bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of inflammatory biomarkers in IgA nephropathy using the NanoString technology: a validation study
Authors: Gaumond Laurence , Lamarche Caroline , Beauchemin Stéphanie , Henley Nathalie , Elftouh Naoual , Gerarduzzi Casimiro , Laurin Louis-Philippe 
Published: September 2023

ABSTRACTObjective and designImmunoglobulin A nephropathy (IgAN) is a kidney disease characterized by the accumulation of IgA deposits in the glomeruli of the kidney, leading to inflammation and damage to the kidney. The inflammatory markers involved in IgAN remain to be defined. Gene expression analysis platforms, such as the NanoString nCounter system, are promising screening and diagnostic tools, especially in the oncology field, but its role as a diagnostic and prognostic tool in IgAN remains scarce. In this study, we aimed to validate the use of NanoString technology to identify potential inflammatory biomarkers involved in the progression of IgAN.SubjectsA total of 30 patients with biopsy-proven IgAN and 7 cases of antineutrophil cytoplasmic antoantibodies (ANCA) associated pauci-immune glomerulonephritis were included for gene expression measurement. For the immunofluorescence validation experiments, a total of 6 IgAN patients and 3 healthy controls were included.MethodsTotal RNA was extracted from formalin-fixed-paraffin-embedded kidney biopsy specimens, and a customized 48-plex human gene CodeSet was used to study 29 genes implicated in different biological pathways. Comparisons in gene expression were made between IgAN and ANCA-associated pauci-immune glomerulonephritis patients to delineate an expression profile specific to IgAN. Gene expression was compared between patients with low and moderate risk of progression. Genes for which RNA expression was associated with disease progression were analyzed for protein expression by immunofluorescence, and compared with healthy controls.ResultsIgAN patients had a distinct gene expression profile with decreased expression in genesIL-6, INFGandC1QBcompared to ANCA patients.C3andTNFR2were identified as potential biomarkers for IgAN progression in patients early in their disease course. Protein expression for those 2 candidate genes was upregulated in IgAN patients compared to controls. Expression of genes implicated in fibrosis (PTEN, CASPASE 3, TGM2, TGFB1, IL2, andTNFRSF1B) was more pronounced in IgAN patient with severe fibrosis compared to those with none.ConclusionsOur findings validate our NanoString mRNA profiling by examining protein expression levels of two candidate genes,C3andTNFR2, in IgAN patients and healthy controls. We were also able to identify several upregulated mRNA transcripts implicated in the development of fibrosis that may be considered as fibrotic markers within IgAN patients.
The extrafollicular B cell response is a hallmark of childhood idiopathic nephrotic syndrome
Authors: Al-Aubodah Tho-Alfakar , Aoudjit Lamine , Pascale Giuseppe , Perinpanayagam Maneka A. , Langlais David , Bitzan Martin , Samuel Susan M. , Piccirillo Ciriaco A. , Takano Tomoko 
Published: September 2023

AbstractAn autoimmune B cell origin for childhood idiopathic nephrotic syndrome (INS) is predicted based on the efficacy of rituximab (RTX) at maintaining long-term remission from proteinuria. Knowledge regarding the nature of the culprit B cell response is very limited. Using single-cell RNA-sequencing, we demonstrate that a B cell transcriptional program poised for effector functions represents the major immune perturbation in the blood of children with active INS. This was conferred by the engagement of an extrafollicular B cell response marked by the expansion of atypical B cells (atBCs), marginal zone-like B cells, and antibody-secreting cells (ASCs). In flow-based analyses of blood from 13 children with active INS and 24 healthy donors, this was reflected by the proliferation of RTX-sensitive extrafollicular (CXCR5−) CD21lowT-bet+CD11c+atBCs, and short-lived T-bet+ASCs. Together, our study provides evidence for an extrafollicular origin for humoral immunity in active INS.
Ensembling Unets, sparse representation and low dimensional visualization for rare chromosomal aberration detection in light microscopy images
Authors: Deschemps Antonin , Grégoire Eric , Martinez Juan S. , Vaurijoux Aurélie , Fernandez Pascale , Dugue Delphine , Bobyk Laure , Valente Marco , Gruel Gaëtan , Moebel Emmanuel , Benadjaoud Mohamed Amine , Kervrann Charles 
Published: September 2023

AbstractIn biological dosimetry, a radiation dose is estimated using the average number of chromosomal aberration per peripheral blood lymphocytes. To achieve an adequate precision in the estimation of this average, hundreds of cells must be analyzed in 2D microscopy images. Currently, this analysis is performed manually, as conventional computer vision techniques struggle with the wide variety of shapes showcased by chromosomes. The false discovery rate of current automated detection systems is high and variable, depending on small variations in data quality (chromosome spread, illumination variations …), which makes using it in a fully automated fashion impossible. Automating chromosomal aberration is needed to reduce diagnosis time. Furthermore, an automated system can process more images, which improves confidence intervals around the estimated radiation dose. We build an object detection model to automate chromosomal aberration detection using recent advances in deep convolutional neural networks and statistical learning. We formulated the problem of rare aberration detection as a heatmap regression problem requiring the minimization of a sparsity-promoting loss to reduce the false alarm rate. Our Unet-based approach is analoguous to a one-stage object detector, and keeps the number of hyperparameters to a minimum. Finally, we demonstrate large performance improvements using an ensemble of checkpoints collected during a single run of training. A PCA-based strategy is used to provide cues for interpretation of our deep neural network-based model. The methodology is demonstrated on real, large, and challenging datasets depicting rare chromosomal aberrations and is favorably compared to a reference dosimetry technique.
Discovery of a functionally selective serotonin 5-HT1Areceptor agonist for the treatment of pain
Authors: Ullrich Annika , Schneider Johannes , Braz João M. , Neu Eduard , Staffen Nico , Stanek Markus , Bláhová Jana , Hove Tamsanqa , Albert Tamara , Allikalt Anni , Löber Stefan , Bhardwaj Karnika , Rodriguez-Rosado Sian , Fink Elissa , Rasmussen Tim , Hübner Harald , Inoue Asuka , Shoichet Brian K. , Basbaum Allan J. , Böttcher Bettina , Weikert Dorothee , Gmeiner Peter 
Published: September 2023

AbstractThe G protein-coupled serotonin receptor 5-HT1AR mediates antinociception and may serve as a valuable target for the treatment of pain. Starting from a chemical library, ST171, a bitopic chemotype activating 5-HT1AR was evolved.In vitropharmacological investigations of ST171 revealed potent and selective Giactivation (EC50= 0.3 nM), with marginal Gsand β-arrestin recruitment. Preclinical studies in mice showed that ST171 was effective in acute and chronic (inflammatory and neuropathic) pain models, without causing sedation. Comparison of cryo-EM structures of a 5-HT1AR-Gicomplex bound to the functionally biased agonist ST171, with a structure bound to the functionally balanced agonist befiradol, showed that both ligands bind to the same orthosteric site, but address different exo-sites. The individual poses are associated with ligand-specific helical dispositions and rearrangements of microdomains. Complementation of these studies with molecular dynamics simulations allowed us to derive structural features associated with ST171’s functional selectivity, a phenomenon that may be crucial to the discovery of more effective and safe GPCR drugs.
Guided Diffusion for molecular generation with interaction prompt
Authors: Wu Peng , Du Huabin , Yan Yingchao , Lee Tzong-Yi , Bai Chen , Wu Song 
Published: September 2023

AbstractMolecular generative models have demonstrated their potential in designing molecules from scratch with high binding affinities in a pre-determined protein pocket and could be combined with traditional structural based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model-InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for each residue to guide the molecular generative process. Comprehensive in-silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.
Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information
Authors: Ahangari Mohammad , Gentry Amanda Elswick , Hassan Mohammed F. , Nguyen Tan Hoang , Kendler Kenneth S. , Bacanu Silviu-Alin , Peterson Roseann E. , Riley Brien P. , Webb Bradley T. 
Published: September 2023

AbstractAlcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast,ADH1CandTHRAgene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in theADH1Cgene (SKAT-OPDefault= 1.06 x 10-9andPEmpiricalweight= 6.25 x 10-11). These findings provide evidence for rare variant association of theADH1Cgene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.
Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
Authors: Nieves Emily , Dandekar Raj , Rackauckas Chris 
Published: September 2023

1AbstractThe recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.
Simultaneous multimodal three-photon and optical coherence microscopy of the mouse brain in the 1700 nm optical windowin vivo
Authors: Yang Xusan , Liu Siyang , Xia Fei , Wu Meiqi , Adie Steven , Xu Chris 
Published: September 2023

AbstractMultimodal microscopy combining various imaging approaches can provide complementary information about tissue in a single imaging session. Here, we demonstrate a multimodal approach combining three-photon microscopy (3PM) and spectral-domain optical coherence microscopy (SD-OCM). We show that an optical parametric chirped-pulse amplification (OPCPA) laser source, which is the standard source for three-photon fluorescence excitation and third harmonic generation (THG), can be used for simultaneous OCM, 3-photon (3P) fluorescence and THG imaging. We validated the system performance in deep mouse brainsin vivowith an OPCPA source operating at 1620 nm center wavelength. We visualized small structures such as myelinated axons, neurons, and large fiber tracts in white matter with high spatial resolution non-invasively using linear and nonlinear contrast at >1 mm depth in intact adult mouse brain. Our results showed that simultaneous OCM and 3PM at the long wavelength window can be conveniently combined for deep tissue imagingin vivo.
Tissue-specific transfer-learning enables retasking of a general comprehensive model to a specific domain
Authors: Li Qing , Perera Deshan , Chen Zhishan , Wen Wanqing , Wang Dinghao , Yan Jun , Shu Xiao-Ou , Zheng Wei , Guo Xingyi , Long Quan 
Published: September 2023

AbstractMachine learning (ML) has proven successful in biological data analysis. However, may require massive training data. To allow broader use of ML in the full spectrum of biology and medicine, including sample-sparse domains, re-directing established models to specific tasks by add-on training via a moderate sample may be promising. Transfer learning (TL), a technique migrating pre-trained models to new tasks, fits in this requirement. Here, by TL, we retasked Enformer, a comprehensive model trained by massive data, tailored to breast cancers using breast-specific data. Its performance has been validated through statistical accuracy of predictions, annotation of genetic variants, and mapping of variants associated with breast cancer. By allowing the flexibility of adding dedicated training data, our TL protocol unlocks future discovery within specific domains with moderate add-on samples by standing on the shoulders of giant models.
Predicting photosynthetic pathway from anatomy using machine learning
Authors: Gilman Ian S. , Heyduk Karolina , Maya-Lastra Carlos A. , Hancock Lillian P. , Edwards Erika J. 
Published: September 2023

SUMMARY- Plants with Crassulacean acid metabolism (CAM) have long been associated with a specialized anatomy, including succulence and thick photosynthetic tissues. Firm, quantitative boundaries between non-CAM and CAM plants have yet to be established – if they indeed exist.- Using novel computer vision software to measure anatomy, we combined new measurements with published data across flowering plants. We then used machine learning and phylogenetic comparative methods to investigate relationships between CAM and anatomy.- We found significant differences in photosynthetic tissue anatomy between plants with differing CAM phenotypes. Machine learning based classification was over 95% accurate in differentiating CAM from non-CAM anatomy, and had over 70% recall of distinct CAM phenotypes. Phylogenetic least squares regression and threshold analyses revealed that CAM evolution was significantly correlated with increased mesophyll cell size, thicker leaves, and decreased intercellular airspace.- Our findings suggest that machine learning may be used to aid the discovery of new CAM species and that the evolutionary trajectory from non-CAM to strong, obligate CAM requires continual anatomical specialization.
IL-23 receptor signaling licenses group 3-like innate lymphoid cells to restrict a live-attenuated oral Chlamydia vaccine in the gut
Authors: He Ying , Wang Yihui , He Rongze , Abdelsalam Ahmed Mohamed , Zhong Guangming 
Published: September 2023

AbstractAn IFNγ-susceptible mutant ofChlamydia muridarumis attenuated in pathogenicity in the genital tract and recently licensed as anintracellularOral vaccinevector or intrOv. Oral delivery of intrOv induces transmucosal protection in the genital tract but intrOv itself is cleared from the gut (without shedding any infectious particles externally) by IFNγ from group 3-like innate lymphoid cells (ILC3s). We further characterized the intrOv interactions with ILC3s in the current study since the interactions may impact both the safety and efficacy of intrOv as an oral Chlamydia vaccine. Intracolonic inoculation with intrOv induced IFNγ that in return inhibited intrOv. The intrOv-IFNγ interactions were dependent on RORγt, a signature transcriptional factor of ILC3s. Consistently, transfer of oral intrOv-induced ILC3s from RORγt-GFP reporter mice to IFNγ-deficient mice rescued the inhibition of intrOv. Thus, IFNγ produced by intrOv-induced ILC3s is likely responsible for inhibiting intrOv, which is further supported by the observation that oral intrOv did induce significant levels of IFNγ-producing LC3s (IFNγ+ILC3s). Interestingly, IL-23 receptor knockout (IL-23R-/-) mice no longer inhibited intrOv, which was accompanied with reduced colonic IFNγ. Transfer of oral intrOv-induced ILC3s rescued the IL-23R-/-mice to inhibit intrOv, validating the dependence of ILC3s on IL-23R signaling for inhibiting intrOv. Clearly, intrOv induces intestinal IFNγ+ILC3s for its own inhibition in the gut, which is facilitated by IL-23R signaling. These findings have provided a mechanism for ensuring the safety of intrOv as an oral Chlamydia vaccine and a platform for investigating how oral intrOv induces transmucosal protection in the genital tract.SignificanceDespite the extensive efforts, no subunit vaccine is available for protecting humans againstC. trachomatisinfection and pathogenicity. Recent studies have led to a licensed live-attenuated oral vaccine (intrOv) that is both effective in inducing transmucosal protection in the genital tract and safe due to its susceptibility to IFNγ delivered by ILC3s. Characterization of the intrOv interactions with IFNγ-producing ILC3s in the current study has revealed a critical role of IL-23 receptor signaling in facilitating ILC3s to clear intrOv from the gut, which has provided a mechanism for ensuring the safety of intrOv and laid a foundation for further revealing the mechanisms by which oral intrOv induces transmucosal immunity in the genital tract since ILC3s can also function as antigen presenting cells.
Friday excess of overall mortality in three German subpopulations
Authors: Wackermann Jiří 
Published: September 2023

AbstractWe studied variations of all-cause human mortality during the weekly cycle, using deathdates collected from death notices published in regional or local newspapers in three distinct regions in Germany (South Baden, North Bavaria, Saarland), total database sizeN≈ 44000. Probability distributions of death on different days of the week show significant (P= 0.0155) departures from the uniformity hypothesis. The day of maximum mortality is Friday (probability of dying = 0.148), whereas minimum mortality is observed on Sundays and Mondays (probability of dying = 0.140 for both days). This ‘Friday excess’ effect seems to be a novel phenomenon, unrelated to other findings on weekly modulation of human mortality.
Allo: Accurate allocation of multi-mapped reads enables regulatory element analysis at repeats
Authors: Morrissey Alexis , Shi Jeffrey , James Daniela Q. , Mahony Shaun 
Published: September 2023

AbstractTransposable elements (TEs) and other repetitive regions have been shown to contain gene regulatory elements, including transcription factor binding sites. Unfortunately, regulatory elements harbored by repeats have proven difficult to characterize using short-read sequencing assays such as ChIP-seq or ATAC-seq. Most regulatory genomics analysis pipelines discard “multi-mapped” reads that align equally well to multiple genomic locations. Since multi-mapped reads arise predominantly from repeats, current analysis pipelines fail to detect a substantial portion of regulatory events that occur in repetitive regions. To address this shortcoming, we developed Allo, a new approach to allocate multi-mapped reads in an efficient, accurate, and user-friendly manner. Allo combines probabilistic mapping of multi-mapped reads with a convolutional neural network that recognizes the read distribution features of potential peaks, offering enhanced accuracy in multi-mapping read assignment. Allo also provides read-level output in the form of a corrected alignment file, making it compatible with existing regulatory genomics analysis pipelines and downstream peak-finders. In a demonstration application on CTCF ChIP-seq data, we show that Allo results in the discovery of thousands of new CTCF peaks. Many of these peaks contain the expected cognate motif and/or serve as TAD boundaries. We additionally apply Allo to a diverse collection of ENCODE ChIP-seq datasets, resulting in multiple previously unidentified interactions between transcription factors and repetitive element families. Finally, we show that Allo may be particularly effective in identifying ChIP-seq peaks in younger TEs, which hold evolutionary significance due to their emergence during human evolution from primates.