bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epidemiology of Congenital Heart Disease in Qatar: Preliminary findings from the Sidra Medicine Cardiac Registry
Authors: Almasri Muna , Al-Sulaiti Khalifa , Al Sayegh Dana , Sharma Omna , El Jerbi Aya , Al-Riyami Zakariya , Sarada Padma Kumari , Gupta Samir , Al-Saloos Hesham , Manyama Mange , Al-Shafai Kholoud N. 
Published: August 2023

AbstractBackgroundCongenital heart disease (CHD) is one of the most common congenital anomalies in newborns. The birth prevalence of CHD across the world is variable, reflecting differences in diagnostic capabilities as well as environmental and/or genetic risk factors between regions. Here, we aim to determine the epidemiology of CHD in Qatar in terms of the magnitude, subtypes, associated risk factors and complications.Methods and resultsData for 76 subjects with CHD were extracted from the Sidra cardiac registry database in October 2022. Extracted information for each subject included on age, nationality, gender, CHD diagnosis, outcomes of CHD, and comorbidities. Maternal history for some of the risk factors were also obtained from the registry. Statistical analysis was performed using Chi-Square tests and Fisher’s Exact tests.Most of the subjects were from the Middle East and North Africa (MENA) region (52.6%), followed by Southeast Asian (30.3%) and Qatari (14.5%). The three most common CHD types were ventricular septal defect (VSD), Atrial septal defect (ASD), and Transposition of the great arteries (TGA). About 32% subjects were born by parents who are first-degree cousins. This association was statistically significant (P<0.05). About 6.9% subjects in this cohort were born to mothers who had Pre-Gestational Diabetes Mellitus.ConclusionThe most common CHD type in this study was VSD followed by ASD. The rate of parental consanguinity was generally high among patients with CHDs. The pattern of non-genetic risk factors, comorbidities, and outcomes in patients with CHD was similar to those previously reported in other studies.Clinical perspectiveCongenital heart diseases and other birth defects are largely understudied in Qatar. This study provides epidemiological characteristics of patients with congenital heart diseases in Qatar.A significant number of subjects in this study had multiple CHD lesions.This study highlights the association between parental consanguinity and the increased incidence of congenital heart diseases in Qatar, a country with a high rate of parental consanguinity.Majority of the subjects in this study underwent surgical intervention, perhaps reflecting the complexity of the CHD lesions.
Cellular and molecular basis of leptin resistance
Authors: Tan Bowen , Hedbacker Kristina , Kelly Leah , Zhang Zhaoyue , Luo Ji-Dung , Rabinowitz Joshua D. , Friedman Jeffrey M. 
Published: August 2023

SUMMARYObese humans and diet induced obese mice (DIO) have high leptin levels and fail to respond to the exogenous hormone suggesting that obesity is caused by leptin resistance, the pathogenesis of which is unknown. We found that leptin treatment reduced plasma levels of mTOR ligands leading us to hypothesize that mTOR activation might inhibit leptin signaling. Rapamycin, an mTOR inhibitor, reduced fat mass and increased leptin sensitivity in DIO mice but not in mice with defects in leptin signaling. Rapamycin restored leptin’s actions on POMC neurons but failed to reduce the weight of mice with defects in melanocortin signaling. mTOR activation in POMC neurons caused leptin resistance while POMC specific mutations in mTOR activators decreased the weight gain of DIO mice. Thus increased mTOR activity in POMC neurons is necessary and sufficient for the development of leptin resistance in DIO mice establishing a key pathogenic mechanism leading to obesity.
Where’s Whaledo: A software toolkit for array localization of animal vocalizations
Authors: Snyder Eric R. , Solsona-Berga Alba , Baumann-Pickering Simone , Frasier Kait E. , Wiggins Sean M. , Hildebrand John A. 
Published: August 2023

AbstractWhere’s Whaledois a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to estimate population density, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers.Where’s Whaledocombines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness ofWhere’s Whaledoin localizing groups of Cuvier’s beaked whales (Ziphius cavirostris). We reconstruct the tracks of six individual animals vocalizing concurrently and identifyZiphius cavirostristracks despite being obscured by a large pod of vocalizing dolphins.Author summaryReconstructing the movement of animals from their vocalizations is a powerful method to observe their behavior in situations where visual monitoring is impractical. Arrays of acoustic recording devices can be used to determine the location of vocalizing animals and a series of locations can be linked to form tracks. However, reconstructing tracks requires methods of determining which animal in a group is vocalizing, finding the same vocalization on multiple recording devices, and determining the most likely location of the animal based on the relative times the sound arrived at various recording devices. We have developed a toolkit calledWhere’s Whaledoto assist researchers in reconstructing the behavior of these animals using arrays of acoustic recording devices. This toolkit greatly accelerates the process of reconstructing their tracks using a combination of automated processes and user interfaces. We useWhere’s Whaledoto reconstruct the tracks of deep-diving Cuvier’s beaked whales. We successfully reconstruct tracks of groups of up to six whales vocalizing concurrently.
An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning
Authors: Lejeune Laurent , Roussin Morgane , Leggio Bruno , Vernay Aurelia 
Published: August 2023

AbstractThe cell painting microscopy imaging protocol has recently gained traction in the biology community as it allows, through the addition of fluorescent dyes, to acquire images that highlight intra-cellular components that are not visible through traditional whole-cell microscopy. While previous works have successfully applied cell painting to mammalian cells, we devise a staining protocol applicable to a filamentous fungus model. Following a principled visual inspection and annotation protocol of phenotypes by domain-experts, we devise an efficient, robust, and conceptually simple image analysis strategy based on the Deep Cosine Metric Learning paradigm that allows to estimate phenotypical similarities across different imaging modalities. We experimentally demonstrate the benefits of our pipeline in the tasks of estimating dose-response curves over a wide range of subtle phenotypical variations. Last, we showcase how our learned metrics can group image samples according to different modes of action and biological targets in an interpretable manner.
Situating the oxytocin receptor gene polymorphisms in the context of structural and connectome-level substrates and association with endogenous oxytocin
Authors: Kaya Kutlu , Önal Deniz , Kartal Yasemin , Budak Murat Timur , Karabulut Erdem , Karlı Oğuz Kader , Pehlivanoğlu Bilge 
Published: August 2023

AbstractGenetic variants in the oxytocin receptor (OXTR) have been linked to individual differences in social behavior, while aberrant oxytocin regulation is associated with increased susceptibility to neuropsychiatric disorders. Recent magnetic resonance imaging (MRI) studies have demonstrated altered brain morphology and connectivity in response to OXTR single nucleotide polymorphisms (SNP) of GG homozygous compared to targeted allele carriers, such as T or A. However, the sex-specific differences in the structural and connectome-level substrates of OXTR genetic variants and their relationship with endogenous oxytocin remain poorly understood. Therefore, we aimed to decompose structural MRI and functional/structural connectivity to identify sex-specific differences among young adults through OXTR SNPs (rs53576, rs1042778, and rs2254298). High-resolution 3D T1-weighted, resting-state functional, and diffusion tensor images were acquired by sixty-one participants who provided blood samples for quantification of endogenous oxytocin concentrations and use for genotyping, followed by grouping with respect to homozygous and targeted allele carriers. We found that men had greater cortical surface area and sub(cortical) gray matter volume in different homozygous and targeted allele carriers. Resting-state functional and structural connectivity (rsFC and SC, respectively) were allocated differently, primarily in temporal and subcortical brain regions. There were also significant sex-specific differences in mean correlations between endogenous oxytocin and SC, whereas rsFC delineated more significant correlations on the node level. Our results provide valuable insights into sex-specific differences in the structural and connectome-level substrates of OXTR SNPs, contributing to understanding the role of oxytocin in socio-emotional processing and highlighting sex-specific differences in genetic and neural mechanisms underlying social behavior.
Dynamics of bacterial operons during genome-wide stresses
Authors: Jagadeesan Rahul , Dash Suchintak , Palma Cristina S.D. , Baptista Ines S.C. , Chauhan Vatsala , Mäkelä Jarno , Ribeiro Andre S. 
Published: August 2023

SUMMARYBacterial gene networks have many operons, each coordinating the kinetics of a few genes in time and strength. InEscherichia coli, nearly half of these operons have internal promoters, which allow regulating the downstream genes, independently from upstream genes. We studied their role during genome-wide stresses targeting a few key elements of the transcription machinery, namely, RNAP and gyrase. We show that absolute differences in response strength between genes in the same operon follow a sinusoidal function of the distance between them. This results from the combined effect of elongation fall-offs and internal promoters. The frequency of this function is regulated by the positioning of internal promoters, while its amplitude differs with the perturbation strength. Moreover, we find that positive supercoiling buildup partially explains the RNAP fall-off rates. Finally, we report similar sinusoidal patterns inE. colicells subject to several other stresses, as well as inBacillus subtilis, Corynebacterium glutamicum, and Helicobacter pylori. Overall, our results suggest that the strength, number, and positioning of internal promoters in operons are influenced by a genome-wide need to compensate for RNAP fall-offs, and thus provide distal genes in the operon with similar response strength as genes proximal to the primary promoter.
Patient-specific logical models replicate phenotype responses to psoriatic and anti-psoriatic stimuli
Authors: Tsirvouli Eirini , Aker Eir , Kuiper Martin 
Published: August 2023

AbstractPsoriasis is a dermatologic disease that affects 2% of the world population. Psoriasis is characterized by chronic inflammation and aberrant behavior of keratinocytes, which display increased levels of proliferation, and decreased differentiation and apoptosis. Stimulation of keratinocytes by psoriatic cytokines leads to the increased production of immunostimulatory ligands that further attract immune cells and amplify inflammatory responses. Psoriasis can have severe, moderate, or mild outcomes and while these severity levels demand custom medical treatment schemes, assigning an effective treatment to patients with moderate or severe disease is a demanding task.The varied responses of patients to treatments highlight a large disease complexity, demanding that new ways to analyze and integrate patients’ molecular profiles are developed to design patient-specific therapies. We have used gene expression values from psoriasis biopsies to separate patients into two clusters, each with distinct expression profiles, but nevertheless not correlating with any of the available clinical data, such as disease severity. When using these gene expression levels in logical model simulations these data became highly descriptive of patient-specific phenotype characteristics. Starting from a psoriatic keratinocyte model that we published recently, we added additional pathways highlighted by a differential gene expression analysis between the subgroups. This included components from the Interleukin-1 family, IFN-alpha/beta and IL-6 signaling pathways. Model personalization was performed by using patient gene expression levels in model configurations, exploiting the PROFILE pipeline. Personalized simulations revealed that the two patient clusters represent more innate immunity-driven, highly inflammatory phenotypes and adaptive immunity-driven, chronic phenotypes, respectively. The model was also able to finely capture differences between responses in patients with a known disease severity. A treatment response analysis among the patient cohort predicted differential responses to the inhibition of psoriatic stimuli, with IL-17, TNFα and PGE2 inhibition reducing proliferation and inflammatory phenotypes. Alternative treatment with PGE2 or TNFα inhibition instead of IL-17 was suggested for patients with high NF-κB activity and prosurvival factors, such as CREB1. With this project, we aim to highlight the value of combining omics data with logical modeling for the detection of ‘emergent’ phenotypes and for gaining disease knowledge on the individual patient level.
Numerical investigation of heterogeneous soft particle pairs in inertial microfluidics
Authors: Owen Benjamin , Thota Krishnaveni , Krüger Timm 
Published: August 2023

AbstractThe formation of pairs of particles or cells of different types in microfluidic channels can be desired or detrimental in healthcare applications. It is still unclear what role softness heterogeneity plays in the formation of these particle pairs. We use an in-house lattice-Boltzmann-immersed-boundary-finite-element solver to simulate a pair of particles with different softness flowing through a straight channel with a rectangular cross-section under initial conditions representative of a dilute suspension. We find that softness heterogeneity significantly affects the pair dynamics, determining whether a pair will form or not, and determining the lateral and inter-particle equilibrium behaviour in the pair. We also observe close matches between the transient deformation of particles in a linear pair and single particles in isolation. These results further our understanding of pair behaviour, providing a foundation for understanding particle train formation, and open up the potential to develop reduced-order models for particle pair formation based upon the behaviour of single particles.
Specialized structure of neural population codes in parietal cortex outputs
Authors: Safaai Houman , Wang Alice Y. , Kira Shinichiro , Malerba Simone Blanco , Panzeri Stefano , Harvey Christopher D. 
Published: August 2023

SummaryDo cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.
Rescue ofMycobacterium bovisDNA obtained from cultured strains during official surveillance of animal TB: key steps for robust whole genome sequence data generation
Authors: Pinto Daniela , Themudo Gonçalo , Pereira André C. , Botelho Ana , Cunha Mónica V. 
Published: August 2023

AbstractEpidemiological surveillance of animal tuberculosis (TB) based on whole genome sequencing (WGS) ofMycobacterium bovishas recently gained track due to its high resolution to identify infection sources, characterize the pathogen population structure, and for contact tracing. However, the workflow from bacterial isolation to sequence data analyses has several technical challenges that may severely impact the power to understand the epidemiological scenario and inform outbreak response.While trying to use archived DNA from cultured strains obtained during routine official surveillance of animal TB in Portugal, we struggled against three major challenges: the low amount ofM. bovisDNA obtained from routinely processed animal samples; the lack of purity ofM. bovisDNA,i.e.high levels of contamination with DNA from other organisms; and the co-occurrence of more than oneM. bovisstrain per sample (within-host mixed infection). The loss of an isolate’s genome is a missed link in transmission chain reconstruction, hampering the biological and epidemiological interpretation of data as a whole.Upon identification of these challenges, we implemented an integrated solution framework, based on whole genome amplification and a dedicated computational pipeline, to minimize their effects and recover as many genomes as possible. With the approaches described herein, we were able to recover 62 out of 100 samples that would have otherwise been lost. Based on these results, we discuss adjustments that should be made in official and research laboratories to facilitate sequential implementation of bacteriological culture, PCR, downstream genomics and computational-based methods. All of this in a time frame supporting data-driven intervention.
Efficient Sequence Embedding For SARS-CoV-2 Variants Classification
Authors: Ali Sarwan , Sardar Usama , Khan Imdad Ullah , Patterson Murray 
Published: August 2023

AbstractKernel-based methods, such as Support Vector Machines (SVM), have demonstrated their utility in various machine learning (ML) tasks, including sequence classification. However, these methods face two primary challenges:(i) the computational complexity associated with kernel computation, which involves an exponential time requirement for dot product calculation, and (ii) the scalability issue of storing the largen × nmatrix in memory when the number of data points(n) becomes too large. Although approximate methods can address the computational complexity problem, scalability remains a concern for conventional kernel methods. This paper presents a novel and efficient embedding method that overcomes both the computational and scalability challenges inherent in kernel methods. To address the computational challenge, our approach involves extracting thek-mers/nGrams (consecutive character substrings) from a given biological sequence, computing a sketch of the sequence, and performing dot product calculations using the sketch. By avoiding the need to compute the entire spectrum (frequency count) and operating with low-dimensional vectors (sketches) for sequences instead of the memory-intensiven × nmatrix or full-length spectrum, our method can be readily scaled to handle a large number of sequences, effectively resolving the scalability problem. Furthermore, conventional kernel methods often rely on limited algorithms (e.g., kernel SVM) for underlying ML tasks. In contrast, our proposed fast and alignment-free spectrum method can serve as input for various distance-based (e.g.,k-nearest neighbors) and non-distance-based (e.g., decision tree) ML methods used in classification and clustering tasks. We achieve superior prediction for coronavirus spike/Peplomer using our method on real biological sequences excluding full genomes. Moreover, our proposed method outperforms several state-of-the-art embedding and kernel methods in terms of both predictive performance and computational runtime.
Benchmarking AI-based plasmid annotation tools for antibiotic resistance genes mining from metagenome of the Virilla River, Costa Rica
Authors: Rojas-Villalta Dorian , Calderón-Osorno Melany , Barrantes-Jiménez Kenia , Arias-Andres Maria , Rojas-Jiménez Keilor 
Published: August 2023

AbstractBioinformatics and Artificial Intelligence (AI) stand as rapidly evolving tools that have facilitated the annotation of mobile genetic elements (MGEs), enabling the prediction of health risk factors in polluted environments, such as antibiotic resistance genes (ARGs). This study aims to assess the performance of four AI-based plasmid annotation tools (Plasflow, Platon, RFPlasmid, and PlasForest) by employing defined performance parameters for the identification of ARGs in the metagenome of one sediment sample obtained from the Virilla River, Costa Rica. We extracted and sequenced complete DNA from the sample, assembled the metagenome, and then performed the plasmid prediction with each bioinformatic tool, and the ARGs annotation using the Resistance Gene Identifier web portal. Sensitivity, specificity, precision, negative predictive value, accuracy, and F1score were calculated for each ARGs prediction result of the evaluated plasmidomes. Notably, Platon emerged as the highest performer among the assessed tools, exhibiting exceptional scores. Conversely, Plasflow seems to face difficulties distinguishing between chromosomal and plasmid sequences, while PlasForest has encountered limitations when handling small contigs. RFPlasmid displayed diminished specificity and was outperformed by its taxon-dependent workflow. We recommend the adoption of Platon as the preferred bioinformatic tool for resistome investigations in the taxon-independent environmental metagenomic domain. Meanwhile, RFPlasmid presents a compelling choice for taxon-dependent prediction due to its exclusive incorporation of this approach. We expect that the results of this study serve as a guiding resource in selecting AI-based tools for accurately predicting the plasmidome and its associated genes.