bluuebunny/crossref_metadata_2025_split huggingface dataset:
The role of intraspecific crop-weed hybridization in the evolution of weediness and invasiveness: cultivated and weedy radish (Raphanus sativusL.) as a case study
Authors: Vercellino Román B. , Hernández Fernando , Presotto Alejandro 
Published: March 2023

PREMISECrop-wild/weed hybrids usually exhibit intermediate and maladapted phenotypes compared to their parents; however, hybridization has sometimes been associated with increased fitness, potentially leading to enhanced weediness and invasiveness. Since the ecological context and maternal genetic effects may affect hybrid fitness, they could influence the evolutionary outcomes of hybridization. Here, we evaluated the performance of first-generation crop-weed hybrids ofRaphanus sativusL. and their parents under two contrasting ecological conditions.METHODSUsing experimental hybridization and common garden experiments under field conditions, we assessed the differences in time to flowering, survival to maturity, plant biomass and reproductive components between bidirectional crop-weed hybrids and their parents, under two contrasting ecological conditions, agrestal (wheat cultivation, fertilization, weeding) and ruderal (human-disturbed uncultivated area) over two years.RESULTSCrop, weeds and bidirectional hybrids overlapped -at least partially-during the flowering period, indicating a high probability of gene flow. Hybrids survived to maturity at rates at least as successful as their parents and showed higher plant biomass and fecundity, which resulted in higher fitness compared to their parents in both contrasting environments, without any differences associated with the direction of the hybridization.CONCLUSIONSIntraspecific crop-weed hybridization, regardless of the cross direction, has the potential to promote weediness in weedyR. sativusboth in agrestal and ruderal environments, increasing the chances of the introgression of crop alleles into weed populations. This is the first report of intraspecific crop-weed hybridization inR. sativus.
Protein nonadditive expression and solubility inArabidopsishybrids and allotetraploids
Authors: June Viviana , Xu Dongqing , Papoulas Ophelia , Boutz Daniel , Marcotte Edward M. , Chen Z. Jeffrey 
Published: March 2023

AbstractInterspecific hybridization in plants often leads to allopolyploids including most important crops such as wheat, cotton, and canola, while many other crops such as corn and sorghum are grown as hybrid. Both allopolyploids and hybrids show hybrid vigor or heterosis. The phenomenon has been widely applied in agriculture and extensively studied at the genetic and gene expression levels. However, proteomic changes in hybrids and polyploids remain poorly understood. Here, we report comparative analysis of soluble and insoluble proteomes inArabidopsisintraspecific and interspecific hybrids or allotetraploids formed betweenA. thalianaandA. arenosa. Both allotetraploids and intraspecific hybrids displayed nonadditive expression (unequal to the sum of the two parents) of the proteins, most of which were involved in biotic and abiotic stress responses. In the allotetraploids, homoeolog-expression bias was not observed among all proteins examined but could occur among 17-20% of the nonadditively expressed proteins, consistent with the transcriptome results. Among expression-biased homoeologs, there were moreA. thaliana-biased thanA. arenosa-biased homoeologs,. Analysis of the insoluble and soluble proteomes revealed more soluble proteins in the hybrids than their parents but not in the allotetraploids. Most proteins in ribosomal biosynthesis and in the thylakoid lumen, membrane, and stroma were in the soluble fractions, indicating a role of protein stability in photosynthetic activities for promoting growth. These results collectively support roles of nonadditive expression of stress-responsive proteins and increased solubility of photosynthetic proteins inArabidopsishybrids and allotetraploids, which may promote hybrid vigor.Plain Language SummaryWe analyzed fractionated proteomes to test protein abundance and solubility inArabidopsishybrids and polyploids. Many proteins in stress-responses are nonadditively expressed in intraspecific hybrids or allotetraploids. There are more soluble proteins of ribosomal biosynthesis and photosynthetic activities in the hybrids than in their parents but not in allotetraploids. Expression levels are equal among most protein homoeologs in the allotetraploids but are biased for ~20% of nonadditively expressed proteins. Nonadditive protein expression and solubility may play a role in heterosis.
Differential Effects of Cocaine and Morphine on the Diurnal Regulation of the Mouse Nucleus Accumbens Proteome
Authors: Ketchesin Kyle D. , Becker-Krail Darius D. , Xue Xiangning , Wilson Rashaun S. , Lam TuKiet T. , Williams Kenneth R. , Nairn Angus C. , Tseng George C. , Logan Ryan W. 
Published: March 2023

ABSTRACTSubstance use disorders (SUDs) are associated with disruptions in sleep and circadian rhythms that persist during abstinence and may contribute to relapse risk. Repeated use of substances such as psychostimulants and opioids may lead to significant alterations in molecular rhythms in the nucleus accumbens (NAc), a brain region central to reward and motivation. Previous studies have identified rhythm alterations in the transcriptome of the NAc and other brain regions following the administration of psychostimulants or opioids. However, little is known about the impact of substance use on the diurnal rhythms of the proteome in the NAc. We used liquid chromatography coupled to tandem mass spectrometry-based (LC-MS/MS) quantitative proteomics, along with a data-independent acquisition (DIA) analysis pipeline, to investigate the effects of cocaine or morphine administration on diurnal rhythms of proteome in the mouse NAc. Overall, our data reveals cocaine and morphine differentially alters diurnal rhythms of the proteome in the NAc, with largely independent differentially expressed proteins dependent on time-of-day. Pathways enriched from cocaine altered protein rhythms were primarily associated with glucocorticoid signaling and metabolism, whereas morphine was associated with neuroinflammation. Collectively, these findings are the first to characterize the diurnal regulation of the NAc proteome and demonstrate a novel relationship between phase-dependent regulation of protein expression and the differential effects of cocaine and morphine on the NAc proteome.
Cis-regulatory evolution that caused change inwinglessexpression pattern associated with wing pigmentation pattern ofDrosophila
Authors: Karasawa Takumi , Saito Namiho , Koshikawa Shigeyuki 
Published: March 2023

AbstractGenetic mechanisms underlying the acquisition of new traits are an important topic in evolutionary developmental biology. Especially, the co-option of important regulatory genes potentially plays an important role in the gain of new traits. However, how the co-option occurs at the sequence level is still elusive.Drosophila guttiferahas a unique wing pigmentation pattern and this is newly gained via the evolution of the expression pattern ofwingless, which induces the pigmentation pattern formation. In this study, to reveal the changes in thecis-regulatory sequence which caused the co-option ofwinglessthat lead to the expression in a new place, we conducted transgenic EGFP reporter assays of alteredcis-regulatory sequences. As a result, the sequence was divided into regions needed to activate expression in the entire wing veins and a region required for repressing expression in excess parts. Comparisons with the homologous sequence ofDrosophila melanogastershowed that the repressive function of thecis-regulatory region is also possessed byD. melanogasterwhile the activating function is newly gained in a lineage leading toD. guttifera. Furthermore, a putative binding site of SMAD transcription factors is shown to be essential for activating expression but also existing in the homologous region ofD. melanogaster. Our results suggest that the pre-existing regulatory sequences in thecis-regulatory region coordinate with the newly gained sequences to acquire the new expression pattern ofwingless.Graphical Abstract
Bayesian Inference of a Spectral Graph Model for Brain Oscillations
Authors: Jin Huaqing , Verma Parul , Jiang Fei , Nagarajan Srikantan , Raj Ashish 
Published: March 2023

AbstractThe relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.
Spatial cancer systems biology resolves heterotypic interactions and identifies disruption of spatial hierarchy as a pathological driver event
Authors: Filipp Fabian V. 
Published: March 2023

AbstractSpatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity (PCP) is the organization of epithelial cells along the planar axis, orthogonal to the apical-basal axis. Here, we investigate PCP factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive a gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent PCP factors such as specific representatives of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion.
Exploring neural tracking of acoustic and linguistic speech representations in individuals with post-stroke aphasia
Authors: Kries Jill , Clercq Pieter De , Gillis Marlies , Vanthornhout Jonas , Lemmens Robin , Francart Tom , Vandermosten Maaike 
Published: March 2023

AbstractAphasia is a communication disorder that affects processing of language at different levels (e.g., acoustic, phonological, semantic). Recording brain activity via EEG while people listen to a continuous story allows to analyze brain responses to acoustic and linguistic properties of speech. When the neural activity aligns with these speech properties, it is referred to as neural tracking. Even though measuring neural tracking of speech may present an interesting approach to studying aphasia in an ecologically valid way, it has not yet been investigated in individuals with stroke-induced aphasia. Here, we explored processing of acoustic and linguistic speech representations in individuals with aphasia in the chronic phase after stroke and age-matched healthy controls. We found decreased neural tracking of acoustic speech representations (envelope and envelope onsets) in individuals with aphasia. In addition, word surprisal displayed decreased amplitudes in individuals with aphasia around 195 ms over frontal electrodes, although this effect was not corrected for multiple comparisons. These results show that there is potential to capture language processing impairments in individuals with aphasia by measuring neural tracking of continuous speech. However, more research is needed to validate these results. Nonetheless, this exploratory study shows that neural tracking of naturalistic, continuous speech presents a powerful approach to studying aphasia.Key pointsIndividuals with aphasia display decreased encoding of acoustic speech properties (envelope and its onsets) in comparison to healthy controls.Neural responses to word surprisal reveal decreased amplitudes in individuals with aphasia around 195 ms processing time (not corrected for multiple comparisons).Neural tracking of natural speech can be used to study speech processing impairments in aphasia.
Pairing Explainable Deep Learning Classification with Clustering to Uncover Effects of Schizophrenia Upon Whole Brain Functional Network Connectivity Dynamics
Authors: Ellis Charles A. , Miller Robyn L. , Calhoun Vince D. 
Published: March 2023

ABSTRACTMany studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.
The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts
Authors: Nelson Mark C. , Royer Jessica , Leppert Ilana R. , Campbell Jennifer S.W. , Schiavi Simona , Jin Hyerang , Tavakol Shahin , de Wael Reinder Vos , Rodriguez-Cruces Raul , Pike G. Bruce , Bernhardt Boris C. , Daducci Alessandro , Misic Bratislav , Tardif Christine L. 
Published: March 2023

ABSTRACTA central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models supportin vivoinvestigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain.AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.
Slitflow: a Python framework for single-molecule dynamics and localization analysis
Authors: Ito Yuma , Hirose Masanori , Tokunaga Makio 
Published: March 2023

AbstractSingle-molecule imaging is a promising method for direct quantification of the dynamics and distribution of biomolecules in living cells. Although numerous methods have been developed to gain biological insights into molecular behavior, the high diversity of microscopes and single-molecule dynamics can result in incomplete reproducibility of analyses. Here, we present Slitflow, an open-source framework for a single-molecule analysis workflow that includes image processing, dynamics analysis, and figure creation. We demonstrated the integrity and flexibility of the workflow using 1) a cherry-picked tracking method combining popular tools and 2) various state-of-the-art analyses in a single pipeline. The software accommodates a large variety of data and methods, paving the way for integrative analyses.Code metadata
Sexual antagonism in sequential hermaphrodites
Authors: Hitchcock Thomas J. , Gardner Andy 
Published: March 2023

AbstractFemales and males may have distinct phenotypic optima, but share essentially the same complement of genes, potentially leading to trade-offs between attaining high fitness through female versus male reproductive success. Such sexual antagonism may be particularly acute in hermaphrodites, whereby both reproductive strategies are housed within a single individual. Whilst previous models have focused on simultaneous hermaphroditism, we lack theory for how sexual antagonism may play out under sequential hermaphroditism, which have the additional complexities of age-structure. Here we develop a formal theory of sexual antagonism in sequential hermaphrodites. First, we develop a general theoretical overview of the problem, then consider different types of sexually antagonistic and life-history trade-offs, under different modes of genetic inheritance (autosomal or cytoplasmic), and different forms of sequential hermaphroditism (protogynous, protoandrous or bi-directional). Finally, we provide a concrete illustration of these general patterns by developing a two-stage two-sex model, which yields conditions for both invasion of sexually antagonistic alleles and maintenance of sexually antagonistic polymorphisms.
Weather variation in objectively measured physical activity: results of four Brazilian cohorts
Authors: Martins Rafaela Costa , Blumenberg Cauane , Wendt Andrea T , de Andrade Müller Werner , Santos Iná S , Matijasevich Alicia , Domingues Marlos , Bertoldi Andréa D , Gonçalves Helen , Wehrmeister Fernando C. , Reichert Felipe Fossati 
Published: March 2023

ABSTRACTBackgroundThe aim of the present study was to examine the relationship of objectively measured physical activity and weather variables in different stages of life course.MethodsThis study used data from four Brazilian cohorts (mean ages: 10.9, 22.6, 30.2, and 70.5 years). The exposure was weather variation, using temperature, rain, humidity, and wind velocity. The outcome measured was accelerometer based physical activity in three ways: overall, 5-minute bouts, and 10-minute bouts physical activity. Both exposure and outcome were collected from the same period. Crude and adjusted analyses were fitted using a two-part model.ResultsAmong 9,966 individuals from different ages evaluated, physical activity levels were low, especially in older adults. Rain and mean wind velocity were not associated physical activity of individuals, however, the probability of children being inactive was higher during humid days. After mutually adjusting the models for weather variables, only mean temperature and humidity were associated with higher and lower physical activity levels in children, respectively.ConclusionsWeather variables are not key indicators to be accounted when modelling physical activity studies in almost all ages. However, this study helps to identify specifically exposures, regardless of the physical activity operationalization.