MUBA.
Optimizing Chaotic Dynamics in a Semiconductor Laser with Dual Optical Feedback

Semiconductor lasers subject to optical feedback can behave chaotically, which can be used as a source of randomness. The optical feedback, provided by mirrors at a distance, determines the characteristics of the chaos and thus the quality of the randomness. However, this fixed distance also shows itself in the intensity, an unwanted feature called the Time Delay Signature (TDS). One promising solution to optimize the chaotic behavior is using double optical feedback, which we study here. In particular, we focus on the impact of the feedback phase, a small sub-wavelength change in the position of the mirrors, on the TDS and complexity of the system. We show that by optimizing the feedback parameters, including the feedback phases, the TDS can be suppressed, and that in some cases feedback phase control is necessary rather than optional. With control of all feedback parameters, it is possible to suppress the TDS without loss of the chaotic bandwidth. Further, we show that the system can restabilize at strong feedback rates, and one can switch between a chaotic and steady state by changing the feedback phase. Finally, we relate the feedback phase sensitivity to interference between the two delayed signals. This system is promising as one can either suppress the TDS without loss of the chaotic bandwidth or significantly increase the chaotic bandwidth.
Hybrid Circuit Mapping: Leveraging the Full Spectrum of Computational Capabilities of Neutral Atom Quantum Computers

Quantum computing based on Neutral Atoms (NAs) provides a wide range of computational capabilities, encompassing high-fidelity long-range interactions with native multi-qubit gates, and the ability to shuttle arrays of qubits. While previously these capabilities have been studied individually, we propose the first approach of a fast hybrid compiler to perform circuit mapping and routing based on both high-fidelity gate interactions and qubit shuttling. We delve into the intricacies of the compilation process when combining multiple capabilities and present effective solutions to address resulting challenges. The final compilation strategy is then showcased across various hardware settings, revealing its versatility, and highlighting potential fidelity enhancements achieved through the strategic utilization of combined gate- and shuttling-based routing. With the additional multi-qubit gate support for both routing capabilities, the proposed approach is able to take advantage of the full spectrum of computational capabilities offered by NAs.
Collective Relational Inference for learning heterogeneous interactions

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. These interacting systems can be modeled by graphs where edges correspond to the interactions between interactive entities. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. The associated challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a novel probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. We further show that when combined with known constraints, it allows us, for example, to discover physics-consistent interaction laws of particle systems. Overall the proposed model is data-efficient and generalizable to large systems when trained on smaller ones. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning.
Behavior of Rigid-Faced Reinforced Soil-Retaining Walls Subjected to Different Earthquake Ground Motions

AbstractA study of the seismic behavior of rigid-faced reinforced soil-retaining walls subjected to scaled earthquake ground motions is presented in this paper. A numerical model was developed and validated to simulate the shaking-table test of a rigid-faced reinforced soil-retaining wall. The calibrated numerical model was extended to develop a full-scale model to study the behavior of five scaled earthquake (EQ) ground motion excitations with different predominant frequencies ranging from 0.673 Hz for the Loma Prieta EQ to 5.437 Hz for the Parkfield EQ. Analyses of the input ground motions at the base of the wall model and their responses at the top show that amplitudes close to the fundamental frequency of the wall were amplified the most. Two deformation zones formed: a high-strain zone very close to the facing and a constant-strain zone extending beyond the reinforced zone. The extent of these zones varied with different ground motion parameters. The minimum and maximum lengths of the shear deformati.
TREVIXMBD: A Transcriptional Regulation-driven Variational Inference Model to Speculate Gene Expression Mechanism with Integration of Single-cell Multi-omics

Single-cell multi-omics technology enables the concurrent measurement of multiple molecular entities, making it critical for unraveling the inherent gene regulation mechanisms driving cell heterogeneity. However, existing multi-omics techniques have limitations in capturing the intricate regulatory interactions among these molecular components. In this study, we introduce TREVIXMBD (Transcriptional REgulation-driven Variational Inference), a novel method that integrates the well-established gene regulation structure with scRNA-seq and scATAC-seq data through an advanced Bayesian framework. TREVIXMBD models the generation of gene expression profiles in individual cells by considering the integrated influence of three fundamental biological factors: accessibility of cis-regulatory elements regions, transcription factor (TF) activities and regulatory weights. TF activities and regulatory weights are probabilistically represented as latent variables, which capture the inherent gene regulatory significance. Hence, in contrast to gene expression, TF activities and regulatory weights that depict the cell states from a more intrinsic perspective, can keep consistent across diverse datasets. TREVIXMBD exhibits superior performance when compared to baseline methods in a variety of biological analyses, including cell typing, cell development tracking, and batch effect correction, as validated through comprehensive benchmarking. Moreover, TREVIXMBD can reveal variations in TF-gene regulation relationships across cells. The pretrained TREVIXMBD model can work even when only scRNA-seq is available. Overall, TREVIXMBD introduces a pioneering biological-mechanism-driven framework for elucidating cell states at a gene regulatory level. The model’s structure is adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis.
Focal adhesion-derived liquid-liquid phase separations regulate mRNA translation

Liquid-liquid phase separation (LLPS) has emerged as a major organizing principle in cells. Recent work showed that multiple components of integrin-mediated focal adhesions including p130Cas can form LLPS, which govern adhesion dynamics and related cell behaviors. In this study, we found that the focal adhesion protein p130Cas drives formation of structures with the characteristics of LLPS that bud from focal adhesions into the cytoplasm. Condensing concentrated cytoplasm around p130Cas-coated beads allowed their isolation, which were enriched in a subset of focal adhesion proteins, mRNAs and RNA binding proteins, including those implicated in inhibiting mRNA translation. Plating cells on very high concentrations of fibronectin to induce large focal adhesions inhibited message translation which required p130Cas and correlated with droplet formation. Photo-induction of p130Cas condensates using the Cry2 system also reduced translation. These results identify a novel regulatory mechanism in which high adhesion limits message translation via induction of p130Cas-dependent cytoplasmic LLPS. This mechanism may contribute to the quiescent state of very strongly adhesive myofibroblasts and senescent cells.
Sex differences in intrinsic functional cortical organization reflect differences in network topology rather than cortical morphometry

Brain size robustly differs between sexes. However, the consequences of this anatomical dimorphism on sex differences in intrinsic brain function remain unclear. We investigated the extent to which sex differences in intrinsic cortical functional organization may be explained by differences in cortical morphometry, namely brain size, microstructure, and the geodesic distances of connectivity profiles. For this, we computed a low dimensional representation of functional cortical organization, the sensory-association axis, and identified widespread sex differences. Contrary to our expectations, observed sex differences in functional organization were not fundamentally associated with differences in brain size, microstructural organization, or geodesic distances, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis were associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry. Teaser Investigating sex differences in functional cortical organization and their association to differences in cortical morphometry.
Proteomic mapping of macrophages in response to the clearance of apoptotic cells reveals a unique reprogramming profile

During the daily process of healthy cellular turnover, billions of cells undergo apoptosis in the human body. These cells are removed by phagocytic cells, namely macrophages through a process known as efferocytosis, which triggers a cascade of reprogramming events in the cell, with a shift towards a pro-resolving or ‘wound healing’ phenotype. To date, no study has attempted to investigate these phenotypic changes from a proteomic perspective. Here, we present a novel and robust workflow for the investigation of proteome and secretome changes in bone marrow-derived macrophages (BMDMs) following efferocytosis using stable isotope labelling by amino acids in cell culture (SILAC) combined with data-independent acquisition (DIA) mass spectrometry. We show that using this workflow we can dissect the mixed proteomes of BMDMs and apoptotic cells to specifically map the reprogramming events occurring in macrophages in the later stages of efferocytosis. Specifically, we find that efferocytic macrophages adopt an alternatively activated phenotype underpinned by an increase in efferocytic and anti-inflammatory markers. We also show that the secretome contains factors that can reprogram naïve BMDMs towards an efferocytosis-like, pro-resolving, phenotype. Our results provide an unprecedented view of the efferocytic landscape of macrophages and will aid in further understanding this important immunological process in the larger context of immune homeostasis and inflammatory disorders.
Identification of Essential Temperature-Stressed Genes From Apis mellifera Hypopharyngeal Glands Transcriptomes Under Variable Temperatures

Temperature is one of the essential abiotic factors required for honey bee survival and pollination. It affects honey bee physiology, behavior, and expression of related genes. Also, considered one of the major factors contributing to colony collapse disease (CCD). In this research, RNA-seq analysis was performed using hypopharyngeal glands (HGs) tissue at low (18 °C), high (25 °C), and regular (22 °C) temperatures. Differentially expressed genes (DEGs) were identified after comparing the three groups with one another based on temperature differences using DESeq analysis. 5196 common DEGs (cDEGs) were identified among the groups. They are highly enriched in RNA processing and RNA metabolism process while the KEGG pathway enrichment analysis showed that the cDEGs are enriched in longevity regulating pathway, MAPK signaling pathway-fly, and Glycerophospholipid metabolism. Further, 360 temperature-stressed genes identified are highly enriched in translation, oxidative activity, and ribonucleoprotein complex. The enriched KEGG pathway includes ribosome, oxidative phosphorylation, fatty acid metabolism, and citrate cycle (TCA cycle). All the top ten (10) hub genes among the 360 temperature-stressed genes are found up-regulated. In addition, heat-shock protein 90 (HSP90) known as the stressed response gene, and Gr10, the amino acid response gene were up-regulated and down-regulated respectively in the temperature-stressed group. Low expression of Gr10 under temperature-stress can affect nursing behavior and bee development. Ultimately, these findings will help in identifying honeybee-temperature survival mechanisms under varying temperature effects.
Multidimensional neural representations of social features during movie viewing

The social world is dynamic and contextually embedded. Yet, most studies utilize simple stimuli that do not capture the complexity of everyday social episodes. To address this, we implemented a movie viewing paradigm and investigated how the everyday social episodes are processed in the brain. Participants watched one of two movies during an MRI scan. Neural patterns from brain regions involved in social perception, mentalization, action observation, and sensory processing were extracted. Representational similarity analysis results revealed that several labeled social features (including social interaction, mentalization, the actions of others, characters talking about themselves, talking about others, and talking about objects) were represented in superior temporal gyrus (STG) and middle temporal gyrus (MTG). The mentalization feature was also represented throughout the theory of mind network, and characters talking about others engaged the temporoparietal junction (TPJ), suggesting that listeners may spontaneously infer the mental state of those being talked about. In contrast, we did not observe the action representations in frontoparietal regions of the action observation network. The current findings indicate that STG and MTG serve as central hubs for social processing, and that listening to characters talk about others elicits spontaneous mental state inference in TPJ during natural movie viewing.