Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible grouping patterns in a western and eastern chimpanzee community.

Primate social organizations, or grouping patterns, vary significantly across species. Behavioral strategies that allow for flexibility in grouping patterns offer a means to reduce the costs of group living. Chimpanzees (Pan troglodytes) have a fission-fusion social system in which temporary subgroups ("parties") change in composition because of local socio-ecological conditions. Notably, western chimpanzees (P. t. verus) are described as showing a higher degree of bisexual bonding and association than eastern chimpanzees, and eastern female chimpanzees (P. t. schweinfurthii) are thought to be more solitary than western female chimpanzees. However, reported comparisons in sociality currently depend on a small number of study groups, particularly in western chimpanzees, and variation in methods. The inclusion of additional communities and direct comparison using the same methods are essential to assess whether reported subspecies differences in sociality hold in this behaviorally heterogeneous species. We explored whether sociality differs between two communities of chimpanzees using the same motion-triggered camera technology and definitions of social measures. We compare party size and composition (party type, sex ratio) between the western Gahtoy community in the Nimba Mountains (Guinea) and the eastern Waibira community in the Budongo Forest (Uganda). Once potential competition for resources such as food and mating opportunities were controlled for, subspecies did not substantially influence the number of individuals in a party. We found a higher sex-ratio, indicating more males in a party, in Waibira; this pattern was driven by a greater likelihood in Gahtoy to be in all-female parties. This finding is the opposite of what was expected for eastern chimpanzees, where female-only parties are predicted to be more common. Our results highlight the flexibility in chimpanzee sociality, and caution against subspecies level generalizations.
SvABA: Genome-wide detection of structural variants and indels by local assembly

Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at-scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA’s performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs, and substantially improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that templated-sequence insertions occur in ~4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized SVs.
The Latent Threat in Wild Birds: Clostridium botulinum

Simple Summary This work addresses the significance of Clostridium botulinum in avian botulism. Its importance is underscored due to the numerous outbreaks affecting wild birds and the potential risks it poses to biodiversity. To comprehend this pathogen and effectively fight against it, this work has conducted a meticulous compilation of the most cutting-edge scientific studies to date. This allows the reader to gain insights into the most relevant microbiological characteristics of this bacterium, the conditions that most promote its proliferation, its special and effective epidemiological cycle, the avian species most susceptible to it, the most characteristic clinical manifestations and more. All of this is geared towards equipping us with the necessary tools to implement effective surveillance and control strategies, thereby preventing the death of individuals belonging to endangered species through accurate diagnosis and treatment. The aim is to minimise, as far as possible, the presence of this pathogen in the habitats of these birds. Abstract Avian botulism caused by Clostridium botulinum emerged in 1910, affecting birds across North America, leading to severe outbreaks exacerbated by climate change, decreasing water levels, and inadequate wastewater management. While deadly for birds, its epidemiological impact on humans and other animals remains limited. Despite its significance, understanding and controlling the disease remain challenging. This review delves into the pathogen’s epidemiology in wild bird populations, exploring the transmission, pathogenicity, clinical symptoms, diagnosis and treatment. The disease’s growing concern in wild birds relates to the bacterium’s adaptability and expansive spread, evident through genetic similarities among strains across countries. Outbreaks are influenced by environmental factors such as temperature and soil characteristics. Wild birds inadvertently transmit the bacterium, perpetuating the cycle through carcasses and flies. Some species suffer severely, while others, like scavengers, show resistance. Understanding disease mechanisms, involving potential toxin ingestion or internal production, remains ongoing. Clinical signs vary, affecting diverse bird orders. Diagnostic methods evolve, with treatment success varying among affected populations. Prevention and surveillance take precedence due to treatment challenges, emphasising population-based strategies and preventive measures to manage the widespread presence of C. botulinum.
Patterns of Alcohol Use in Hispanic Individuals with TBI over the Ten Years Post-Injury: A Model Systems Study

The study’s aim was to examine alcohol consumption patterns and predictors of consumption across time among Hispanics with traumatic brain injury (TBI) in the U.S. within ten years post-injury. This longitudinal cohort study included 1342 Hispanic individuals (77.6% males) from the multi-site, longitudinal TBI Model Systems (TBIMS) database. The main outcome measures were consumption information, demographic, and injury characteristics. Across the full sample, alcohol consumption variables generally demonstrated quadratic movement characterized by an initial increase followed by a plateau or slight decrease over the ten years post-injury. The predictors of higher consumption were being men, single, with a history of excessive alcohol use, with a nonviolent mechanism of injury, shorter duration of PTA, and higher levels of education. Participants had a greater number of 5+ drinks/episode occurrences in the past month if they were men and had had a greater number of 5+ drinks/episode occurrences in the month before injury. There was no differential change in alcohol consumption over time as a function of these predictors. This study identified a profile of at-risk Hispanics with TBI for increased alcohol consumption. These individuals should be identified and targeted for early evidence-based alcohol intervention after TBI when results might be most favorable.
Leptin deficient rats develop nonalcoholic steatohepatitis with unique disease progression

Nonalcoholic steatohepatitis (NASH) is an aggressive liver disease threatening public health, however its natural history is poorly understood. Unlike ob/ob mice, Lep∆I14/∆I14 rats develop unique NASH phenotype with steatosis, lymphocyte infiltration and ballooning after postnatal week 16. Using Lep∆I14/∆I14 rats as NASH model, we studied the natural history of NASH progression by performing an integrated analysis of hepatic transcriptome from postnatal week 4 to 48. Leptin deficiency results in a robust increase in expression of genes encoding 9 rate-limiting enzymes in lipid metabolism such as ACC and FASN. However, genes in positive regulation of inflammatory response are highly expressed at week 16 and then remain the steady elevated expression till week 48. The high expression of cytokines and chemokines including CCL2, TNFα, IL6 and IL1β is correlated with the phosphorylation of several key molecules in pathways such as JNK and NF-κB. Meanwhile, we observed cell infiltration of MPO+ neutrophils, CD8+ T cells, CD68+ hepatic macrophages and CCR2+ inflammatory monocyte-derived macrophages, together with macrophage polarization from M2 to M1. Importantly, Lep∆I14/∆I14 rats share more homologous genes with NASH patients than previously established mouse models and crab eating monkeys with spontaneous hepatic steatosis. Transcriptomic analysis showed that many drug targets in clinical trials can be evaluated in Lep∆I14/∆I14 rats. Conclusion We characterize Lep∆I14/∆I14 rats as a unique NASH model by performing a long-term (i.e., 4 to 48 postnatal weeks) integrated transcriptomic analysis. This work reveal the temporal dynamics of hepatic gene expression in lipid metabolism and inflammation, and shed light on understanding the natural history of NASH in human beings.
Thyroid Hormone Metabolites Quantified in Pup and Adult Rat Cerebellum, Cortex and Whole-Brain Samples Using an Automated Online SPE-LC-MS/MS Method

Changes in thyroid hormone (TH) levels in rat brain at early developmental stages are correlated with adverse effects on offspring development. To characterize the ability of substances to interfere with the TH concentrations in, e.g., rat brain, it is essential to know the mean TH concentrations in this tissue under control conditions. In this publication, an online solid-phase extraction (SPE) liquid chromatography (LC) tandem mass spectrometry (MS/MS) method was validated and used to measure TH metabolites (T4, T3, rT3, T2 and T1) in the brains of untreated rats. Data on TH concentrations in the whole brain and separate data from the cerebellum and the cortex are shown. The corresponding samples were gathered from young rats at postnatal days (PND) 4 and 21/22 and from adult rats. The results show inter alia the high accuracy and precision of the method, and LOQs of 0.02 ng/mL were determined for T1, T2 and rT3 and of 0.15 ng/mL for T3 and T4. Technical variability is low, as shown by the relative standard deviations of 7.5–20%. For our rat model, we found that T4, T3 and T2 concentrations rise from PND4 to PND21, whereas the rT3 concentration decreases; as well as there is no statistical difference between TH concentrations in the male and female rat brain. This method is suitable to analyze TH metabolites in the brain and build up a database of historical TH concentrations in control rats. Together, this yields a robust diagnostic tool to detect potentially adverse disturbances of TH homeostasis in the most vulnerable anatomic structure.
Autoimmune Hepatitis: From Evolution to Current Status—A Pathologist’s Perspective

Autoimmune hepatitis (AIH) is a chronic, relapsing and remitting, immune-mediated liver disease that progresses to cirrhosis if left untreated. A significant number of patients may present with acute hepatitis or acute liver failure, which are often misdiagnosed as toxic liver injury. AIH shows a preponderance in young women but may be seen in children and the elderly. Diagnosis requires the integration of clinical, biochemical, and serologic parameters, along with supportive liver histology and exclusion of other causes of liver disease. Liver biopsy is a prerequisite for diagnosis of AIH, to assess severity and stage of disease, exclude other entities, and recognize any concurrent morbidities. No single biomarker or histologic feature is pathognomonic for AIH. The diagnostic and histologic criteria have undergone several modifications since the original scoring system was proposed by the International Autoimmune Hepatitis Group (IAIHG) in 1993. Recently, the IAIHG has proposed consensus recommendations for histologic criteria, relevant for both acute and chronic AIH. This review article will describe the evolving diagnostic criteria for AIH, with their limitations and utility, and with an emphasis on the role of liver histology in the diagnosis and management of AIH.
Spatial and seasonal variations of atmospheric BTEX, sulfur dioxide, nitrogen dioxide, and ozone concentrations in Istanbul, and health risk assessment of BTEX

Abstract Volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene, and xylene (BTEX) along with inorganic gases such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) levels were found in the atmosphere of the Kemerburgaz region where environmental issues became a major concern due to nearby incineration plant and waste disposal facility in Istanbul. Ten sampling locations were selected considering possible sources in the study area. The sampling areas were classified as suburban, industrial, rural, and background regions. Sampling campaigns were carried out for four-week periods from March 2011 to August 2012 in all locations. Elevated concentrations of BTEX around roads and the industrial locations indicated that vehicle exhaust and industrial activities were the main sources of these pollutants in the region. Concentrations of NO2 were also high around roads. A much more uniform distribution was observed for SO2 during sampling periods. However higher levels were observed at suburban locations due to the use of coal for local heating especially during winter. Ozone concentrations were low at the industrial locations and roadsides, but high in suburban and rural locations downwind from the sources. The results of these organic and inorganic gases meet the national limit values. Furthermore, a lifetime risk assessment methodology was used to evaluate the potential adverse health effects of BTEX. The mean cancer risk level for benzene was estimated to be 7.71E-07 that is lower than assigned acceptable risk level of 1.0E-04. Toluene, ethylbenzene, and xylenes were lower than the specified level of 1.0 with respect to mean non-carcinogenic risks. The findings reveal that determined BTEX emissions do not pose a health threat to residents in the studied region.
Subjective Causality

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker's uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention $A$ is preferred to $B$ iff the expected utility of $A$ is greater than that of $B$. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker's preferences are consistent with some causal model and to identify causal judgements from observed behavior.
Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations

We provide an analysis of the squared Wasserstein-2 ($W_2$) distance between two probability distributions associated with two stochastic differential equations (SDEs). Based on this analysis, we propose the use of a squared $W_2$ distance-based loss functions in the \textit{reconstruction} of SDEs from noisy data. To demonstrate the practicality of our Wasserstein distance-based loss functions, we performed numerical experiments that demonstrate the efficiency of our method in reconstructing SDEs that arise across a number of applications.
Deep neural networks for texture classification

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik–Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks.
Integrality of Hecke eigenvalues and the growth of Hecke fields

We prove that Hecke eigenvalues for any Hilbert and Siegel modular forms are algebraic integers. Our method does not rely on cohomologicality nor Galois representations. We apply the integrality of Hecke eigenvalues for Hilbert modular forms of non-parallel weight to the estimation of the growth of Hecke fields of Hilbert cusp forms with non-vanishing central $L$-values. As a further application, we give the growth of the fields of rationality of cuspidal automorphic representations of ${\rm GL}_{2d}(\mathbb{A}_\mathbb{Q})$ for a prime number $d$ with non-vanishing central $L$-values. We also apply the integrality of Hecke eigenvalues for holomorphic Siegel cusp forms of general degree in order to give the growth of the Hecke fields of those forms.
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores

The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores. The following HARDCORE (H-field and power loss estimation for Arbitrary waveforms with Residual, Dilated convolutional neural networks in ferrite COREs) approach shows that a residual convolutional neural network with physics-informed extensions can serve this task efficiently when trained on observational data beforehand. One key solution element is an intermediate model layer which first reconstructs the bh curve and then estimates the power losses based on the curve's area rendering the proposed topology physically interpretable. In addition, emphasis was placed on expert-based feature engineering and information-rich inputs in order to enable a lean model architecture. A model is trained from scratch for each material, while the topology remains the same. A Pareto-style trade-off between model size and estimation accuracy is demonstrated, which yields an optimum at as low as 1755 parameters and down to below 8\,\% for the 95-th percentile of the relative error for the worst-case material with sufficient samples.
Data-driven compression of electron-phonon interactions

First-principles calculations of electron interactions in materials have seen rapid progress in recent years, with electron-phonon (e-ph) interactions being a prime example. However, these techniques use large matrices encoding the interactions on dense momentum grids, which reduces computational efficiency and obscures interpretability. For e-ph interactions, existing interpolation techniques leverage locality in real space, but the high dimensionality of the data remains a bottleneck to balance cost and accuracy. Here we show an efficient way to compress e-ph interactions based on singular value decomposition (SVD), a widely used matrix / image compression technique. Leveraging (un)constrained SVD methods, we accurately predict material properties related to e-ph interactions - including charge mobility, spin relaxation times, band renormalization, and superconducting critical temperature - while using only a small fraction (1-2%) of the interaction data. These findings unveil the hidden low-dimensional nature of e-ph interactions. Furthermore, they accelerate state-of-the-art first-principles e-ph calculations by about two orders of magnitudes without sacrificing accuracy. Our Pareto-optimal parametrization of e-ph interactions can be readily generalized to electron-electron and electron-defect interactions, as well as to other couplings, advancing quantitative studies of condensed matter.
Lightweight Self-Driven Deformable Organ Animations

The subject of simulating internal organs is a valuable and important topic of research to multiple fields from medical analysis to education and training. This paper presents a solution that utilizes a graphical technique in combination with a Stochastic method for tuning an active physics-based model. We generate responsive interactive organ animations with regional properties (i.e., areas of the model oscillating with different harmonic frequencies) to reproduce and capture real-world characteristics. Our method builds upon biological and physical discoveries to procedurally generate internally controlled rhythmic motions but also enable the solution to be interactive and adaptive. We briefly review deformation models for medical simulations and investigate the impediments to combining 'computergraphics' representations with biomechanical models. Finally, we present a lightweight solution that is scalable and able to procedurally generate large organ animations. In particular, simplified geometric representations of deformable structures that use periodic coupled forces to drive themselves.
The Significance of Data Abstraction Methods in Machine Learning Classification Processes for Critical Decision-Making

The applicability of widely adopted machine learning (ML) methods to classification is circumscribed by the imperatives of explicability and uncertainty, particularly evident in domains such as healthcare, behavioural sciences, and finances, wherein accountability assumes priority. Recently, Small and Incomplete Dataset Analyser (SaNDA) has been proposed to enhance the ability to perform classification in such domains, by developing a data abstraction protocol using a ROC curve-based method. This paper focuses on column-wise data transformations called abstractions, which are crucial for SaNDA's classification process and explores alternative abstractions protocols, such as constant binning and quantiles. The best-performing methods have been compared against Random Forest as a baseline for explainable methods. The results suggests that SaNDA can be a viable substitute for Random Forest when data is incomplete, even with minimal missing values. It consistently maintains high accuracy even when half of the dataset is missing, unlike Random Forest which experiences a significant decline in accuracy under similar conditions.