bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flexible Intentions: An Active Inference Theory
Authors: Priorelli Matteo , Stoianov Ivilin Peev 
Published: April 2022

AbstractWe present a normative computational theory of how neural circuitry may support visually-guided goal-directed actions in a dynamic environment. The model builds on Active Inference, in which perception and motor control signals are inferred through dynamic minimization of generalized prediction errors. The Posterior Parietal Cortex (PPC) is proposed to maintain constantly updated expectations, or beliefs over the environmental state, and by manipulating them through flexible intentions it is involved in dynamically generating goal-directed actions. In turn, the Dorsal Visual Stream (DVS) and the proprioceptive pathway implement generative models that translate the high-level belief into sensory-level predictions to infer targets, posture, and motor commands. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC putatively hosts its core intention mechanism. More broadly, the study provides a normative basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.
Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics
Authors: Luo Songhao , Wang Zihao , Zhang Zhenquan , Zhou Tianshou , Zhang Jiajun 
Published: April 2022

AbstractGene expression in mammalian cells is highly variable and episodic, and results in a series of discontinuous bursts of mRNAs. A challenge is understanding how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this issue, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson’s paradoxes, i.e., genome-wide burst kinetics exhibited different characteristics in two cases without and with distinguishing feedback regulations. We show that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.
A novel lineage of the Capra genus discovered in the Taurus Mountains of Turkey using ancient genomics
Authors: Daly Kevin G. , Arbuckle Benjamin S. , Rossi Conor , Mattiangeli Valeria , Lawlor Phoebe A. , Mashkour Marjan , Sauer Eberhard , Lesur Joséphine , Atici Levent , Erek Cevdet Merih , Bradley Daniel G. 
Published: April 2022

AbstractDirekli Cave, located in the Taurus Mountains of southern Turkey, was occupied by Late Epipaleolithic hunters-gatherers for the seasonal hunting and processing of game including large numbers of wild goats. We report genomic data from new and publishedCapraspecimens from Direkli Cave and, supplemented with historic genomes from multipleCapraspecies, find a novel lineage best represented by a ∼14,000 year old 2.59X genome sequenced from specimen Direkli4. This newly discovered Capra lineage is a sister clade to the Caucasian tur species (Capra cylindricornisandCapra caucasica), both now limited to the Caucasus region. We identify genomic regions introgressed in domestic goats with high affinity to Direkli4, and find that West Eurasian domestic goats in the past, but not those today, appear enriched for Direkli4-specific alleles at a genome-wide level. This forgotten “Taurasian tur” likely survived Late Pleistocene climatic change in a Taurus Mountain refugia and its genomic fate is unknown.
Humans have distinct repertoires of IgA1
Authors: Dingess Kelly A. , Hoek Max , van Rijswijk Danique M.H. , Tamara Sem , den Boer Maurits A. , Damen Mirjam J.A. , Barendregt Arjan , Romijn Michelle , Juncker Hannah G. , van Keulen Britt J. , Vidarsson Gestur , van Goudoever Johannes B. , Bondt Albert , Heck Albert J.R. 
Published: April 2022

AbstractThe most abundant immunoglobulin present in the human body is IgA1. It has the highest concentrations at the mucosal lining and in biofluids such as milk and is the second most abundant class of antibodies in serum. We assessed the structural diversity and clonal repertoire of IgA1-containing molecular assemblies longitudinally in human serum and milk from three donors using a mass spectrometry-based approach. IgA-containing molecules purified from serum or milk were assessed by the release and subsequent analysis of their Fab fragments. Our data revealed that serum IgA1 consists of two distinct structural populations, namely monomeric IgA1 (∼ 80%) and dimeric joining (J-) chain coupled IgA1 (∼ 20%). Also, we confirmed that IgA1 in milk is present solely as secretory (S)IgA, consisting of two (∼ 50%), three (∼ 33%) or four (∼ 17%) IgA1 molecules assembled with a J-chain and secretory component (SC). Interestingly, the serum and milk IgA1-Fab repertoires were distinct between monomeric, and J-chain coupled dimeric IgA1. The serum dimeric J-chain coupled IgA1 repertoire contained several abundant clones also observed in the milk IgA1 repertoire. The latter repertoire had little to no overlap with the serum monomeric IgA1 repertoire. This suggests that human IgA1s have (at least) two distinct origins; one of these produces dimeric J-chain coupled IgA1 molecules, shared in human serum and milk, and another produces monomeric IgA1 ending up exclusively in serum.
Data Driven Control of Vagus Nerve Stimulation for the Cardiac System
Authors: Branen Andrew , Yao Yuyu , Kothare Mayuresh , Mahmoudi Babak , Kumar Gautam 
Published: April 2022

1AbstractVagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. This electroceutical therapy has been approved for epilepsy, and treatment resistant depression. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, heart arrhythmia, hypertension, and gastric motility disorders. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were left inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiac physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiac system. Using an existingin silicophysiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and the blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlyingin silicophysiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiac system.
FollicleFinder: automated three-dimensional segmentation of human ovarian follicles
Authors: Yamauchi Kevin , Biniasch Marieke , Franz Leopold , Gómez Harold , De Geyter Christian , Iber Dagmar 
Published: April 2022

SummaryIn vitro fertilization (IVF) treatment protocols require frequent monitoring of the ovarian follicle growth process. We report FollicleFinder, an open source pipeline for the automated, 3D segmentation of ovarian follicles. FollicleFinder also accurately measures clinically-relevant morphological properties such as diameter, surface area, and volume.AvailabilityThe FollicleFinder pipeline is available at https://git.bsse.ethz.ch/iber/ovary-analysis and the graphical user interface is available at https://git.bsse.ethz.ch/iber/follicle-tracker.
MOCCS profile analysis clarifies the cell type dependency of transcription factor-binding sequences and cis-regulatory SNPs in humans
Authors: Tahara Saeko , Tsuchiya Takaho , Matsumoto Hirotaka , Ozaki Haruka 
Published: April 2022

AbstractTranscription factors (TFs) show heterogeneous DNA-binding specificities in individual cells and whole organisms in natural conditions): de novo motif discovery usually provides multiple motifs even from a single ChIP-seq sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored. Here, we propose MOCCS profiles, the new representation of DNA-binding specificity of TFs, which describes a ChIP-seq sample as a profile of TF-binding specificity scores (MOCCS2scores) for every k-mer sequence. Using our k-mer-based motif discovery method MOCCS2, we systematically computed MOCCS profiles for >10,000 human TF ChIP-seq samples across diverse TFs and cell types. Comparison of MOCCS profiles revealed the global distributions of DNA-binding specificities, and found that one-third of the analyzed TFs showed differences in DNA-binding specificities across cell types. Moreover, we showed that the differences in MOCCS2scores (ΔMOCCS2scores) predicted the effect of variants on TF binding, validated by in vitro and in vivo assay datasets. We also demonstrate ΔMOCCS2scores can be used to interpret non-coding GWAS-SNPs as TF-affecting SNPs and provide their candidate responsible TFs and cell types. Our study provides the basis for investigating gene expression regulation and non-coding disease-associated variants in humans.
High-quality haploid genomes corroborate 29 chromosomes and highly conserved synteny of genes inHyleshawkmoths (Lepidoptera: Sphingidae)
Authors: Hundsdoerfer Anna K. , Schell Tilman , Patzold Franziska , Wright Charlotte J. , Yoshido Atsuo , Marec František , Daneck Hana , Winkler Sylke , Greve Carola , Podsiadlowski Lars , Hiller Michael , Pippel Martin 
Published: April 2022

AbstractBackgroundMorphological and traditional genetic studies of the young Pliocene genusHyleshave led to the understanding that despite its importance for taxonomy, phenotypic similarity of wing patterns does not correlate with phylogenetic relationship. To gain insights into various aspects of speciation in the Spurge Hawkmoth (Hyles euphorbiae), we assembled a chromosome-level genome and investigated some of its characteristics.ResultsThe genome of a maleH. euphorbiaewas sequenced using PacBio and Hi-C data, yielding a 504 Mb assembly (scaffold N50 of 18.2 Mb) with 99.9% of data represented by the 29 largest scaffolds forming the haploid chromosome set. Consistent with this, FISH analysis of the karyotype revealedn= 29 chromosomes and a WZ/ZZ (female/male) sex chromosome system. Estimates of chromosome length based on the karyotype image provided an additional quality metric of assembled chromosome size. Rescaffolding the published maleH. vespertiliogenome resulted in a high-quality assembly (651 Mb, scaffold N50 of 22 Mb) with 98% of sequence data in the 29 chromosomes. The larger genome size ofH. vespertilio(average 1C DNA value of 562 Mb) was accompanied by a proportional increase in repeats from 45% inH. euphorbiae(measured as 472 Mb) to almost 55% inH. vespertilio. Several wing pattern genes were found on the same chromosomes in the two species, with varying amounts and positions of repetitive elements and inversions possibly corrupting their function.ConclusionsOur two-fold comparative genomics approach revealed high gene synteny of theHylesgenomes to other Sphingidae and high correspondence to intact Merian elements, the ancestral linkage groups of Lepidoptera, with the exception of three simple fusion events. We propose a standardized approach for genome taxonomy using nucleotide homology via scaffold chaining as the primary tool combined with Oxford plots based on Merian elements to infer and visualize directionality of chromosomal rearrangements. The identification of wing pattern genes promises future understanding of the evolution of forewing patterns in the genusHyles, although further sequencing data from more individuals are needed. The genomic data obtained provide additional reliable references for further comparative studies in hawkmoths (Sphingidae).
Social determinants of health and disease in companion dogs: A cohort study from the Dog Aging Project
Authors: McCoy Brianah M. , Brassington Layla , Jin Kelly , Dolby Greer A. , Shrager Sandi , Collins Devin , Dunbar Matthew ,   Dog Aging Project Consortium, Snyder-Mackler Noah 
Published: April 2022

AbstractExposure to social environmental adversity is associated with health and survival in many social species, including humans. However, little is known about if and how these health and mortality effects vary across the lifespan, largely due to the difficulty of studying long-lived organisms across much of their lifespan. Here, we leveraged a relatively new and powerful model for human aging, the companion dog, to investigate which components of the social environment are associated with dog health and how these associations vary across the lifespan. We drew on comprehensive survey data collected on 21,410 dogs from the Dog Aging Project and identified five factors that together explained 33.7% of the variation in a dog’s social environment. Factors capturing financial and household adversity were associated with poorer health and lower physical mobility in companion dogs, while factors that captured social support, such as living with other dogs, were associated with better health when controlling for dog age and weight. Some of these associations differed across a dog’s lifespan, including a stronger relationship between owner age and health in younger (as compared to older) dogs. Taken together, these findings suggest the importance of income, stability, and owner age on owner-reported health outcomes in companion dogs and point to potential behavioral and/or environmental modifiers that can be used to promote healthy aging across species.
Kinome Inhibition States and Multiomics Data Enable Prediction of Cell Viability in Diverse Cancer Types
Authors: Berginski Matthew E. , Joisa Chinmaya U. , Golitz Brian T. , Gomez Shawn M. 
Published: April 2022

AbstractProtein kinases play a vital role in a wide range of cellular processes and compounds that inhibit kinase activity have emerged as a primary focus for targeted therapy development in cancer. This has inspired work that characterizes the spectrum of kinases targeted by specific inhibitors and the inclusion of these inhibitors in large-scale cell viability screening efforts. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve reasonable prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.
Entropy sorting of single cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryo
Authors: Radley Arthur , Corujo-Simon Elena , Nichols Jennifer , Smith Austin , Dunn Sara-Jane 
Published: April 2022

ABSTRACTA major challenge in single cell gene expression analysis is to discern meaningful cellular heterogeneity from technical or biological noise. To address this challenge, we present Entropy Sorting, a mathematical framework that distinguishes genes indicative of cell identity. ES achieves this in an unsupervised manner by quantifying if observed correlations between features are more likely to have occurred due to random chance versus a dependent relationship, without the need for any user defined significance threshold. On synthetic data we demonstrate the removal of noisy signals to reveal a higher resolution of gene expression patterns than commonly used feature selection methods. We then apply ES to human pre-implantation embryo scRNA-seq data. Previous studies failed to unambiguously identify early inner cell mass (ICM), suggesting that the human embryo may diverge from the mouse paradigm. In contrast, ES resolves the ICM and reveals sequential lineage bifurcations as in the classical model. Entropy sorting thus provides a powerful approach for maximising information extraction from high dimensional datasets such as scRNA-seq data.
High-throughput Oligopaint screen identifies druggable regulators of genome folding
Authors: Park Daniel S. , Nguyen Son C. , Isenhart Randi , Shah Parisha P. , Kim Wonho , Barnett R. Jordan , Chandra Aditi , Luppino Jennifer M. , Harke Jailynn , Wai May , Yang Rachel , Lan Yemin , Yoon Sora , Yunker Rebecca , Vahedi Golnaz , Phillips-Cremins Jennifer E. , Jain Rajan , Joyce Eric F. 
Published: April 2022

SummaryAlthough the molecular rules governing genome organization are being quickly elucidated, relatively few proteins regulating this process have been identified. To address this gap, we developed a fully automated imaging pipeline, called HiDRO (high-throughput DNA or RNA labeling with optimized Oligopaints), that permits quantitative measurement of chromatin interactions across a large number of samples. Using HiDRO, we screened the human druggable genome and identified >300 factors that regulate chromatin folding during interphase, including 43 validated hits that either increase or decrease interactions between topological associating domains (TADs). We discovered that genetic or chemical inhibition of the ubiquitous kinase GSK3A enhances long-range interactions by dysregulating cohesin-mediated chromatin looping. Collectively, these results highlight a noncanonical role for GSK3A signaling in nuclear architecture and underscore the broader utility of HiDRO-based screening to identify novel mechanisms that drive the spatial organization of the genome.
Protein Kinase C Alpha is a Central Node for Tumorigenic Transcriptional Networks in Human Prostate Cancer
Authors: Cooke Mariana , Zhang Xuyao , Zhang Suli , Lal Priti , Daniel Reba E. , Feldman Michael D. , Abba Martin C. , Kazanietz Marcelo G. 
Published: April 2022

ABSTRACTAberrant expression of protein kinase C (PKC) isozymes is a hallmark of cancer. The different members of the PKC family control cellular events associated with cancer development and progression. Whereas the classical/conventional PKCα isozyme has been linked to tumor suppression in most cancer types, here we demonstrate that this kinase is required for the mitogenic activity of aggressive human prostate cancer cells displaying aberrantly high PKCα expression. Immunohistochemical analysis showed abnormal up-regulation of PKCα in human primary prostate tumors. Interestingly, silencing PKCα expression from aggressive prostate cancer cells impairs cell cycle progression, proliferation and invasion, as well as their tumorigenic activity in a mouse xenograft model. Mechanistic analysis revealed that PKCα exerts a profound control of gene expression, particularly over genes and transcriptional networks associated with cell cycle progression and E2F transcription factors. PKCα RNAi depletion from PC3 prostate cancer cells led to reduced expression of pro-inflammatory cytokine and epithelial-to-mesenchymal transition (EMT) genes, as well as a prominent down-regulation in the expression of the immune checkpoint ligand PD-L1. This PKCα-dependent gene expression profile was corroborated in a human prostate cancer database. Our studies established PKCα as a multifunctional kinase that plays pleiotropic roles in prostate cancer, particularly by controlling genetic networks associated with tumor growth and progression. The identification of PKCα as a pro-tumorigenic kinase in human prostate cancer provides strong rationale for developing therapeutic approaches towards targeting PKCα or its effectors.IMPLICATIONSOur findings implicate PKCα as a major node for transcriptional regulation of tumorigenic pathways in prostate cancer.