bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning-based detection of murine congenital heart defects from µCT scans
Authors: Nguyen Hoa , Desgrange Audrey , Ochandorena-Saa Amaia , Benhamo Vanessa , Meilhac Sigolène M. , Zimmer Christophe 
Published: April 2024

AbstractCongenital heart defects (CHD) result in high morbidity and mortality rates, but their origins are poorly understood. Mouse models of heart morphogenesis are required to study the pathological mechanisms of heart development compared to normal. In mouse fetuses, CHD can be observed and detected in 3D images obtained by thoracic micro-computed tomography (μCT). However, diagnosis of CHD from μCT scans is a time-consuming process that requires the experience of senior experts. An automated alternative would thus save time, empower less experienced investigators and could broaden analysis to larger numbers of samples.Here, we describe and validate an approach based on deep learning to automatically segment the heart and screen normal from malformed hearts in mouse μCT scans. In an initial cohort, we collected 139 μCT scans from thorax and abdomen of control and mutant perinatal mice. We trained a self-configurating neural network (nnU-Net) to segment hearts from body μCT scans and validated its performance on expert segmentations, achieving a Dice coefficient of 96%. To identify malformed hearts, we developed and trained a 3D convolutional neural network (CNN) that uses segmented μCT scans as inputs. Despite the relatively small training data size, our diagnosis model achieved a sensitivity, specificity (for a 0.5 threshold), and area under the curve (AUC) of 92%, 96%, and 97% respectively, as determined by 5-fold cross-validation.As further validation, we analyzed two additional cohorts that were collected after the model was trained: a ‘prospective’ cohort, using the same experimental protocol as the initial cohort, and containing a subset of its genotypes, and a ‘divergent’ cohort in which mice were subjected to a different treatment for heart arrest (cardioplegia) and that contained a new mouse line. Performance on the prospective cohort was excellent, with a sensitivity of 92%, a specificity of 100%, and an AUC of 100%. Performance on the divergent cohort was moderate (sensitivity: 69%, specificity: 80% and AUC: 81%), but was much improved when the model was finetuned on (a subset of) the cohort (sensitivity: 79%, specificity: 88% and AUC: 91%). These results showcase our model’s robustness and adaptability to technical and biological differences in the data, highlighting its usefulness for practical applications.In order to facilitate the adoption, adaptation and further improvement of these methods, we built a user-friendlyNapari plugin(available atnapari-hub.org/plugins/mousechd-napari) that allows users without programming skills to utilize the segmentation and diagnosis models and re-train the latter on their own data and resources. The plugin also highlights the cardiac regions used for the diagnosis. Our automatic and retrainable pipeline, which can be employed in high-throughput genetic screening, will accelerate diagnosis of heart anomalies in mice and facilitate studies of the mechanisms of CHD.
Variational calculus approach to Zernike polynomials with application to fluorescence correlation spectroscopy
Authors: Gligonov Ivan , Enderlein Jörg 
Published: April 2024

ABSTRACTZernike polynomials are a sequence of orthogonal polynomials that play a crucial role in optics and in particular in modeling microscopy systems. Introduced by Frits Zernike in 1934, they are particularly useful in expressing wavefront aberrations and thus imperfections of imaging systems. However, their origin and properties are rarely discussed and proven. Here, we present a novel approach to Zernike polynomials using variational calculus, and apply them to describe aberrations in fluorescence microscopy. In particular, we model the impact of various optical aberrations on the performance of one-photon and two-photon excitation fluorescence microscopy.SIGNIFICANCEThis manuscript explores the mathematical derivation of Zernike polynomials and highlights their critical role in describing optical wavefronts and aberrations, particularly in the domain of optical microscopy. Special emphasis is placed on their utility in simulating the effects of aberrations on one-photon and two-photon excitation fluorescence correlation spectroscopy.
Positive Selection on Rare Variants Underlying the Cold Adaptation of Wild Boar
Authors: Chen Jianhai , Jakovlić Ivan , Sablin Mikhail , Xia Shengqian , Xu Zhixiang , Guo Yapin , Kuang Renzuo , Zhong Jie , Jia Yangying , Tran Thi Thuy Nhien , Yang Hao , Ma Hong , Šprem Nikica , Han Jianlin , Liu Di , Zhao Yunxia , Zhao Shuhong 
Published: April 2024

AbstractThe wide geographical distribution of Eurasian wild boar (Sus scrofa) offers a natural experiment to study the thermoregulation. Here, we conducted whole-genome resequencing and chromatin profiling experiments on the local populations from cold regions (northern and northeastern Asia) and warm regions (southeastern Asia and southern China). Using genome-wide scans of four methods, we detected candidate genes underlying cold-adaptation with significant enrichment of pathways related to thermogenesis, fat cell development, and adipose tissue regulation. We also found two enhancer variants under positive selection, an intronic variant ofIGF1R(rs341219502) and an exonic variant ofBRD4(rs327139795), which showed the highest differentiation between cold and warm region populations of wild boar and domestic pigs. Moreover, these rare variants were absent in outgroup species and warm-region wild boar but nearly fixed in cold-region populations, suggesting theirde novoorigins in cold-region populations. The experiments of CUT&Tag chromatin profiling showed that rs341219502 ofIGF1Ris associated with the gain of three novel transcription factors involving regulatory changes in enhancer function, while rs327139795 ofBRD4could result in the loss of a phosphorylation site due to amino acid alteration. We also found three genes (SLCO1C1, PDE3A,and TTC28) with selection signals in both wild boar and native human populations from Siberia, which suggests convergent molecular adaptation in mammals. Our study shows the adaptive evolution of genomic molecules underlying the remarkable environmental flexibility of wild boar.
Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution
Authors: Shen Wenjun , Liu Cheng , Hu Yunfei , Lei Yuanfang , Wong Hau-San , Wu Si , Zhou Xin Maizie 
Published: April 2024

AbstractA main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.
Forebrain neuronal SMC3 regulates body weight and metabolic health partially through regulation of hypothalamic Melanocortin 4 receptor
Authors: Saleev Natalia , Getselter Dmitriy , Bartman Joanna , Gutman Roee , Marco Asaf , Elliott Evan 
Published: April 2024

AbstractSMC3 is a major component of the cohesin complex that regulates higher-order chromatin organization and gene expression. Mutations in SMC3 gene are found in patients with Cornelia de Lange syndrome (CdLs). This syndrome is characterized by intellectual disabilities, behavioral patterns as self-injury, as well as metabolic dysregulation. Nonetheless, little is known about the role of neuronal SMC3 in gene expression and physiology in adulthood. This study determined the role of SMC3 in adulthood brain, by knocking out Smc3 specifically in adulthood forebrain excitatory neurons. Excitatory conditional neuron-specific SMC3 knockout (cKO) mice displayed a very strong metabolic phenotype in both male and female mice, including a robust overweight phenotype, loss of muscle mass, increased food consumption, lower respiratory exchange ratio, lower energy expenditure and hormonal changes. The hypothalamus displayed dysregulated neuronal morphology and associated transcriptional abnormalities in RNA-seq analysis across various cellular pathways, including decrease of Melanocortin 4 receptor (Mc4r) expression level, a pivotal regulator of appetite and metabolism. Correspondingly, ChIP-seq analysis revealed genome-wide alterations in the binding dynamics of SMC3 of the cKO animals, including Mc4r associated regions. Notably, a significant correlation emerged between multiple sites exhibiting a marked decrease in binding and downregulated genes. The administration of Setmelanotide, an MC4r agonist, to cKO group resulted in a notable reduction in both weight and food consumption in these mice. Therefore, we have identified specific and reversable metabolic parameters that are regulated by neuronal Smc3 in adulthood.
Plasma-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems
Authors: Goeminne Ludger J.E. , Eames Alec , Tyshkovskiy Alexander , Argentieri M. Austin , Ying Kejun , Moqri Mahdi , Gladyshev Vadim N. 
Published: April 2024

AbstractAging is a complex process manifesting at the molecular, cell, organ and organismal levels. It leads to functional decline, disease and ultimately death, but the relationship between these fundamental biomedical features remains elusive. By applying machine learning to plasma proteome data of over fifty thousand human subjects in the UK Biobank and other cohorts, we report organ-specific and conventional aging models trained on chronological age, mortality and longitudinal proteome data. We show how these tools predict organ/systems-specific disease through numerous phenotypes. We find that men are biologically older and age faster than women, that accelerated aging of organs leads to diseases in these organs, and that specific diets, lifestyles, professions and medications are associated with accelerated and decelerated aging of specific organs and systems. Altogether, our analyses reveal that age-related chronic diseases epitomize accelerated organ- and system-specific aging, modifiable through environmental factors, advocating for both universal whole-organism and personalized organ/system-specific anti-aging interventions.
Adherence to a healthful plant-based diet and risk of mortality among individuals with chronic kidney disease: A prospective cohort study
Authors: Thompson Alysha S. , Gaggl Martina , Bondonno Nicola P. , Jennings Amy , O’Neill Joshua K. , Hill Claire , Karavasiloglou Nena , Rohrmann Sabine , Cassidy Aedín , Kühn Tilman 
Published: April 2024

AbstractBackgroundPlant-rich dietary patterns may protect against negative health outcomes among individuals with chronic kidney disease (CKD), although aspects of plant-based diet quality have not been studied. This study aimed to examine associations between healthful and unhealthful plant-based dietary patterns with risk of mortality among CKD patients for the first time.MethodsThis prospective analysis included 4,807 UK Biobank participants with CKD at baseline. We examined associations of adherence to both the healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI), calculated from repeated 24-hour dietary assessments, with risk of mortality using multivariable Cox proportional hazard regression models.ResultsOver a 10-year follow-up, 675 deaths were recorded. Participants with the highest hPDI scores had a 33% lower risk of mortality [HRQ4vsQ1(95% CI): 0.67 (0.53-0.84), ptrend= <0.001], while those with the highest uPDI scores had a 49% higher risk [1.49 (1.18-1.89), ptrend= 0.004], compared to participants with the lowest respective scores and following adjustment for other dietary and lifestyle factors. In food group-specific analyses, higher wholegrain intakes were associated with a 29% lower mortality risk, while intakes of refined grains, and sugar-sweetened beverages were associated a 28% and 31% higher risk, respectively.ConclusionsIn CKD patients, a higher intake of healthy plant-based foods was associated with a lower risk of mortality, while a higher intake of unhealthy plant-based foods was associated with a higher risk. These results underscore the importance of plant food quality and support the potential role of healthy plant food consumption in the treatment and management of CKD to mitigate unfavourable outcomes.
A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase
Authors: Giglio Ross M. , Hou Nicholas , Wyatt Adeya , Hong Justin , Shi Lingting , Vaikunthan Mathini , Fuchs Henry , Nima Jose Pomarino , Malinowski Seth W. , Ligon Keith L. , McFaline-Figueroa José R. , Yosef Nir , Azizi Elham , McFaline-Figueroa José L. 
Published: April 2024

AbstractOver-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.One Sentence SummaryDeep chemical genomic profiling reveals heterogeneity in response to the targeting of EGFR via myriad chemical means.
Computationally-informed insights into anhedonia and treatment byκ-opioid receptor antagonism
Authors: Bari Bilal A. , Krystal Andrew D. , Pizzagalli Diego A. , Gershman Samuel J. 
Published: April 2024

AbstractAnhedonia, the loss of pleasure, is prevalent and impairing. Parsing its computational basis promises to explain its transdiagnostic character. We argue that one manifestation of anhedonia— reward insensitivity—may be linked to limited memory capacity. Further, the need to economize on limited capacity engenders a perseverative bias towards frequently chosen actions. Anhedonia may also be linked with deviations from optimal perseveration for a given memory capacity, a pattern that causesinefficiencybecause it results in less reward for the same memory cost. To test these hypotheses, we perform secondary analysis of a randomized controlled trial testingκ-opioid receptor (KOR) antagonism for anhedonia, as well as analyses of three other datasets. We find that anhedonia is associated with deficits in efficiency but not memory, whereas KOR antagonism (which likely elevates tonic dopamine) increases memory and efficiency. KOR antagonism therefore has distinct cognitive effects, only one related to anhedonia.
Natural language processing charts transcriptome evolution to design combination cancer therapies
Authors: Jassim Amir , Nimmervoll Birgit V. , Terranova Sabrina , Nathan Erica , Masih Katherine E. , Ruff Lisa , Duarte Matilde , Cooper Elizabeth , Hu Linda P. , Katyal Gunjan , Akhbari Melika , Gilbertson Reuben J. , Terhune Colton , Balmus Gabriel , Jackson Stephen P. , Filbin Mariella G. , Hill Anthony , Patrizi Anarita , Dani Neil , Regev Aviv , Lehtinen Maria K. , Gilbertson Richard J. 
Published: April 2024

ABSTRACTCombination treatment–the mainstay of cancer therapy–often fails because treatment interactions evoke complex resistance mechanisms that are hard to predict. Designing combination therapy to prevent treatment resistance is especially challenging for rare cancers. Here, we introduce REsistance through COntext DRift (RECODR): a computational pipeline that tracks how genes change their transcriptome context across cancer development and drug treatment conditions. By applying RECODR to a genetically modified mouse model of choroid plexus carcinoma–a rare brain tumour of young children–we identified patterns of transcriptome evolution, cellular heterogeneity and treatment targets that emerged as tumours were initiated and resisted combination treatment. This enabled the prediction of treatment resistance mechanisms and the design of highly effective therapeutic protocols that avoided treatment failure. RECODR can describe complex and dynamic changes in normal and diseased tissues that could be applied to multimodal data from a variety of settings, mitigating treatment resistance across cancers and other diseases.
Terrestrial 3D Laser Scanning for Ecosystem and Fire Effects Monitoring
Authors: Murphy Mary Carlton , Loudermilk E. Louise , Pokswinski Scott , Williams Brett , Link Emily , Lienesch Laila , Douglas Leta , Skowronski Nicholas , Gallagher Michael , Maxwell Aaron , Snitker Grant , Hawley Christie , Wallace Derek , Payne Irenee , Yurkiewicz Tim , Sánchez Meador Andrew J. , Anderson Chad , Jackson J. Mark , Parsons Russell , Floca Melissa , Nealey Isaac , Altintas Ilkay , Hiers J. Kevin , Wallace Jon 
Published: April 2024

ABSTRACTLong-term terrestrial ecosystem monitoring is a critical component of documenting outcomes of land management actions, assessing progress towards management objectives, and guiding realistic long-term ecological goals, all through repeated observation and measurement. Traditional monitoring methods have evolved for specific applications in forestry, ecology, and fire and fuels management. While successful monitoring programs have clear goals, trained expertise, and rigorous sampling protocols, new advances in technology and data management can help overcome the most common pitfalls in data quality and repeatability. This paper presents Terrestrial Laser Scanning (TLS), a specific form of LiDAR (Light Detection and Ranging), as an emerging sampling method that can complement and enhance existing monitoring methods. TLS captures in high resolution the 3D structure of a terrestrial ecosystem (forest, grassland, etc.), and is increasingly efficient and affordable (<$30,000). Integrating TLS into ecosystem monitoring can standardize data collection, improve efficiency, and reduce bias and error. Streamlined data processing pipelines can rigorously analyze TLS data and incorporate constant improvements to inform management decisions and planning. The approach described in this paper utilizes portable, push-button TLS equipment that, when calibrated with initial transect sampling, captures detailed forestry, fuels, and ecological features in less than 5 minutes per plot. We also introduce an interagency automated processing pipeline and dashboard viewer for instant, user-friendly analysis, and data retrieval of hundreds of metrics. Forest metrics and inventories produced with these methods offer effective decision-support data for managers to quantify landscape-scale conditions and respond with efficient action. This protocol further supports interagency compatibility for efficient natural resource monitoring across jurisdictional boundaries with uniform data, language, methods, and data analysis. With continued improvement of scanner capabilities and affordability, these data will shape the future of terrestrial ecosystem monitoring as an important means to address the increasingly fast pace of ecological change facing natural resource managers.
Analysis of endogenous NOTCH1 fromPOFUT1 S162Lpatient fibroblasts reveals the importance of theO-fucose modification on EGF12 in human development
Authors: Matsumoto Kenjiroo , Luther Kelvin B. , Haltiwanger Robert S. 
Published: April 2024

AbstractNOTCH1 (N1) is a transmembrane receptor interacting with membrane-tethered ligands on opposing cells that mediate the direct cell-cell interaction necessary for many cell fate decisions. ProteinO-fucosyltransferase 1 (POFUT1) addsO-fucose to Epidermal Growth Factor (EGF)-like repeats in the NOTCH1 extracellular domain, which is required for trafficking and signaling activation. We previously showed thatPOFUT1 S162Lcaused a 90% loss of POFUT1 activity and global developmental defects in a patient; however, the mechanism by which POFUT1 contributes to these symptoms is still unclear. Compared to controls,POFUT1 S162Lpatient fibroblast cells had an equivalent amount of N1 on the cell surface but showed a 60% reduction of DLL1 ligand binding and a 70% reduction in JAG1 ligand binding. To determine if the reduction ofO-fucose on N1 inPOFUT1 S162Lpatient fibroblasts was the cause of these effects, we immunopurified endogenous N1 from control and patient fibroblasts and analyzedO-fucosylation using mass spectral glycoproteomics methods. N1 EGF8 to EGF12 comprise the ligand binding domain, andO-fucose on EGF8 and EGF12 physically interact with ligands to enhance affinity. Glycoproteomics of N1 fromPOFUT1 S162Lpatient fibroblasts showed WT fucosylation levels at all sites analyzed except for a large decrease at EGF9 and the complete absence ofO-fucose at EGF12. Since the loss ofO-fucose on EGF12 is known to have significant effects on N1 activity, this may explain the symptoms observed in thePOFUT1 S162Lpatient.