bluuebunny/crossref_metadata_2025_split huggingface dataset:
MTL neurons phase-lock to human hippocampal theta
Authors: Schonhaut Daniel R. , Ramayya Ashwin G. , Solomon Ethan A. , Herweg Nora A. , Fried Itzhak , Kahana Michael J. 
Published: December 2022

AbstractMemory formation depends on neural activity across a network of regions, including the hippocampus and broader medial temporal lobe (MTL). Interactions between these regions have been studied indirectly using functional MRI, but the bases for inter-regional communication at a cellular level remain poorly understood. Here we evaluate the hypothesis that oscillatory currents in the hippocampus synchronize the firing of neurons both within and outside the hippocampus. We recorded extracellular spikes from 1,854 single- and multi-units simultaneously with hippocampal local field potentials (LFPs) in 28 neurosurgical patients who completed virtual navigation experiments. A majority of hippocampal neurons phase-locked to oscillations in the slow (2-4Hz) or fast (6-10Hz) theta bands, with a significant subset exhibiting nested slow theta × beta frequency (13-20Hz) phase-locking. Outside of the hippocampus, phase-locking to hippocampal oscillations occurred only at theta frequencies and primarily among neurons in the entorhinal cortex and amygdala. Extrahippocampal neurons phase-locked to hippocampal theta even when theta did not appear locally. These results indicate that spike-time synchronization with hippocampal theta is a defining feature of neuronal activity in the hippocampus and structurally-connected MTL regions. Theta phase-locking could mediate flexible communication with the hippocampus to influence the content and quality of memories.
A Re-evaluation of Phylogenomic Data Reveals that Current Understanding in Wheat Blast Population Biology and Epidemiology is Obfuscated by Oversights in Population Sampling
Authors: Farman Mark L. , Ascari Joao P. , Rahnama Mostafa , Del Ponte Emerson M. , Pedley Kerry F. , Martinez Sebastián , Fernandes José Maurício C. , Valent Barbara 
Published: December 2022

ABSTRACTWheat blast, caused by theTriticumlineage ofPyricularia oryzae(PoT), is a serious disease that first emerged in Brazil and quickly spread to neighboring countries. The recent appearance of this disease in Bangladesh and Zambia highlights a need to understand the population biology and epidemiology of the disease so as to mitigate pandemic outbreaks. Current knowledge in these areas is largely based on analyses of wheat blast isolates collected in Brazil, and their comparison with isolates from non-wheat, endemic grasses. Those studies concluded that wheat blast is caused by a highly diverseP. oryzaepopulation that lacks host specificity and, as a result, undergoes extensive gene flow with populations infecting non-wheat hosts. Additionally, based on genetic similarity between wheat blast and isolates infectingUrochloaspecies, it was proposed that the disease originally emerged via a host jump from this grass, and the widespread use ofUrochloaas a pasture grass likely plays a central role in wheat blast epidemiology. Inconsistencies with earlier phylogenetic studies prompted us to re-analyze the Brazilian data in the context of a comprehensive, global, phylogenomic dataset. We now show that the seminal studies failed to sample theP. oryzaepopulations normally found on endemic grasses and, instead, repeatedly sampled PoT andP. oryzae Lolium(PoL) members that happened to be present in these hosts. The resulting lack of accurate and representative information about the grass-infecting populations in Brazil means that current conclusions about wheat blast’s evolution, population biology and epidemiology are unsubstantiated and could be equivocal.
Heritable epigenetic variation facilitates long-term maintenance of epigenetic and genetic variation
Authors: Webster Amy K. , Phillips Patrick C. 
Published: December 2022

ABSTRACTThe maintenance of genetic and phenotypic variation has long been one of the fundamental questions in population and quantitative genetics. A variety of factors have been implicated to explain the maintenance of genetic variation in some contexts (e.g. balancing selection), but the potential role of epigenetic regulation to influence population dynamics has been understudied. It is well recognized that epigenetic regulation, including histone methylation, small RNA expression, and DNA methylation, helps to define differences between cell types and facilitate phenotypic plasticity. In recent years, empirical studies have shown the potential for epigenetic regulation to also be heritable for at least a few generations without selection, raising the possibility that differences in epigenetic regulation can act alongside genetic variation to shape evolutionary trajectories. Like genetic mutation, heritable differences in epigenetic regulation can arise spontaneously; these are termed ‘epimutations’. Epimutations differ from genetic mutations in two key ways – they occur at a higher rate and the loci at which they occur often revert back to their original state within a few generations. Here, we present an extension of the standard population-genetic model with selection to incorporate epigenetic variation arising via epimutation. Our model assumes a diploid, sexually reproducing population with random mating. In addition to spontaneous genetic mutation, we included parameters for spontaneous epimutation and back-epimutation, allowing for four potential epialleles at a single locus (two genetic alleles, each with two epigenetic states), each of which affect fitness. We then analyzed the conditions under which stable epialleles were maintained. Our results show that highly reversible epialleles can be maintained in long-term equilibrium under neutral conditions in a manner that depends on the epimutation and back-epimutation rates, which we term epimutation-back-epimutation equilibrium. On the other hand, epialleles that compensate for deleterious mutations cause deviations from the expectations of mutation-selection balance by a simple factor that depends on the epimutation and back-epimutation rate. We also numerically analyze several sets of fitness parameters for which large deviations from mutation-selection balance occur. Together, these results demonstrate that transient epigenetic regulation may be an important factor in the maintenance of both epigenetic and genetic variation in populations.
High spontaneous integration rates of end-modified linear DNAs upon mammalian cell transfection
Authors: Lim Samuel , Yocum R. Rogers , Silver Pamela A , Way Jeffrey C 
Published: December 2022

AbstractIn gene therapy, potential integration of therapeutic transgene into host cell genomes is a serious risk that can lead to insertional mutagenesis and tumorigenesis. Viral vectors are often used as the gene delivery vehicle, but they are prone to undergoing integration events. More recently, non-viral delivery of linear DNAs having modified geometry such as closed-end linear duplex DNA (CELiD) have shown promise as an alternative, due to prolonged transgene expression and less cytotoxicity. However, whether such modified-end linear DNAs can also provide a safe, non-integrating gene transfer remains unanswered. Herein, we provide a systematic comparison of genomic integration frequency upon transfection of cells with expression vectors in the forms of circular plasmid, unmodified linear DNA, CELiD, and Streptavidin-conjugated blocked-end linear DNA. All of these forms of linear DNA resulted in a high fraction of the cells being stably transfected – between 10% and 20% of the initially transfected cells, with CELiDs showing the highest rates of integration. These results indicate that blocking the ends of linear DNA is insufficient to prevent integration. Moreover, our analysis suggest that conventional AAV-based gene therapy may be highly susceptible to integration, which is consistent with recent findings from long-term clinical studies.
Larval and adult traits coevolve in response to coastal oceanography to shape marine dispersal kernels
Authors: Peniston James H. , Burgess Scott C. 
Published: December 2022

AbstractDispersal emerges as an outcome of organismal traits and external forcings. However, it remains unclear how the emergent dispersal kernel evolves as a by-product of selection on the underlying traits. This question is particularly compelling in coastal marine systems where dispersal is tied to development and reproduction, and where directional currents bias larval dispersal downstream causing selection for retention. We modelled the dynamics of a metapopulation along a finite coastline using an integral projection model and adaptive dynamics to understand how asymmetric coastal currents influence the evolution of larval (pelagic larval duration) and adult (spawning frequency) life history traits, which indirectly shape the evolution of marine dispersal kernels. Selection induced by alongshore currents favors the release of larvae over multiple time periods, allowing long pelagic larval durations and long-distance dispersal to be maintained in marine life cycles in situations where they were previously predicted to be selected against. Two evolutionary stable strategies emerged: one with a long pelagic larval duration and many spawning events resulting in a dispersal kernel with a larger mean and variance, and another with a short pelagic larval duration and few spawning events resulting in a dispersal kernel with a smaller mean and variance. Our theory shows how coastal ocean flows are important agents of selection that can generate multiple, often co-occurring, evolutionary outcomes for marine life history traits that affect dispersal.
Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing
Authors: Chen Serena H. , Weiss Kevin L. , Stanley Christopher , Bhowmik Debsindhu 
Published: December 2022

AbstractCharacterizing the structural ensembles of intrinsically disordered proteins (IDPs) is essential for studying structure-function relationships as conformational dynamics govern proteins’ biological functions. Due to the notable difference between the neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, the experimental timescale typically results in measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a protein complex formed by two IDP domains, the NCBD/ACTR complex, using data from selective labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. By incorporating structural metrics derived from the SANS experiments as constraints in MD structure classification, we describe a structural ensemble that captures the experimental SANS and, in addition, NMR data better than MD ensembles generated by one single force field. Based on structural similarity, DL reveals three clusters of distinct conformations in the ensemble. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.
Dynamics and maintenance of categorical responses in primary auditory cortex during task engagement
Authors: Chillale Rupesh Kumar , Shamma Shihab , Ostojic Srdjan , Boubenec Yves 
Published: December 2022

SummaryGrouping sets of sounds into relevant categories is an important cognitive ability that facilitates the association of stimuli with appropriate goal-directed behavioral responses. In perceptual tasks, a prominent role of primary auditory cortex (A1) is to multiplex the encoding of sound sensory features and task variables. Here we investigated the involvement of A1 in initiating sound categorization. We trained ferrets to discriminate click trains of different rates in a Go/No-Go delayed categorization task and recorded neural activity during both active behavior and passive exposure to the same sounds. Purely categorical response components were extracted and analyzed separately from sensory responses to reveal their contributions to the overall population response throughout the trials. We found that population-level representation of Go/No-Go behavioral categories emerged during sound presentation and was present in both active behavioral and passive states. However, upon task engagement, categorical responses to the No-Go category became suppressed, leading to an asymmetrical representation of the Go stimuli relative to the No-Go sounds and prestimulus baseline. At stimulus offset, the population code changed abruptly but categorical representation was maintained during the delay period. Categorical responses extracted from the stimulus period correlated with the ones found in the delay epoch, suggesting an early contribution of A1 to stimulus categorization.
Microbiome features differentiating unsupervised-stratification based clusters of patients with abnormal glycometabolism
Authors: Xu Ting , Wang Xuejiao , Chen Yu , Li Hui , Zhao Liping , Ding Xiaoying , Zhang Chenhong 
Published: December 2022

ABSTRACTThe alteration of gut microbiota structure plays a pivotal role in the pathogenesis of abnormal glycometabolism. However, the microbiome features identified in patient groups stratified solely based on glucose levels remain controversial among different studies.. In this study, we stratified 258 participants (discovery cohort) into three clusters according to an unsupervised method based on 16 clinical parameters involving the levels of blood glucose, insulin, and lipid. We found 67 cluster-specific microbiome features (i.e., amplicon sequence variants, ASVs) based on 16S rRNA gene V3-V4 region sequencing. Specifically, ASVs belonging toBarnesvilleandAlistipeswere enriched in Cluster 1, in which participants had the lowest blood glucose levels, high insulin sensitivity, and high-fecal short-chain fatty acid concentration. ASVs belonging toPrevotella copriandRuminococcus gnavuswere enriched in Cluster 2, which was characterized by a moderate level of blood glucose, serious insulin resistance, and high levels of cholesterol and triglyceride. Cluster 3 was characterized by a high level of blood glucose and insulin deficiency, enriched with ASVs inP. copriandBacteroides vulgatus. In addition, machine learning classifiers using the 67 cluster-specific ASVs were used to distinguish individuals in one cluster from those in the other two clusters both in discovery and testing cohorts (N = 83). Therefore, microbiome features identified based on the unsupervised stratification of patients with more inclusive clinical parameters may better reflect microbiota alterations associated with the progression of abnormal glycometabolism.IMPORTANCEThe gut microbiota is altered in patients with type 2 diabetes (T2D) and prediabetes. The association of particular bacteria with T2D, however, varied among studies, which has made it challenging to develop precision medicine approaches for the prevention and alleviation of T2D. Blood glucose level is the only parameter in clustering patients when identifying the T2D-related bacteria in previous studies. This stratification ignores the fact that patients within the same blood glucose range differ in their insulin resistance and dyslipidemia, which also may be related to disordered gut microbiota. In addition to parameters of blood glucose levels, we also used additional parameters involving insulin and lipid levels to stratify participants into three clusters and further identified cluster-specific microbiome features. We further validated the association between these microbiome features and glycometabolism with an independent cohort. This work highlights the importance of stratification of patients with blood glucose, insulin, and lipid levels when identifying the microbiome features associated with the progression of abnormal glycometabolism.
The free energy of an ecosystem: towards a measure of its inner value
Authors: Perillo Gerardo M. E. , Zilio Mariana I. , Tohme Fernando , Piccolo M. Cintia 
Published: December 2022

AbstractBased on a free energy approach, we propose the estimation of ecosystem Inner Value, which is both non-instrumental and objective, reflecting the ecosystem’s value for itself as a natural entity, abstracted from any human valuation activity. The ecosystem services approach has become the dominant criteria for studying the relationship between humans and nature. Although there is concern about preserving and recuperating damaged ecosystems, we seldom consider how much the ecosystem values itself. Then, we propose that Inner Value could be a tool to evaluate and model ecosystems’ health before any anthropic disturbance, allowing comparison with the impact these disturbances may have in the future. We also suggest that it should be a requirement for any Environmental Impact Assessment.Significance StatementEcosystem are valued for the services they provide to human. Therefore, they are estimated following human (mostly economic) criteria. However, any ecosystem has its own value independent of the services it can give. Therefore, we propose the use of the Inner Value concept as a non-instrumental, objective measure reflecting the ecosystem’s value for itself as a natural entity based on a free-energy approach. Inner Value could be a tool to evaluate and model ecosystems’ health before any anthropic disturbance, allowing comparison with the impact these disturbances may have in the future.
Analyzing postprandial metabolomics data using multiway models: A simulation study
Authors: Li Lu , Yan Shi , Bakker Barbara M. , Hoefsloot Huub , Chawes Bo , Horner David , Rasmussen Morten A. , Smilde Age K. , Acar Evrim 
Published: December 2022

AbstractBackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as asubjectsbymetabolitesbytime pointsarray. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postpran-dial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis offasting-statedata using Principal Component Analysis,T0-correcteddata (i.e., data corrected by subtracting fasting-state data) using a CP model andfull-dynamic(i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased vs. healthy groups.ConclusionsOur experiments show that it is crucial to analyze bothfasting-stateandT0-correcteddata for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models ofT0-correctedorfull-dynamicdata. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.
Aberrant hierarchical prediction errors are associated with transition to psychosis: A computational single-trial analysis of the mismatch negativity
Authors: Hauke Daniel J. , Charlton Colleen E. , Schmidt André , Griffiths John , Woods Scott W. , Ford Judith M. , Srihari Vinod H. , Roth Volker , Diaconescu Andreea O. , Mathalon Daniel H. 
Published: December 2022

AbstractBackgroundMismatch negativity (MMN) reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals at clinical high risk for psychosis (CHR-P). Here, we adopt a computational approach to develop a mechanistic model of MMN reductions in CHR-P individuals and patients early in the course of schizophrenia (ESZ).MethodsElectroencephalography (EEG) was recorded in 38 CHR-P individuals (15 converters), 19 ESZ patients (≤5 years), and 44 healthy controls (HC) during three different auditory oddball MMN paradigms including 10% duration-, frequency-, or double-deviants, respectively. We modelled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated EEG amplitudes over sensor space and time.ResultsBoth low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P and ESZ groups compared to HC. Furthermore, low-level precision-weighted prediction errors were significantly different in CHR-P that later converted to psychosis compared to non-converters.ConclusionsOur results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model appears to capture pathophysiological mechanisms relevant to early psychosis and the risk for future psychosis in CHR-P individuals, and may serve as a predictive biomarker and mechanistic target for novel treatment development.
Impact of Age, Race, and Family History on COVID-19 Related Changes in Breast Cancer Screening among the Boston Mammography Cohort Study
Authors: Chen Naiyu , Cheng David , Sodipo Michelle O. , Barnard Mollie E. , DuPre Natalie C. , Tamimi Rulla M. , Warner Erica T. 
Published: December 2022

ABSTRACTBackgroundWe studied women enrolled in the Boston Mammography Cohort Study to investigate whether subgroups defined by age, race, or family history of breast cancer experienced differences in trends of screening or diagnostic imaging rates during the COVID-19 lockdown and had slower rebound in trends of these rates during reopening.MethodsWe compared trends of monthly breast cancer screening and diagnostic imaging rates over time between the pre-COVID-19, lockdown, and reopening periods and tested for differences in the monthly trend within the same period by age (<50 vs ≥50), race (White vs non-White), and first-degree family history of breast cancer (yes vs no).ResultsOverall, we observed a decline in breast cancer screening and diagnostic imaging rates. The monthly trend of breast cancer screening rates for women age ≥50 was 5% higher (p=0.005) in the pre-COVID-19 period but was 19% lower in the reopening phase than that of women aged<50 (p<0.001). White participants had 36% higher monthly trend of breast cancer diagnostic imaging rates than non-White participants (p=0.018).DiscussionThe rebound in screening was lower in women age ≥50 and lower in non-White women for diagnostic imaging. Careful attention must be paid as the COVID-19 recovery continues to ensure equitable resumption of care.FundingThe project was supported by the Breast Cancer Research Foundation (RT). Researchers were supported by the University of Louisville CIEHS P30 ES030283 (NCD), K01CA188075 (ETW), T32CA09001 (NCD, MOS, MEB) P30 ES000002 (JH, FL), and NIH/NCI K00 CA212222 (MEB). This manuscript is the responsibility of the authors and does not represent the official views of the National Institutes of Health.