bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variation in vocal production learning across songbirds
Authors: Searcy William A. , Soha Jill , Peters Susan , Nowicki Stephen 
Published: September 2021

Songbirds as a whole are considered to be vocal production learners, meaning that they modify the structure of their vocalizations as a result of experience with the vocalizations of others. The more than 4000 species of songbirds, however, vary greatly in crucial features of song development. Variable features include: (i) the normality of the songs of early-deafened birds, reflecting the importance of innate motor programmes in song development; (ii) the normality of the songs of isolation-reared birds, reflecting the combined importance of innate auditory templates and motor programmes; (iii) the degree of selectivity in choice of external models; (iv) the accuracy of copying from external models; and (v) whether or not learning from external models continues into adulthood. We suggest that because of this variability, some songbird species, specifically those that are able to develop songs in the normal range without exposure to external models, can be classified as limited vocal learners. Those species that require exposure to external models to develop songs in the normal range can be considered complex vocal learners.
This article is part of the theme issue ‘Vocal learning in animals and humans’.
Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks
Authors: Sani Omid G. , Pesaran Bijan , Shanechi Maryam M. 
Published: September 2021

AbstractUnderstanding the dynamical transformation of neural activity to behavior requires modeling this transformation while both dissecting its potential nonlinearities and dissociating and preserving its nonlinear behaviorally relevant neural dynamics, which remain unaddressed. We present RNN PSID, a nonlinear dynamic modeling method that enables flexible dissection of nonlinearities, dissociation and preferential learning of neural dynamics relevant to specific behaviors, and causal decoding. We first validate RNN PSID in simulations and then use it to investigate nonlinearities in monkey spiking and LFP activity across four tasks and different brain regions. Nonlinear RNN PSID successfully dissociated and preserved nonlinear behaviorally relevant dynamics, thus outperforming linear and non-preferential nonlinear learning methods in behavior decoding while reaching similar neural prediction. Strikingly, dissecting the nonlinearities with RNN PSID revealed that consistently across all tasks, summarizing the nonlinearity only in the mapping from the latent dynamics to behavior was largely sufficient for predicting behavior and neural activity. RNN PSID provides a novel tool to reveal new characteristics of nonlinear neural dynamics underlying behavior.
ISL1 is necessary for auditory neuron development and contributes towards tonotopic organization
Authors: Filova Iva , Pysanenko Kateryna , Tavakoli Mitra , Vochyanova Simona , Dvorakova Martina , Bohuslavova Romana , Smolik Ondrej , Fabriciova Valeria , Hrabalova Petra , Benesova Sarka , Valihrach Lukas , Cerny Jiri , Yamoah Ebenezer N. , Syka Josef , Fritzsch Bernd , Pavlinkova Gabriela 
Published: September 2021

AbstractA cardinal feature of the auditory pathway is frequency selectivity, represented in a tonotopic map from the cochlea to the cortex. The molecular determinants of the auditory frequency map are unknown. Here, we discovered that the transcription factor ISL1 regulates the molecular and cellular features of auditory neurons, including the formation of the spiral ganglion and peripheral and central processes that shape the tonotopic representation of the auditory map. We selectively knocked out Isl1 in auditory neurons using Neurod1Cre strategies. In the absence of Isl1, spiral ganglion neurons migrate into the central cochlea and beyond, and the cochlear wiring is profoundly reduced and disrupted. The central axons of Isl1 mutants lose their topographic projections and segregation at the cochlear nucleus. Transcriptome analysis of spiral ganglion neurons shows that Isl1 regulates neurogenesis, axonogenesis, migration, neurotransmission-related machinery, and synaptic communication patterns. We show that peripheral disorganization in the cochlea affects the physiological properties of hearing in the midbrain and auditory behavior. Surprisingly, auditory processing features are preserved despite the significant hearing impairment, revealing central auditory pathway resilience and plasticity in Isl1 mutant mice. Mutant mice have a reduced acoustic startle reflex, altered prepulse inhibition, and characteristics of compensatory neural hyperactivity centrally. Our findings show that ISL1 is one of the obligatory factors required to sculpt auditory structural and functional tonotopic maps. Still, upon Isl1 deletion, the ensuing central compensatory plasticity of the auditory pathway does not suffice to overcome developmentally induced peripheral dysfunction of the cochlea.
Parenteral glucose supply and pharmacological glycolysis inhibition determine the clinical fate of infected preterm newborns
Authors: Muk Tik , Brunse Anders , Henriksen Nicole L. , Aasmul-Olsen Karoline , Nguyen Duc Ninh 
Published: September 2021

AbstractPreterm infants are susceptible to bloodstream infection that can lead to sepsis. High parenteral glucose supplement is commonly used to support their growth and energy expenditure, but may exceed endogenous regulation during infection, causing dysregulated immune response and clinical deterioration. Using a preterm piglet model of neonatal sepsis induced byStaphylococcus epidermidisinfection, we demonstrate the delicate interplay between immunity and energy metabolism to regulate the host infection response. Circulating glucose levels, glycolysis and inflammatory response to infection are closely connected across the states of tolerance, resistance and immunoparalysis. Further, high parenteral glucose provision during infection induces hyperglycemia, elevated glycolysis and inflammation, leading to lactate acidosis and sepsis, whereas glucose restricted individuals are clinically unaffected with increased gluconeogenesis to maintain moderate hypoglycemia. Finally, pharmacological glycolysis inhibition during normoglycemia enhances bacterial clearance and dampens inflammation but fails to prevent sepsis. Our results uncover how blood glucose controls immune cell metabolism and function, in turn determining the clinical fate of infected preterm neonates. This also questions the current practice of parenteral glucose supply for infected preterm infants.
The evolution of open habitats in North America revealed by deep learning models
Authors: Andermann Tobias , Strömberg Caroline , Antonelli Alexandre , Silvestro Daniele 
Published: September 2021

AbstractOpen vegetation today constitutes one of the most extensive biomes on earth, including temperate grasslands and tropical savannas. Yet these biomes originated relatively recently in earth history, likely replacing forested habitats as recently as the second half of the Cenozoic, although the timing of their origination and the dynamics of their expansion remain uncertain. Here, we present a new hypothesis of paleovegetation change in North America, showing that open habitats originated between 25 and 20 Ma in the center of the continent, and expanded rapidly starting 8 Ma to eventually become the most prominent vegetation type today. To obtain space-time predictions of paleovegetation, we developed a new Bayesian deep learning model that utilizes available information from fossil evidence, geologic models, and paleoclimate proxies. We compiled a large dataset of paleovegetation reconstructions from the peer-reviewed literature, which we used in combination with current vegetation data to train the model. The model learns to predict vegetation based on the learned associations between the vegetation at a given site and multiple biotic and abiotic predictors: fossil mammal occurrences, plant macrofossils, estimates of temperature and precipitation, latitude, and the effects of spatial and temporal autocorrelation. Our results provide a new, spatially detailed reconstruction of habitat evolution in North America and our deep learning model paves the way for a new quantitative approach to estimating paleovegetation changes.
Medullary vein architecture modulates the white matter BOLD cerebrovascular reactivity signal response to CO2: observations from high-resolution T2*weighted imaging at 7T
Authors: Bhogal Alex A. 
Published: September 2021

ABSTRACTBrain stress testing using blood oxygenation level-dependent (BOLD) MRI to evaluate changes in cerebrovascular reactivity (CVR) is of growing interest for evaluating white matter integrity. However, even under healthy conditions, the white matter BOLD-CVR response differs notably from that observed in the gray matter. In addition to actual arterial vascular control, the venous draining topology may influence the WM-CVR response leading to signal delays and dispersions. These types of alterations in hemodynamic parameters are sometimes linked with pathology, but may also arise from differences in normal venous architecture. In this work, high-resolution T2*weighted anatomical images combined with BOLD imaging during a hypercapnic breathing protocol were acquired using a 7 tesla MRI system. Hemodynamic parameters including base CVR, hemodynamic lag, lag-corrected CVR, response onset and signal dispersion, and finally ΔCVR (corrected CVR minus base CVR) were calculated in 8 subjects. Parameter maps were spatially normalized and correlated against an MNI-registered white matter medullary vein atlas. Moderate correlations (Pearson’s rho) were observed between medullary vessel frequency (MVF) and ΔCVR (0.52; 0.58 for total WM), MVF and hemodynamic lag (0.42; 0.54 for total WM), MVF and signal dispersion (0.44; 0.53 for total WM), and finally MVF and signal onset (0.43; 0.52 for total WM). Results indicate that, when assessed in the context of the WM venous architecture, changes in the response shape may only be partially reflective of the actual vascular reactivity response occurring further upstream by control vessels. This finding may have implications when attributing diseases mechanisms and/or progression to presumed impaired WM BOLD-CVR.
Improve consensus partitioning via a hierarchical procedure
Authors: Gu Zuguang , Hübschmann Daniel 
Published: September 2021

AbstractConsensus partitioning is an unsupervised method widely used in high throughput data analysis for revealing subgroups and assigns stability for the classification. However, standard consensus partitioning procedures are weak to identify large numbers of stable subgroups. There are two main issues. 1. Subgroups with small differences are difficult to separate if they are simultaneously detected with subgroups with large differences. And 2. stability of classification generally decreases as the number of subgroups increases. In this work, we proposed a new strategy to solve these two issues by applying consensus partitionings in a hierarchical procedure. We demonstrated hierarchical consensus partitioning can be efficient to reveal more subgroups. We also tested the performance of hierarchical consensus partitioning on revealing a great number of subgroups with a DNA methylation dataset. The hierarchical consensus partitioning is implemented in the R package cola with comprehensive functionality for analysis and visualizations. It can also automate the analysis only with a minimum of two lines of code, which generates a detailed HTML report containing the complete analysis.
Forest wildflowers bloom earlier as Europe warms – but not everywhere equally
Authors: Willems Franziska M. , Scheepens J. F. , Bossdorf Oliver 
Published: September 2021

AbstractSome of the most striking biological responses to climate change are the observed shifts in the timing of life-history events of many organisms. Plants, in particular, often flower earlier in response to climate warming, and herbarium specimens are excellent witnesses of such long-term changes. However, in large-scale analyses the magnitude of phenological shifts may vary geographically, and the data are often clustered, and it is thus necessary to account for spatial correlation to avoid geographical biases and pseudoreplication. Here, we analysed herbarium specimens of 20 spring-flowering forest understory herbs to estimate how their flowering phenology shifted across Europe during the last century. Our analyses show that on average these forest wildflowers now bloom over six days earlier than at the beginning of the last century. These changes were strongly associated with warmer spring temperatures. Plants flowered on average of 3.6 days earlier per 1°C warming. However, in some parts of Europe plants flowered earlier or later than expected. This means, there was significant residual spatial variation in flowering time across Europe, even after accounting for the effects of temperature, precipitation, elevation and year. Including this spatial autocorrelation into our statistical models significantly improved model fit and reduced bias in coefficient estimates. Our study indicates that forest wildflowers in Europe strongly advanced their phenology in response to climate change during the last century, with potential severe consequences for their associated ecological communities. It also demonstrates the power of combining herbarium data with spatial modelling when testing for long-term phenology trends across large spatial scales.
ProteinPrompt: a webserver for predicting protein-protein interactions
Authors: Canzler Sebastian , Fischer Markus , Ulbricht David , Ristic Nikola , Hildebrand Peter W. , Staritzbichler René 
Published: September 2021

AbstractMotivationProtein-protein interactions play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side-effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine-learning algorithms to calculate specific, currently unknown protein-protein interactions. Our tool is designed to quickly and reliably predict contacts based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.MethodsWe collected and thoroughly filtered a comprehensive database of known contacts from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for contacts.ResultsTo evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the random forest method achieved an accuracy rate of 0.88 and an area under curve of 0.95. The graph neural network achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of random forest and graph neural networks reduces the likelihood of errors. The consensus reached an accuracy of 0.89. ProteinPrompt is available online at: http://proteinformatics.org/ProteinPromptThe server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/Proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.
DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: an in silico strategy towards precision oncology
Authors: Lu Xiaofan , Meng Jialin , Zhou Yujie , Wang Haitao , Ruan Xinjia , Chen Yi , Ye Yuqing , Su Liwen , Fan Xiaole , Yan Hangyu , Jiang Liyun , Yan Fangrong 
Published: September 2021

ABSTRACTBackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs.MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk.ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an ‘immune-cold’ phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS.ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.
Network Modeling Predicts Personalized Gene Expression and Drug Responses in Valve Myofibroblasts Cultured with Patient Sera
Authors: Rogers Jesse D. , Aguado Brian A. , Watts Kelsey M. , Anseth Kristi S. , Richardson William J. 
Published: September 2021

AbstractAortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals are transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor β (TGFβ) along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in VICs cultured with AVS patient sera and treated with or without bosentan - a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.
Spectrum graph-based de-novo sequencing algorithm MaxNovo achieves high peptide identification rates in collisional dissociation MS/MS spectra
Authors: Gutenbrunner Petra , Kyriakidou Pelagia , Welker Frido , Cox Jürgen 
Published: September 2021

AbstractWe describe MaxNovo, a novel spectrum graph-based peptide de-novo sequencing algorithm integrated into the MaxQuant software. It identifies complete sequences of peptides as well as sequence tags that are incomplete at one or both of the peptide termini. MaxNovo searches for the highest-scoring path in a directed acyclic graph representing the MS/MS spectrum with peaks as nodes and edges as potential sequence constituents consisting of single amino acids or pairs. The raw score is a sum of node and edge weights, plus several reward scores, for instance, for complementary ions or protease compatibility. For search-engine identified peptides, it correlates well with the Andromeda search engine score. We use a particular score normalization and the score difference between the first and second-best solution to define a combined score that integrates all available information. To evaluate its performance, we use a human cell line dataset and take as ground truth all Andromeda-identified MS/MS spectra with an Andromeda score of at least 100. MaxNovo outperforms other software in particular in the high-sensitivity range of precision-coverage plots. We also identify incomplete sequence tags and study their statistical properties. Next, we apply MaxNovo to ion mobility-coupled time of flight data. Here we achieve excellent performance as well, except for potential swaps of the two amino acids closest to the C-terminus, which are not well resolved due to the low end of the mass range in MS/MS spectra in this dataset. We demonstrate the applicability of MaxNovo to palaeoproteomics samples with a Late Pleistocene hominin proteome dataset that was generated using three proteases. Interestingly, we did not use any machine learning in the construction of MaxNovo, but implemented expert domain knowledge directly in the definition of the score. Yet, it performs as good as or better than the leading deep learning-based algorithm.