bluuebunny/crossref_metadata_2025_split huggingface dataset:
Regulation of Cardiomyocyte Adhesion and Mechanosignalling Through Distinct Nanoscale Behaviour of Integrin Ligands Mimicking Healthy or Fibrotic ECM
Authors: Hawkes William , Marhuenda Emilie , Reynolds Paul , O’Neill Caoimhe , Pandey Pragati , Samuel Wilson Darren Graham , Freeley Mark , Huang Da , Hu Junquiang , Gondarenko Sasha , Hone James , Gadegaard Nikolaj , Palma Matteo , Iskratsch Thomas 
Published: March 2022

AbstractThe stiffness of the cardiovascular environment changes during ageing and in disease and contributes to disease incidence and progression. Changing collagen expression and crosslinking regulate the rigidity of the cardiac ECM. Additionally, basal lamina glycoproteins, especially laminin and fibronectin regulate cardiomyocyte adhesion formation, mechanics and mechano-signalling. Laminin is abundant in the healthy heart, but fibronectin is increasingly expressed in the fibrotic heart. ECM receptors are co-regulated with the changing ECM. Due to differences in integrin dynamics, clustering, and downstream adhesion formation this is expected to ultimately influence cardiomyocyte mechanosignalling; however details remain elusive. Here we sought to investigate how different cardiomyocyte ligand/integrin combinations are affecting adhesion formation, traction forces and mechanosignalling, using a combination of uniformly coated surfaces with defined stiffness, PDMS nanopillars, micropatterning and specifically designed bionanoarrays for precise ligand presentation. Thereby we find that neonatal rat cardiomyocytes (which express both laminin and fibronectin binding integrins) adhesion nanoscale organisation, signalling and traction force generation are strongly dependent on the integrin/ligand combination. Together our data indicates that the presence of fibronectin in combination with the enhanced stiffness in fibrotic areas will strongly impact on the cardiomyocyte behaviour and influence disease progression.
A single-cell clusters similarity measure for different batches, datasets, and samples
Authors: González-Velasco Óscar , Sanchez-Luis Elena , De La Rosa Enrique , Sánchez-Santos José Manuel , De Las Rivas Javier 
Published: March 2022

AbstractSummarySince the inception of single-cell level measuring techniques, identification of distinct cell stages, phenotypes and populations has been a challenge. Cell clustering and dimensionality reduction methods are the most popular approaches to identify heterogeneity of single-cell data. But, as public repositories continue to grow in number, integrative analyses and merging of large pools of samples from different and heterogeneous datasets becomes a difficult challenge, which showcases the impossibility of scalability of some of the existing methods. Here we present ClusterFoldSimilarity, an R package that calculates a measure of similarity between clusters from different datasets/batches, without the need of correcting for batch effect or normalizing and merging the data, thus avoiding artifacts and the loss of information derived from these kinds of techniques. The similarity metric is based on the average vector module and sign of the product of logarithmic fold-changes. ClusterFoldSimilarity compares every single pair of clusters from any number of different samples/datasets, including different number of clusters for each sample. Additionally, the algorithm is able to select the top genes which contribute the most to the similarity of two specific clusters, serving also as a feature selection tool.Availability and implementationThe algorithm is freely available as an R package at: https://github.com/OscarGVelasco/ClusterFoldSimilarityContactoscargvelasco@gmail.com
Integrated Diffusion Image Operator (iDIO): A tool for automated configuration and processing of diffusion MRI data
Authors: Hsu Chih-Chin Heather , Chong Shin Tai , Kung Yi-Chia , Kuo Kuan-Tsen , Huang Chu-Chung , Lin Ching-Po 
Published: March 2022

AbstractThe preprocessing of diffusion magnetic resonance imaging (dMRI) data involves numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for non-expert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated Diffusion Image Operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via automatic configuration for a set of optimal processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO’s workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or non-specialist users to adopt the state-of-art dMRI processing steps.
Decomposing predictability to identify dominant causal drivers in complex ecosystems
Authors: Suzuki Kenta , Matsuzaki Shin-ichiro S. , Masuya Hiroshi 
Published: March 2022

AbstractEcosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time-series based causal inferences. Here we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded new but interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.Significance StatementEffective use of ecosystem monitoring data to resolve global environmental issues is a major challenge of the 21st century ecology. A promising solution to address this challenge is a time-series-based causal analysis which can provide insight on the mechanical links between ecosystem components. In this work, a model-free framework named EcohNet is proposed. EcohNet utilizes ensemble predictions of echo state networks, which are known to be fast, accurate, and highly relevant for a variety of dynamical systems, and can robustly predict causal networks of ecosystem components. It also can provide an optimized forecasting of overall ecosystem components, and could be used to analyze complex and hybrid multivariate time series in many scientific areas, not limited to ecosystems.
Geometrical factors determining dendritic domain intersection between neurons: a modeling study
Authors: Gatica Rafael Ignacio , Montero Trinidad , Farassat Navid , Henny Pablo 
Published: March 2022

ABSTRACTOverlap between dendritic trees of neighboring neurons is a feature of nervous systems. Overlap allows neurons to share common afferences and defines the topography of the circuit they belong to. Proximity is also a requirement for dendritic communication, including dendro-dendritic synaptic contacts. We simulated overlap dynamics between pairs of ventral tegmental area dopamine neurons, a population characterized by diverse and extensive dendritic domains. Using each neuron’s 3D convex hull (CH) as a proxy for dendritic domain size and shape, we examined intersection versus cell-to-cell distance curves for 210 pairs of neurons, and found that decay dynamics were diverse and complex, indicating that intersection between real dendritic domains does not comply with spherical or isotropic shape assumptions. We re-examined intersection dynamics for the same 210 pairs, but this time with either: a) a normalized volume corresponding to the average volume of all CHs, b) normalized shapes, using an average CH shape, or CHs from neurons exhibiting average dendritic distribution, isotropy or morphology, or c) a normalized cell body position, which we artificially placed in each CHs’ centroid. All three interventions significantly increased pair intersection and simplified decay dynamics, yet shape normalization had the strongest influence. Critically, shape uniformity was also the most relevant factor for increased dendritic domain pair intersection using the neurons’ original brain position and distances. We applied this experimental approach to other populations using reconstructions fromneuromorpho.orgdatabase and found that shape and intersection dynamics were cell-type dependent. We conclude that intersection between neurons is not only maximized by proximity, but that individual-specific dendritic domain geometries have a profound impact too. The results predict that one biological solution for circuits requiring selective connectivity is to exhibit greater heterogeneity in size, cell body location, and especially shape, among their constituent neuronal elements.
A Drosophila in vivo chemical screen reveals that combination drug treatment targeting MEK and DGKα mitigates Ras-driven polarity-impaired tumourigenesis
Authors: La Marca John E. , Ely Robert W. , Diepstraten Sarah T. , Burke Peter , Kelly Gemma L. , Humbert Patrick O. , Richardson Helena E. 
Published: March 2022

AbstractThe RAS oncogene and upregulation of the RAS signalling pathway is highly prevalent in human cancer, and therefore, therapeutically targeting the RAS pathway is a common treatment in cancer. However, RAS pathway upregulation is not sufficient to drive malignant cancer, since senescence mechanisms prevent cancer progression. Thus, additional mutations, such as mutations that prevent senescence or alter the tissue architecture (cell polarity), are required for RAS-driven tumour progression. Moreover, targeting RAS-driven cancers with RAS pathway inhibitors can often lead to undesirable side-effects and to drug resistance. Thus, identifying compounds that synergise with RAS-pathway inhibitors would enable lower doses of the RAS pathway inhibitors to be used and also decrease the acquisition of drug resistance. Here, in a boutique chemical screen using a Drosophila model of Ras-driven cell polarity-impaired cancer, we have identified compounds that reduce tumour burden by synergising with subtherapeutic doses of the RAS pathway inhibitor, Trametinib, which inhibits mitogen-activated kinase kinase (MEK). Analysis of one of the hits from the screen, Ritanserin, which targets serotonin receptors and diacy glycerol kinase alpha (DGKα), revealed that DGKα was the critical target in its synergism with Trametinib. We show that human mammary epithelial cells harbouring the H-RAS oncogene and knockdown of the cell polarity gene, SCRIB, are also sensitive to treatment with low doses of Trametinib and DGKα inhibition. Mechanistically, DGKα inhibition synergises with Trametinib by inhibiting MEK and mTOR activity. Altogether, our results provide evidence that targeting RAS-driven human cancers with RAS pathway and DGKα inhibitors will be an effective combination therapy.
Knowledge-guided data mining on the standardized architecture of NRPS: subtypes, novel motifs, and sequence entanglements
Authors: He Ruolin , Zhang Jinyu , Shao Yuanzhe , Gu Shaohua , Song Chen , Qian Long , Yin Wen-Bing , Li Zhiyuan 
Published: March 2022

AbstractNon-ribosomal peptide synthetase (NRPS) is a diverse family of biosynthetic enzymes for the assembly of bioactive peptides. Despite advances in microbial sequencing, the lack of a consistent standard for annotating NRPS domains and modules has made data-driven discoveries challenging. To address this, we introduced a standardized architecture for NRPS, by using known conserved motifs to partition typical domains. This motif-and-intermotif standardization allowed for systematic evaluations of sequence properties from a large number of NRPS pathways, resulting in the most comprehensive cross-kingdom C domain subtype classifications to date, as well as the discovery and experimental validation of novel conserved motifs with functional significance. Furthermore, our coevolution analysis revealed important barriers associated with reengineering NRPSs and uncovered the entanglement between phylogeny and substrate specificity in NRPS sequences. Our findings provide a comprehensive and statistically insightful analysis of NRPS sequences, opening avenues for future data-driven discoveries.Author SummaryNRPS, a gigantic enzyme that produces diverse microbial secondary metabolites, provides a rich source for important medical products including antibiotics. Despite the extensive knowledge gained about its structure and the large amount of sequencing data available, the frequent failure of reengineering NRPS in synthetic biology highlights the fact that much is still unknown. In this work, we applied existing knowledge to data mining of NRPS sequences, using well-known conserved motifs to partition NRPS sequences into motif-intermotif architectures. This standardization allows for integrating large amounts of sequences from different sources, providing a comprehensive overview of NRPSs across different kingdoms. Our findings included new C domain subtypes, novel conserved motifs with implication in structural flexibility, and insights into why NRPSs are so difficult to reengineer. To facilitate researchers in related fields, we constructed an online platform “NRPS Motif Finder” for parsing the motif-and-intermotif architecture and C domain subtype classification (http://www.bdainformatics.org/page?type=NRPSMotifFinder). We believe that this knowledge-guided approach not only advances our understanding of NRPSs but also provides a useful methodology for data mining in large-scale biological sequences.
MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives
Authors: Castro-de-Araujo Luis FS , Singh Madhurbain , Zhou Yi (Daniel) , Vinh Philip , Verhulst Brad , Dolan Conor V , Neale Michael C 
Published: March 2022

AbstractEstablishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non- familial sources. Our model extends the MRDoC model (Minică et al 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al., 1993).
Learning deep representations of enzyme thermal adaptation
Authors: Li Gang , Buric Filip , Zrimec Jan , Viknander Sandra , Nielsen Jens , Zelezniak Aleksej , Engqvist Martin KM 
Published: March 2022

AbstractTemperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a dataset of over 3 million enzymes labeled with optimal growth temperatures (OGT) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other recent deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
Sexually antagonistic co-evolution can explain female display signals and male sensory adaptations
Authors: Wiberg R. Axel W. , Murray Rosalind L. , Herridge Elizabeth , Pärssinen Varpu , Gwynne Darryl T. , Bussière Luc F. 
Published: March 2022

AbstractThe prevalence and diversity of female ornaments poses a challenge to evolutionary theory because males should prefer mates that spend resources on offspring rather than ornaments. Among dance flies, there is extraordinary variation in sexual dimorphism. Females of many species have conspicuous ornaments (leg scales and inflatable abdominal sacs). Meanwhile males of some species have exaggerated regions of their eyes with larger ommatidial facets that allow for regionally elevated photosensitivity and/or acuity. Here, we conduct a comparative study of these traits using both species descriptions available from the literature, as well as quantitative measures of eyes and ornaments from wild-caught flies. We show a conspicuous covariance across species between exaggerated male dorsal eye regions and the extent of female ornaments: species with highly ornamented females have males with more exaggerated eyes. We discuss this pattern in the context of competing hypotheses for the evolution of these traits and propose a plausible role for sexually antagonistic coevolution.
Pharmacokinetics of Sustained-release Buprenorphine and Extended-release Buprenorphine in Mice with Surgical Catheterization
Authors: Saenz Marissa , Bloom-Saldana Elizabeth , Synold Tim , Ermel Richard W , Fueger Patrick T , Finlay James B 
Published: March 2022

AbstractThe Guide for the Care and Use of Laboratory Animals strongly encourages the use of pharmaceutical grade chemicals and analgesics. The extra-label use of sustained-release buprenorphine (SRB) is commonly administered to rodents to mitigate moderate-to-severe pain. An FDA-indexed buprenorphine formulation, known as extended-release buprenorphine (XRB), has recently become available and is currently the only pharmaceutical grade slow-release buprenorphine approved for use in mice and rats. However, no studies have directly compared the pharmacokinetic (PK) parameters and therapeutic efficacy of SRB and XRB in surgically catheterized mice. Thus, we compared the plasma buprenorphine concentrations and PK parameters of SRB and XRB in mice after surgical catheterization. We hypothesized that mice treated with SRB or XRB would have circulating buprenorphine concentrations exceeding the therapeutic threshold for up to 72-hours post-operatively. Male and female C57Bl/6J mice were anesthetized, treated with either SRB (1 mg/kg, SC, once) or XRB (3.25 mg/kg, SC, once) and underwent surgical catheterization. At 6, 24, 48, and 72 h after SRB or XRB administration, arterial blood samples were collected. Post-operative weight loss was similar between groups with a decline of 11.7 ± 1.6 and 12.3 ± 0.7% in males and 7.6 ± 2.2 and 8.1 ± 1.1% (mean ± SEM) in females treated with SRB and XRB, respectively. Both SRB and XRB maintained circulating buprenorphine concentrations above the therapeutic level of 1.0 ng/mL for 72 h after administration. XRB buprenorphine concentrations were significantly greater (3-4-fold) than SRB concentrations at 6, 24, and 48 h, commensurate with the increase dose concentration of XRB to SRB. These results support the use of either SRB or XRB for the alleviation of postoperative pain in mice. The new availability of FDA-indexed XRB increases the options for safe and effective pharmaceutical grade analgesia in rodents.
Angiotensin blockade enhances motivational reward learning via enhancing striatal prediction error signaling and frontostriatal communication
Authors: Xu Ting , Zhou Xinqi , Kanen Jonathan W. , Wang Lan , Li Jialin , Chen Zhiyi , Zhang Ran , Jiao Guojuan , Zhou Feng , Zhao Weihua , Yao Shuxia , Becker Benjamin 
Published: March 2022

ABSTRACTAdaptive human learning utilizes reward prediction errors (RPEs) that scale the differences between expected and actual outcomes to optimize future choices. Depression has been linked with biased RPE signaling and an exaggerated impact of negative outcomes on learning which may promote amotivation and anhedonia. The present proof-of-concept study combined computational modelling and multivariate decoding with neuroimaging to determine the influence of the selective competitive angiotensin II type 1 receptor antagonist losartan on learning from positive or negative outcomes and the underlying neural mechanisms in healthy humans. In a double-blind, between-subjects, placebo-controlled pharmaco-fMRI experiment, 61 healthy male participants (losartan, n=30; placebo, n=31) underwent a probabilistic selection reinforcement learning task incorporating a learning and transfer phase. Losartan improved choice accuracy for the hardest stimulus pair via increasing expected value sensitivity towards the rewarding stimulus relative to the placebo group during learning. Computational modelling revealed that losartan reduced the learning rate for negative outcomes and increased exploitatory choice behaviors while preserving learning for positive outcomes. These behavioral patterns were paralleled on the neural level by increased RPE signaling in orbitofrontal-striatal regions and enhanced positive outcome representations in the ventral striatum (VS) following losartan. In the transfer phase, losartan accelerated response times and enhanced VS functional connectivity with left dorsolateral prefrontal cortex when approaching maximum rewards. These findings elucidate the potential of losartan to reduce the impact of negative outcomes during learning and subsequently facilitate motivational approach towards maximum rewards in the transfer of learning. This may indicate a promising therapeutic mechanism to normalize distorted reward learning and fronto-striatal functioning in depression.