bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of online tDCS and hf-tRNS on reading performance in children and adolescents with developmental dyslexia: a study protocol for a within-subject, randomized, double-blind, and sham-controlled trial
Authors: Battisti Andrea , Lazzaro Giulia , Varuzza Cristiana , Vicari Stefano , Menghini Deny 
Published: July 2023

ABSTRACTBackgroundDevelopmental Dyslexia (DD) is a brain-based developmental disorder characterized by severe impairments in the acquisition of fluent and/or accurate reading. The extensive data on the neurobiology of DD have increased interest inbrain-directedapproaches. Transcranial direct current stimulation (tDCS) has been proposed as a non-invasive method to address reading difficulties in individuals with DD. While overall positive outcomes have been observed, the results remain heterogeneous. To enhance the current findings, various modalities have been employed, including manipulation of electrode montages, experimental designs, and targeting different brain regions. However, no studies have yet investigated the comparative effects of two different types of transcranial electrical stimulation, namely tDCS and transcranial random noise stimulation (tRNS), on reading abilities in children and adolescents with DD.MethodsThe present study will apply a within-subject, randomized, double-blind, and sham-controlled design. The aim of the present study is to investigate the effects of tDCS and tRNS on reading abilities in children and adolescents with DD. Participants will undergo three conditions, each separated by a one-week interval: (A) a single session of active tDCS; (B) a single session of active tRNS; and (C) a single session of sham (tDCS or sham tRNS). The order of the sessions will be counter-matched among participants. Left anodal/right cathodal tDCS and bilateral tRNS will be applied over the temporo-parietal regions for a duration of 20 minutes. The assessment of text, word, and non-word reading measures will be conducted immediately before and during each stimulation sessions. Safety, tolerability and blinding parameters will be assessed using a questionnaire.ResultsOur hypothesis is that tRNS will demonstrate comparable effectiveness to tDCS in improving text, word, and non-word reading measures compared to the sham conditions. Additionally, we anticipate that tRNS will exhibit a similar safety profile to tDCS.ConclusionThis study has the potential to contribute novel insights into the effectiveness of tRNS, a newly-wave tES method that has not yet been explored in children and adolescents with DD. Furthermore, findings may lay the groundwork for further investigations involving multiple tRNS sessions.Trial registrationThe study has been registered withClinicalTrials.govunder the identifierNCT05832060.
Does larval ability to modulate body buoyancy explain successful colonization of freshwater environments by diadromous gobies?
Authors: Oto Yumeki , Watanabe Katsutoshi 
Published: July 2023

AbstractSalinity is an environmental factor that strongly characterizes the habitat use patterns of aquatic organisms. However, knowledge is biased toward the effect of differences in osmotic pressure among salinity habitats; how ambient specific gravity (SG) differences determine species distribution is scarcely understood. Diadromous fish, which migrate between marine and freshwater habitats, may encounter this SG problem when they are unexpectedly landlocked in or colonize freshwater areas with low environmental SG. This is particularly serious for planktonic larval fish, which must maintain neutral buoyancy for foraging and passive locomotion, although their swimbladders are generally underdeveloped. Then, we hypothesized that the SG problem limits the establishment of freshwater resident populations in marine-originated diadromous fishes. To test this hypothesis, the SG modulation ability of newly hatched larvae was compared among three closely related diadromous goby species inGymnogobius, one of which has freshwater resident populations. The aquarium experimental results did not support that only the species deriving freshwater residents can maintain neutral buoyancy even in freshwater conditions; that is, all three species could modulate their body SG almost equally to those of both fresh and sea waters. This suggests that the ability to maintain neutral buoyancy in freshwater had been pre-adaptively acquired prior to larval freshwater colonization. On the other hand, it is highly noteworthy that the early larvae of the target group maintained neutral buoyancy in various SG environments using swimbladders, which is the first such evidence in teleosts.
Computational prediction of protein interactions on single cells by proximity sequencing
Authors: Xia Junjie , Van Phan Hoang , Vistain Luke , Chen Mengjie , Khan Aly A. , Tay Savaş 
Published: July 2023

AbstractProximity sequencing (Prox-seq) measures gene expression, protein expression, and protein complexes at the single cell level, using information from dual-antibody binding events and a single cell sequencing readout. Prox-seq provides multi-dimensional phenotyping of single cells and was recently used to track the formation of receptor complexes during inflammatory signaling in macrophages and to discover a new interaction between CD9/CD8 proteins on naïve T cells. The distribution of protein abundance affects identification of protein complexes in a complicated manner in dual-binding assays like Prox-seq. These effects are difficult to explore with experiments, yet important for accurate quantification of protein complexes. Here, we introduce a physical model for protein dimer formation on single cells and computationally evaluate several different methods for reducing background noise when quantifying protein complexes. Furthermore, we developed an improved method for analysis of Prox-seq single-cell data, which resulted in more accurate and robust quantification of protein complexes. Finally, our model offers a simple way to investigate the behavior of Prox-seq under various biological conditions and guide users toward selecting the best analysis method for their data.
Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients
Authors: Fontana Diletta , Crespiatico Ilaria , Crippa Valentina , Malighetti Federica , Villa Matteo , Angaroni Fabrizio , Sano Luca De , Aroldi Andrea , Antoniotti Marco , Caravagna Giulio , Piazza Rocco , Graudenzi Alex , Mologni Luca , Ramazzotti Daniele 
Published: July 2023

AbstractBy leveraging the ever-increasing availability of cancer omics data and the continuous advances in cancer data science and machine learning, we have discovered the existence of cancer type-specificevolutionary signaturesassociated with different disease outcomes. These signatures represent “favored trajectories” of acquisition of driver mutations that are repeatedly detected in patients with similar prognosis. In this work, we present a novel framework named ASCETIC (Agony-baSedCancerEvoluTion InferenCe) that extracts such signatures from NGS experiments generated by different technologies such as bulk and single-cell sequencing data. In our study, we applied ASCETIC to (i) single-cell sequencing data from 146 patients with distinct myeloid malignancies and bulk whole-exome sequencing data from 366 acute myeloid leukemia patients, (ii) multi-region sequencing data from 100 early-stage lung cancer patients from the TRACERx project, (iii) whole-exome/genome sequencing data from more than 10,000 Pan-Cancer Atlas samples, and (iv) targeted bulk sequencing data from more than 25,000 MSK-MET metastatic patients (both datasets including multiple cancer types). As a result, we extracted different cancer (sub)type-specific single-nucleotide variants evolutionary signatures associated with clusters of patients with statistically significant different prognoses. In addition, we conducted several validations using diverse and previously unexplored datasets to evaluate the reliability and applicability of the evolutionary signatures extracted by ASCETIC. Such analyses provided evidence of the robustness and generalizability of the identified evolutionary patterns.
An Optimized Pipeline for Detection of Salmonella Sequences in Shotgun Metagenomics Datasets
Authors: Bradford Lauren M. , Carrillo Catherine , Wong Alex 
Published: July 2023

AbstractBackgroundCulture-independent diagnostic tests (CIDTs) are gaining popularity as tools for detecting pathogens in food. Shotgun sequencing holds substantial promise for food testing as it provides abundant information on microbial communities, but the challenge is in analyzing large and complex sequencing datasets with a high degree of both sensitivity and specificity. Falsely classifying sequencing reads as originating from pathogens can lead to unnecessary food recalls or production shutdowns, while low sensitivity resulting in false negatives could lead to preventable illness.ResultsWe have developed a bioinformatic pipeline for identifyingSalmonellaas a model pathogen in metagenomic datasets with very high sensitivity and specificity. We tested this pipeline on mock communities of closely related bacteria and with simulatedSalmonellareads added to published metagenomic datasets.Salmonella-derived reads could be found at very low abundances (high sensitivity) without false positives (high specificity). Carefully considering software parameters and database choices is essential to avoiding false positive sample calls. With well-chosen parameters plus additional steps to confirm the taxonomic origin of reads, it is possible to detect pathogens with very high specificity and sensitivity.
Emergence of selectivity and specificity in a coarse-grained model of the nuclear pore complex with sequence-agnostic FG-Nups
Authors: Patel Manoj Kumar , Chakrabarti Buddhapriya , Panwar Ajay Singh 
Published: July 2023

ABSTRACTThe role of hydrophobicity of phenylalanine-glycine nucleoporins (FG-Nups) in determining transport of receptor-bound cargo across the nuclear pore complex (NPC) is investigated using Langevin dynamics simulations. A coarse-grained, minimal model of the NPC, comprising a cylindrical pore and hydrophobic-hydrophilic random copolymers for FG-Nups was employed. Karyopherin-bound receptor-cargo complexes (Kaps) were modeled as rigid, coarse-grained spheres without (inert) and with (patchy) FG-binding hydrophobic domains. With a sequence-agnostic description of FG-Nups and the absence of any anisotropies associated with either NPC or cargo, the model described tracer transport only as a function of FG-Nup hydrophobicity,f. The simulations showed the emergence of two important features of cargo transport, namely, NPC selectivity and specificity. NPC selectivity to patchy tracers emerged due to hydrophobic Kap-FG interactions and despite the sequence-agnostic description of FG-Nups. Further, NPC selectivity was observed only in a specific range of FG-hydrophobic fraction, 0.05 ≤f≤ 0.20, resulting in specificity of NPC transport with respect tof. Significantly, this range corresponded to the number fraction of FG-repeats observed in bothS. cerevisiaeandH. sapiensNPCs. This established the centrality of FG-hydrophobic fraction in determining NPC transport, and provided a biophysical basis for conservation of FG-Nup hydrophobic fraction across evolutionarily distant NPCs. Specificity in NPC transport emerged from the formation of a hydrogel-like network inside the pore with a characteristic mesh size dependent onf. This network rejected cargo forf> 0.2 based on size exclusion which resulted in an enhanced translocation probability for 0.05 ≤f≤ 0.20. Thus, the NPC model provides a template for designing synthetic, biomimetic nanopores for macromolecular separations with high degrees of selectivity and specificity.
einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data
Authors: Soneson Charlotte , Iesmantavicius Vytautas , Hess Daniel , Stadler Michael B , Seebacher Jan 
Published: July 2023

ABSTRACTWe describe einprot, an R package providing easy-to-use reproducible workflows for quality control, statistical analysis and visualization of quantitative proteomics data. einprot is applicable to tabular output from MaxQuant, Proteome Discoverer and FragPipe, and a single function call generates an html report that describes the full analysis pipeline applied to the data and contains static and interactive figures and tables for further exploration. This has the potential to facilitate routine analyses as well as to provide a standardized, yet comprehensive way to communicate results to collaborators and the broader community. The source file underlying the report is also returned, giving the user full flexibility to further modify the workflow according to their needs.
Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry
Authors: Ellin Nicholas R. , Miranda-Quintana Ramón Alain , Prentice Boone M. 
Published: July 2023

ABSTRACTImaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampledx,yposition, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discretem/zvalues that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
Modeling time-varying phytoplankton subsidy reveals at-risk species in a Chilean intertidal ecosystem
Authors: Duckwall Casey S. , Largier John L. , Wieters Evie A. , Valdovinos Fernanda S. 
Published: July 2023

AbstractThe allometric trophic network (ATN) framework for modeling population dynamics has provided numerous insights into ecosystem functioning in recent years. Herein we extend ATN modeling of the intertidal ecosystem off the central coast of Chile to include empirical data on pelagic chlorophyll-a concentration. This intertidal community requires subsidy of primary productivity to support its rich ecosystem. Previous work models this subsidy using a constant rate of phytoplankton input to the system. However, data shows pelagic subsidies exhibit short-term, highly variable, pulse-like behavior. Incorporating this variable input into ATN modeling is the primary contribution of this work and provides several new insights into this ecosystem’s response to pulses of offshore phytoplankton, including: (1) closely related sea snails show differential responses to pulses of phytoplankton that are explained by underlying network structure; (2) increasing the rate of pelagic-intertidal mixing increases fluctuations in species’ biomasses that may increase the risk of local extirpation; (3) predators are the most sensitive species to phytoplankton biomass fluctuations, putting these species at greater risk of extirpation than others. Finally, our work provides a straightforward way to incorporate empirical, time-series data into the ATN framework that will expand this powerful methodology to new applications.
Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro
Authors: Zhang Wenmin , Sladek Robert , Li Yue , Najafabadi Hamed S. , Dupuis Josée 
Published: July 2023

AbstractBackgroundCharacterizing genetic effect heterogeneity across subpopulations with different environmental exposures is useful for identifying exposure-specific pathways, understanding biological mechanisms underlying disease heterogeneity and further pinpointing modifiable risk factors for disease prevention and management. Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity. However, it can have a high multiple testing burden in the context of genome-wide association studies (GWAS) and requires a large sample size to achieve sufficient power.MethodsWe adapt a colocalization method, SharePro, to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. Through joint fine-mapping of exposure stratified GWAS summary statistics, SharePro can greatly reduce multiple testing burden in GxE analysis.ResultsThrough extensive simulation studies, we demonstrated that accounting for effect heterogeneity can improve power for fine-mapping. With efficient joint fine-mapping of exposure stratified GWAS summary statistics, SharePro alleviated multiple testing burden in GxE analysis and demonstrated improved power with well-controlled false discovery rate. Through analyses of smoking status stratified GWAS summary statistics, we identified genetic effects on lung function modulated by smoking status mapped to the genesCHRNA3,ADAM19andUBR1. Additionally, using sex stratified GWAS summary statistics, we characterized sex differentiated genetic effects on fat distribution and provided biologically plausible candidates for functional follow-up studies.ConclusionsWe have developed an analytical framework to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available athttps://github.com/zhwm/SharePro_gxe.
Functional traits fail to predict long-term responses to climate change
Authors: Ash Jeremy D. , Li Daijiang , Johnson Sarah E. , Rogers David A. , Waller Donald M. 
Published: July 2023

AbstractAimEnvironmental conditions strongly affect the distribution and abundance of species via complex forces. Shifts in environmental conditions and differences in the speed and scale of these effects complicate our efforts to infer how species will respond to future environmental change. We test how 18 functional traits affect plant species responses to gradients in environmental conditions and 50-year shifts in climate.LocationWe analyzed 50-year shifts in the distribution and abundance of 153 plant species distributed across 284 sites in Wisconsin, USA.Time period1950s to 2000s.Major taxa studiedVascular plants (much of the flora of NE North America).MethodsWe used random forest and integrated hierarchical mixed models to test how plant abundances (and 50-year changes in abundance) track gradients in overstory, soil, and climatic conditions.ResultsWithin study periods, plant abundances reflect gradients in environmental conditions. Leaf traits affected local abundance (both directly and via trait-environment interactions) in the 1950s and 2000s. Strong soil and temperature effects in the 1950s have weakened while precipitation effects have strengthened. Although we expected these models to also predict how plants would respond to shifts in climate, they did not.Main conclusionsLags in species’ responses, increases in the stochastic forces affecting community assembly, and other forces limit the ability of models fitted to static data (e.g., space-for-time substitutions) to predict how plant species will respond to long-term shifts in environmental conditions. We must therefore be cautious about applying trait-based species distribution models to predict how climate change will affect species distributions and community structure.
Widespread deviant patterns of heterozygosity in whole-genome sequencing due to autopolyploidy, repeated elements, and duplication
Authors: Dallaire Xavier , Bouchard Raphael , Hénault Philippe , Ulmo-Diaz Gabriela , Normandeau Eric , Mérot Claire , Bernatchez Louis , Moore Jean-Sébastien 
Published: July 2023

AbstractMost population genomic tools rely on accurate SNP calling and filtering to meet their underlying assumptions. However, genomic complexity, due to structural variants, paralogous sequences and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing data from four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata), with low (2X) to intermediate (6X) coverage. The analyses revealed that deviant SNPs accounted for up to 62% of all SNPs in salmonid datasets and approximately 10% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs and could be partially attributed to transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, apparent homogenization of populations and underestimating pairwise FSTvalues. Considering the widespread occurrence of deviant SNPs arising from a variety of source, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant-SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths.Significance: Genomes can be very repetitive and hard to assemble into a reference, which can lead to biases when genotyping genetic markers in complex genomic regions. Here, we draw attention to this issue in various whole-genome datasets and validate a method to identify problematic SNPs at low coverage. We also explore processes creating such SNPs and their consequences on common population genomics analyses.