Miracle-dz/UniFAR-FTUs huggingface dataset:
Different coexistence patterns between apex carnivores and mesocarnivores based on temporal, spatial, and dietary niche partitioning analysis in Qilian Mountain National Park, China

Carnivores play key roles in maintaining ecosystem structure and function as well as ecological processes, understanding how sympatric species coexistence mechanism in natural ecosystems is a central research topic in community ecology and biodiversity conservation. In this study, we explored intra- and interspecific niche partitioning along spatial, temporal, and dietary niche partitioning between apex carnivores (wolf Canis lupus, snow leopard Panthera uncia, Eurasian lynx Lynx lynx) and mesocarnivores (Pallas’s cat Otocolobus manul, red fox Vulpes vulpes, Tibetan fox Vulpes ferrilata) in the Qilian Mountain national park of China using camera trapping data and DNA metabarcoding sequencing data. Our study showed that apex carnivore species had more overlap temporally (the value of time overlap from 0.661 to 0.900) or trophically (the value of diet overlap from 0.458 to 0.892), mesocarnivore species had high dietary overlap with each other (the value of diet overlap from 0.945 to 0.997), and apex carnivore and mesocarnivore species had high temporal overlap (the value of time overlap from 0.497 to 0.855). Large dietary overlap was observed between wolf and snow leopard (Pianka’s index=0.892) and Pallas’s cat and Tibetan fox (Pianka’s index=0.997) and suggesting increased resource competition for these pair species were existed. We conclude that spatial niche partitioning playing a key role in facilitating the coexistence of apex carnivore species, spatial and temporal niche partitioning facilitate the coexistence of mesocarnivore species, and spatial and dietary niche partitioning facilitate the coexistence between apex and mesocarnivore species. Our findings address, for the first time, niche partitioning was considered across temporal, spatial and dietary dimensions and diverse coexistence patterns of carnivore species were presented in the Qilian Mountain national park of China. These finding will contribute substantially to current understanding of carnivore guilds and effective conservation management in fragile alpine ecosystems.
Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltagegated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations—molecular fingerprints, descriptors, and graph-based numerical representations— are rigorously benchmarked. Additionally, a novel training and evaluation dataset framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated dataset covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.
Population coding of time-varying sounds in the non-lemniscal Inferior Colliculus

The inferior colliculus (IC) of the midbrain is important for complex sound processing, such as discriminating conspecific vocalizations and human speech. The IC’s non-lemniscal, dorsal “shell” region is likely important for this process, as neurons in these layers project to higher-order thalamic nuclei that subsequently funnel acoustic signals to the amygdala and non-primary auditory cortices; forebrain circuits important for vocalization coding in a variety of mammals, including humans. However, the extent to which shell IC neurons transmit acoustic features necessary to discern vocalizations is less clear, owing to the technical difficulty of recording from neurons in the IC’s superficial layers via traditional approaches. Here we use 2-photon Ca2+ imaging in mice of either sex to test how shell IC neuron populations encode the rate and depth of amplitude modulation, important sound cues for speech perception. Most shell IC neurons were broadly tuned, with a low neurometric discrimination of amplitude modulation rate; only a subset were highly selective to specific modulation rates. Nevertheless, neural network classifier trained on fluorescence data from shell IC neuron populations accurately classified amplitude modulation rate, and decoding accuracy was only marginally reduced when highly tuned neurons were omitted from training data. Rather, classifier accuracy increased monotonically with the modulation depth of the training data, such that classifiers trained on full-depth modulated sounds had median decoding errors of ∼0.2 octaves. Thus, shell IC neurons may transmit time-varying signals via a population code, with perhaps limited reliance on the discriminative capacity of any individual neuron. Significance Statement The IC’s shell layers originate a “non-lemniscal” pathway whose first- and second-order targets are thought important for perceiving conspecific vocalizations and human speech. However, prior studies suggest that individual shell IC neurons are broadly tuned and have high response thresholds, implying a limited reliability of efferent signals. Here we use Ca2+ imaging to test how shell IC neurons encode amplitude modulation, a key sound cue for speech perception and stream segregation. We show that the rate and depth of amplitude modulation is accurately represented in the ensemble activity of shell IC neuron populations. Thus, downstream targets can read out a sound’s temporal envelope from a distributed rate code transmitted by populations of broadly tuned neurons.
Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Young’s modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.
Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.
Experimental drugs for the prevention or treatment of sensorineural hearing loss

ABSTRACT Introduction Sensorineural hearing loss results in irreversible loss of inner ear hair cells and spiral ganglion neurons. Reduced sound detection and speech discrimination can span all ages, and sensorineural hearing rehabilitation is limited to amplification with hearing aids or cochlear implants. Recent insights into experimental drug treatments for inner ear regeneration and otoprotection have paved the way for clinical trials in order to restore a more physiological hearing experience. Paired with the development of innovative minimally invasive approaches for drug delivery to the inner ear, new, emerging treatments for hearing protection and restoration are within reach. Areas covered This expert opinion provides an overview of the latest experimental drug therapies to protect from and to restore sensorineural hearing loss. Expert opinion The degree and type of cellular damage to the cochlea, the responsiveness of remaining, endogenous cells to regenerative treatments, and the duration of drug availability within cochlear fluids will determine the success of hearing protection or restoration.
Hypoxia-inducible-factors differentially contribute to clinical disease and the control of viral replication during RSV infection

Hypoxia-inducible-factors (HIF) are transcription factors that regulate cellular adaptation to hypoxic conditions, enabling cells to survive in low-oxygen environments. Viruses have evolved to stabilize this pathway to promote successful viral infection, therefore modulation of HIFs could represent a novel antiviral strategy. In previous in vitro studies, we found that respiratory syncytial virus (RSV), a leading cause of respiratory illness, stabilizes HIFs under normoxic conditions, with inhibition of HIF-1α resulting in reduced viral replication. Despite several HIF modulating compounds being tested/approved for use in other non-infectious models, little is known about their efficacy against respiratory viruses using relevant animal models. This study aimed to characterize the disease modulating properties and antiviral potential of anti-HIF-1α (PX478) and anti-HIF-2α (PT2385) in RSV-infected BALB/c mice. We found that inhibition of HIF-1α worsen clinical disease parameters, while simultaneously improving airway function. Additionally, anti-HIF-1α results in significantly reduced viral titer at early and peak time points of RSV replication, followed by a loss in viral clearance when given every day, but not every-other-day. In contrast, inhibition of HIF-2α was associated with improved clinical parameters, with no changes in airway function, and amelioration of interstitial pneumonia. Furthermore, anti-HIF-2α reduced early and peak lung viral replication, with no impairment of viral clearance. Analysis of lung cells found significant modification in the T cell compartment that correlated with changes in lung pathology and viral titers in response to each HIF inhibitor administration. These data underscore the complex role of HIFs in RSV infection and highlight the need for careful therapeutic consideration.
First spectroscopic observations of the galaxies that reionized the Universe

Low-mass galaxies in the early universe are believed to be the building blocks of present-day galaxies. These fledgling systems likely played a pivotal role in cosmic reionization, a major phase transition from neutral Hydrogen to ionized plasma around 600-800 Myr after the Big Bang. However, these galaxies have eluded comprehensive spectroscopic studies owing to their extreme faintness. Here we report the first spectroscopic analysis of 8 ultra-faint galaxies during the epoch of reionization with absolute magnitudes between M$_{\rm UV}$ $\sim -17$ to $-15$ mag (down to 0.005 $L^{\star}$). The combination of ultra-deep NIRSpec (Near-Infrared Spectrograph) observations and the strong gravitational lensing boost of Abell~2744 allow us to derive the first spectroscopic constraints on the prevalence of faint galaxies and their ionizing properties during the Universe's first billion years. We find that faint galaxies are prodigious producers of ionizing photons with log($\xi_{\rm ion}$/ Hz erg$^{-1}$) =$25.8\pm 0.05$, a factor of 4 larger than canonical values. This means that the total rate of ionizing photons produced by galaxies exceeds that needed for reionization, even for modest values of escape fraction ($f_{\rm esc}$ =5%). These findings provide robust evidence that faint galaxies were the main drivers of cosmic reionization at $z\sim7$.
PEvoLM: Protein Sequence Evolutionary Information Language Model

With the exponential increase of the protein sequence databases over time, multiple-sequence alignment (MSA) methods, like PSI-BLAST, perform exhaustive and time-consuming database search to retrieve evolutionary information. The resulting position-specific scoring matrices (PSSMs) of such search engines represent a crucial input to many machine learning (ML) models in the field of bioinformatics and computational biology. A protein sequence is a collection of contiguous tokens or characters called amino acids (AAs). The analogy to natural language allowed us to exploit the recent advancements in the field of Natural Language Processing (NLP) and therefore transfer NLP state-of-the-art algorithms to bioinformatics. This research presents an Embedding Language Model (ELMo), converting a protein sequence to a numerical vector representation. While the original ELMo trained a 2-layer bidirectional Long Short-Term Memory (LSTMs) network following a two-path architecture, one for the forward and the second for the backward pass, by merging the idea of PSSMs with the concept of transfer-learning, this work introduces a novel bidirectional language model (bi-LM) with four times less free parameters and using rather a single path for both passes. The model was trained not only on predicting the next AA but also on the probability distribution of the next AA derived from similar, yet different sequences as summarized in a PSSM, simultaneously for multitask learning, hence learning evolutionary information of protein sequences as well. The network architecture and the pre-trained model are made available as open source under the permissive MIT license on GitHub at https://github.com/issararab/PEvoLM.
Climate and Weather

The climate of the area where your property is located and the annual fluctuations you experience in weather conditions can affect how much energy you need to operate your buildings. Portfolio Manager includes metrics designed to incorporate these effects and help you understand your energy performance. These procedures rely on temperature data collected by weather monitoring stations and published by the National Climatic Data Center (NCDC), which is part of the National Oceanic and Atmospheric Administration (NOAA). To discuss how we use this data, it is helpful to introduce two concepts:
Eris: Measuring discord among multidimensional data sources

Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and record merging. To solve the latter, it is mostly assumed that ground truth can be determined, either as master data or from user feedback. However, in many cases, this is not the case because firstly the merging processes cannot be accurate enough, and also the data gathering processes in the different sources are simply imperfect and cannot provide high quality data. Instead of enforcing consistency, we propose to evaluate how concordant or discordant sources are as a measure of trustworthiness (the more discordant are the sources, the less we can trust their data). Thus, we define the discord measurement problem in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different data (for example, cases and deaths), we wish to assess whether the different sources are concordant, or if not, measure how discordant they are. We also define a set of algebraic operators to describe the alignments, together with two alternative relational implementations that reduce the problem to linear or quadratic programming. These are evaluated against both COVID-19 and synthetic data, and our experimental results show that discordancy measurement can be performed efficiently in realistic situations.
Computational and Structural Biotechnology Journal

predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artiﬁcial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by blockchain to obtain a cohesive view of the risk factors data from patients across diﬀerent hospitals and ensure security and privacy of the user’s data. We provide a comparative analysis of diﬀerent medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out within our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system predicts diabetes using RF with 4.57% more accuracy on average in comparison with the other models LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show signiﬁcant improvement. When using feature selection, the performance is improved by 1.14% for PIMA Indian and 0.02% for Sylhet datasets, while it is reduced by 0.89% for MIMIC III.
Knowledge, Practices, and Factors Affecting use of Condoms among the Youths in Lira District in HIV Prevention

As Uganda continues with the fight to end HIV epidemic by 2030, increasing condom use, especially in high-risk sexual encounters is one of the key strategies for reducing HIV transmission in Uganda. This study aimed to assess the knowledge, practices and factors affecting the utilization of condoms in the prevention of HIV among youths in Lira District. The study adopted a cross-sectional study design employing quantitative methods. A total of 189 respondents aged 15-35 years were recruited using a systematic random sampling technique. Data was obtained using an interviewer administered questionnaire and analysed using SPSS version 2.0. Descriptive analysis was used to generate frequencies and percentages. The measure and significance of association were determined using a Chisquare test and odds ratios with a P-value set at 0.05 corresponding to 95% confidence interval. Linear logistic regression analysis was used to generate Crude and Adjusted odds ratios to determine the factors associated with condom use among youths in HIV prevention. The results showed that 48% had ever used condoms. Majority had knowledge of condom use. Over 120(63.49%) were very knowledgeable, 28(14.82%) moderately knowledgeable while the rest 41(21.69%) were less knowledgeable. Likewise, over three quarters 71(78.02%) reported good condom use practices. Marital status (p=0.002), education (p=0.012), relationship status (p=0.019), number of sexual partners (p=0.011) and knowledge about condom use (p=0.018) were significant predictors of condom use in HIV prevention. Only marital status was independently associated with condom use after adjusting for possible cofounders. Although the study reveals relatively good knowledge and condom use practices, there is still some level of less knowledge and poor practices which may be driving the high prevalence of not using condoms among youths in HIV prevention. Reproductive health education programs targeting married, less educated, and youths in more than a single relationship should be tightened so as to increase condom use knowledge and eventually utilization in the fight against HIV. Keywords: Condom, HIV epidemic and sexually transmitted infections (STIs).
A CLINICO-BACTERIOLOGICAL PROFILE OF CHRONIC DACRYOCYSTITIS IN ADULT PATIENTS - A STUDY IN KHAJA BANDA NAWAZ TEACHING AND GENERAL HOSPITAL, KALABURAGI NORTH KARNATAKA

Background: To study chronic dacryocystitis in adults with respect to age, gender, laterality, symptoms and signs. To isolate the current spectrum of aerobic bacterial pathogens causing chronic dacryocystitis and their antibiotic sensitivity patterns. Materials and Methods: A prospective study of 50 cases of chronic dacryocystitis were studied in Khaja Banda Nawaz Teaching And General Hospital, Kalaburagi for which Gram’s staining and aerobic culture and sensitivity done on lacrimal regurgitate. Samples which was collected either by Regurgitation On Pressure over the Lacrimal Sac (ROPLAS) test. Or By irrigating the lacrimal drainage system with sterile saline and collecting the sample from the refluxing material. Result: Incidence of chronic dacryocystitis is more in middle ages 41-50 yrs (28%) & in males with sex ratio F: M = 2.84: 1. The disease is unilateral in 41 cases. Most common chief complaint being watering and discharge in 26 cases (52%). Positive culture is seen in 42 (84%) cases, while 8 (16%) cases showed no growth. Gram positive organisms were isolated in 27 (54%) cases and gram negative organism in 13(26%) cases. Gram positive organisms, Staph Aureus and CONS accounted for 22% each which were sensitive to Amoxicillin/ Clavulinic acid. Among Gram negative organisms Klebsiella in 10% cases sensitive