bluuebunny/crossref_metadata_2025_split huggingface dataset:
Permutation-based group sequential analyses for cognitive neuroscience
Authors: Veillette John P. , Ho Letitia , Nusbaum Howard C. 
Published: February 2023

AbstractCognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs – undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs – in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori – are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package “niseq” with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement,t-max,F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.
Stress Recovery Triggers Rapid Transcriptional Reprogramming and Activation of Immunity in Plants
Authors: Illouz-Eliaz Natanella , Yu Jingting , Swift Joseph , Lande Kathryn , Jow Bruce , Tuang Za Khai , Lee Travis , Yaaran Adi , Castanon Rosa Gomez , Nery Joseph R. , Nobori Tatsuya , Zait Yotam , Burdman Saul , Ecker Joseph R. 
Published: February 2023

SummaryAll organisms experience stress as an inevitable part of life, from single-celled microorganisms to complex multicellular beings. The ability to recover from stress is a fundamental trait that determines the overall resilience of an organism, yet stress recovery is understudied. To begin unraveling the stress recovery process we studies recovery from drought stress inArabidopsis thaliana. We performed a fine-scale time series of bulk RNA sequencing starting 15 minutes after rehydration following moderate drought. We reveal that drought recovery is a rapid process involving the activation of thousands of recovery-specific genes. To capture these rapid recovery responses in different leaf cell types, we performed single-nucleus transcriptome analysis at the onset of post-drought recovery, identifying a cell type-specific transcriptional state developing within 15 minutes of rehydration independently across cell types. Furthermore, we reveal a recovery-induced activation of the immune system that occurs independent of infection, which enhances pathogen resistancein vivoinA. thaliana, wild tomato (Solanum pennellii)and domesticated tomato (Solanum lycopersicumcv. M82). Since rehydration promotes microbial proliferation and thereby increases the risk of infection1–2, drought recovery-induced immunity may be crucial for plant survival in natural environments. These findings indicate that drought recovery coincides with a preventive defense response, unraveling the complex regulatory mechanisms that facilitate stress recovery in different plant cell types.
Games and the treatment convexity of cancer
Authors: Bayer Péter , West Jeffrey 
Published: February 2023

AbstractEvolutionary game theory has been highly valuable in studying frequency-dependent selection and growth between competing cancer phenotypes. We study the connection between the type of competition as defined by properties of the game, and the convexity of the treatment response function. Convexity is predictive of differences in the tumor’s response to treatments with identical cumulative doses delivered with different variances. We rely on a classification of 2 × 2 games based on the signs of ‘dilemma strengths’, containing information about the kind of selection through the game’s equilibrium structure. With the disease starting in one game class, we map the type of effects treatment may have on the game depending on dosage and the implications of treatment convexity. Treatment response is a linear function of dose if the game is a prisoner’s dilemma, coordination, or harmony game and does not change game class, but may be convex or concave for anti-coordination games. If the game changes class, there is a rich variety in response types including convex-concave and concave-convex responses for transitions involving anti-coordination games, response discontinuity in case of a transition out of coordination games, and hysteresis in case of a transition through coordination games.
An Ensemble Metabolome-Epigenome Interaction Network Identifies Metabolite Modulators of Epigenetic Drugs
Authors: Campit Scott E. , Bhowmick Rupa , Lu Taoan , Saoji Aaditi Vivek , Jin Ran , Shay Madeline R. , Robida Aaron M. , Chandrasekaran Sriram 
Published: February 2023

AbstractMetabolites such as acetyl-CoA and citrate play an important moonlighting role by influencing the levels of histone post-translational modifications (PTMs) and regulating gene expression. This cross talk between metabolism and epigenome impacts numerous biological processes including development and tumorigenesis. However, the extent of moonlighting activities of cellular metabolites in modulating the epigenome is unknown. We developed a data-driven screen to discover moonlighting metabolites by constructing a histone PTM-metabolite interaction network using global chromatin profiles, metabolomics, and epigenetic drug sensitivity data from over 600 cell lines. Our ensemble statistical learning approach uncovered metabolites that are predictive of histone PTM levels and epigenetic drug sensitivity. We experimentally validated synergistic and antagonistic interactions between histone deacetylase and demethylase inhibitors with epigenetic metabolites kynurenic acid, pantothenate, and 1-methylnicotinamide. We apply our approach to track metaboloepigenetic interactions during the epithelial-mesenchymal transition. Overall, our data-driven approach unveils a broader range of metaboloepigenetic interactions than anticipated from previous studies, with implications for reversing aberrant epigenetic alterations and enhancing epigenetic therapies through diet.
A data-fusion approach to identifying developmental dyslexia from multi-omics datasets
Authors: Carrion Jackson , Nandakumar Rohit , Shi Xiaojian , Gu Haiwei , Kim Yookyung , Raskind Wendy H. , Peter Beate , Dinu Valentin 
Published: February 2023

AbstractThis exploratory study tested and validated the use of data fusion and machine learning techniques to probe high-throughput omics and clinical data with a goal of exploring the etiology of developmental dyslexia. Developmental dyslexia is the leading learning disability in school aged children affecting roughly 5-10% of the US population. The complex biological and neurological phenotype of this life altering disability complicates its diagnosis. Phenome, exome, and metabolome data was collected allowing us to fully explore this system from a behavioral, cellular, and molecular point of view. This study provides a proof of concept showing that data fusion and ensemble learning techniques can outperform traditional machine learning techniques when provided small and complex multi-omics and clinical datasets. Heterogenous stacking classifiers consisting of single-omic experts/models achieved an accuracy of 86%, F1 score of 0.89, and AUC value of 0.83. Ensemble methods also provided a ranked list of important features that suggests exome single nucleotide polymorphisms found in the thalamus and cerebellum could be potential biomarkers for developmental dyslexia and heavily influenced the classification of DD within our machine learning models.
A comparative study ofin vitroair-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution
Authors: Prescott Rachel A. , Pankow Alec P. , de Vries Maren , Crosse Keaton , Patel Roosheel S. , Alu Mark , Loomis Cynthia , Torres Victor , Koralov Sergei , Ivanova Ellie , Dittmann Meike , Rosenberg Brad R. 
Published: February 2023

AbstractThe airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial cultures at air-liquid interface (HAE) are a physiologically relevantin vitromodel of this heterogeneous tissue, enabling numerous studies of airway disease1–7. HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining capacity for differentiation to HAE5. However, gene expression and innate immune function in HAE derived from BCi-NS1.1 versus primary cells have not been fully characterized. Here, combining single cell RNA-Seq (scRNA-Seq), immunohistochemistry, and functional experimentation, we confirm at high resolution that BCi-NS1.1 and primary HAE cultures are largely similar in morphology, cell type composition, and overall transcriptional patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1 HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus andStaphylococcus aureus. Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.
Repetitive Spreading Depolarization induces gene expression changes related to synaptic plasticity and neuroprotective pathways
Authors: Dell’Orco Michela , Weisend Jordan E. , Perrone-Bizzozero Nora I. , Carlson Andrew P. , Morton Russell A. , Linsenbardt David N , Shuttleworth C. William 
Published: February 2023

AbstractSpreading depolarization (SD) is a slowly propagating wave of profound depolarization that sweeps through cortical tissue. While much emphasis has been placed on the damaging consequences of SD, there is uncertainty surrounding the potential activation of beneficial pathways such as cell survival and plasticity. The present study used unbiased assessments of gene expression to evaluate that compensatory and repair mechanisms could be recruited following SD, regardless of the induction method, which prior to this work had not been assessed. We also tested assumptions of appropriate controls and the spatial extent of expression changes that are important forin vivoSD models. SD clusters were induced with either KCl focal application or optogenetic stimulation in healthy mice. Cortical RNA was extracted and sequenced to identify differentially expressed genes (DEGs). SDs using both induction methods significantly upregulated 16 genes (versus sham animals) that included the cell proliferation-related genes FOS, JUN, and DUSP6, the plasticity-related genes ARC and HOMER1, and the inflammation-related genes PTGS2, EGR2, and NR4A1. The contralateral hemisphere is commonly used as control tissue for DEG studies, but its activity could be modified by near-global disruption of activity in the adjacent brain. We found 21 upregulated genes when comparing SD-involved cortex versus tissue from the contralateral hemisphere of the same animals. Interestingly, there was almost complete overlap (21/16) with the DEGs identified using sham controls. Neuronal activity also differs in SD initiation zones, where sustained global depolarization is required to initiate propagating events. We found that gene expression varied as a function of the distance from the SD initiation site, with greater expression differences observed in regions further away. Functional and pathway enrichment analyses identified axonogenesis, branching, neuritogenesis, and dendritic growth as significantly enriched in overlapping DEGs. Increased expression of SD-induced genes was also associated with predicted inhibition of pathways associated with cell death, and apoptosis. These results identify novel biological pathways that could be involved in plasticity and/or circuit modification in brain tissue impacted by SD. These results also identify novel functional targets that could be tested to determine potential roles in recovery and survival of peri-infarct tissues.
Robust backstepping control for maneuver aircraft based on event-triggered mechanism and nonlinear disturbance observer
Authors: Shen Yaohua , Chen Mou 
Published: February 2023

Purpose
This study aims to achieve the post-stall pitching maneuver (PSPM) and decrease the deflection frequency of aircraft actuators controlled by the robust backstepping method based on event-triggered mechanism (ETM), nonlinear disturbance observer (NDO) and dynamic surface control (DSC) techniques.

Design/methodology/approach
To estimate unsteady aerodynamic disturbances (UADs) to suppress their adverse effects, the NDO is designed. To avoid taking the derivative of the virtual control law directly and eliminate the coupling term of the system states and dynamic surface errors in the stability analysis, an improved DSC is developed. Combined with the NDO and DSC techniques, a robust backstepping method is proposed to achieve the PSPM. Furthermore, to decrease the deflection frequency of the aircraft actuators, a state-dependent ETM is introduced.

Findings
An ETM-and-NDO-based backstepping method with an improved DSC technique is developed to achieve the PSPM and decrease the deflection frequency of aircraft actuators. And simulation results are presented to verify the effectiveness of the proposed paper.

Originality/value
Few studies have been conducted on the control of the PSPM in which the lateral and longitudinal attitude dynamics are coupled with each other considering the UADs. Moreover, the mechanism that can decrease the deflection frequency of aircraft actuators is rarely developed in existing research. This study proposes an ETM-and-NDO-based backstepping scheme to address these problems with satisfactory performance of the PSPM.
Promotion or inhibition of different incubation services? Evidence from government funding of China
Authors: Qi Xinzhou , Ning Zhong 
Published: February 2023

Purpose
The purpose of this paper is to investigate the relationship between the characteristics of the incubation industry, government funding, and the intensity of funding for different services. Because the incubation industry has particular characteristics, government funding varies for different services, and its intensity varies with service.

Design/methodology/approach
Government funding is classified as incubation subsidy and incubation incentive. Besides, incubation services include property management, business mentoring as well as investment and financing. Based on this, this study examines the influence mechanism of different subsidy and incentive on incubation services by using the generalized propensity score matching method.

Findings
The empirical results show that subsidy and incentive have an inverse-U shape effect on property management service, but a linear effect on business guidance service. Furthermore, subsidy does not affect investment and financing service, but incentive that can have a significant impact.

Originality/value
The theme of government funding and incubator services plays an important role in helping entrepreneurs expand their businesses. Incubation subsidy and incentive can provide important support to help enterprises obtain more preferential loans, technical services and technical support in the incubator. Applying it to incubator services can provide better technology and entrepreneurship guidance. These services can help new entrepreneurs understand products and markets, and how to develop more successfully in the early stage. In short, incubators supported by government funds can provide important support to entrepreneurs to help them successfully realize their business plans.
Social media on the route to circular economy transition from a dialogic perspective: evidence from the agri-food industry
Authors: Esposito Benedetta , Sica Daniela , Malandrino Ornella , Supino Stefania 
Published: February 2023

PurposeThis paper investigates circular economy communications and stakeholder dialogic engagement with circular economy posts published by European agri-food companies on Twitter from the spread of the COVID-19 pandemic. It explores the use of social media as a dialogic tool to activate circular economy engagement in order to involve all supply-chain actors on the route to a circular transition.Design/methodology/approachA coding framework based on the reclassification of the Glossary of Circular Economy, according to a 4-R paradigm (reduce, reuse, recycle and recover), was developed for the analysis. All tweets published by a sample of European agri-food companies, starting from the start of the COVID-19 pandemic until data extraction, were collected, purified and analysed.FindingsAgri-food companies showed a higher level of engagement through social media, even if mainly focused on “recycling” and “general circular economy” issues. In general, awareness among social network users of the need to be part of the circular economy transition emerged. Moreover, the highest percentage of posts published by the companies' Twitter accounts was informative rather than interactive. In addition, starting with the COVID-19 pandemic crisis, the circular economy has arisen as a central topic of debate and a driver for the rethinking process of the agri-food business community.Originality/valueTo the best of the authors' knowledge, this research represents the first study focused on circular economy engagement through social media from the company perspective in the agri-food industry.
The effect of ownership composition on corporate financial performance in the European frontier markets
Authors: Zahid R.M. Ammar , Taran Alina , Khan Muhammad Kaleem , Simga-Mugan Can 
Published: February 2023

PurposeThis study investigates the influence of ownership composition on market-based and accounting-based financial performance in the European frontier markets (EFMs), a target region for global investors.Design/methodology/approachOwnership composition is defined as shareholders' concentration and structure (i.e. foreign, domestic, state and free-float), whereas financial performance is measured as Tobin's Q and return on assets. The system generalised method of moments panel data estimation technique is employed on a sample of 241 companies.FindingsFindings reveal that companies from European Union (EU) frontier markets are controlled, on average, by one to five large shareholders. Being a signal of expropriation rationale of majority shareholders regardless of the capital structure, this highly concentrated ownership and decision-making model negatively affects the market-based and accounting-based financial performance of the companies and thereby supports the agency theory in the frontier markets.Research limitations/implicationsThe findings provide empirical evidence for authorities, investors, analysts and corporations regarding the effect of ownership percentage and structure in the Eastern European region, assisting also other frontier and emerging markets in corporate governance and other regulatory decisions.Originality/valueThe ownership–performance relationship varies from developed to emerging markets with conflicting results. This study provides evidence on monitoring and expropriation effects of majority shareholders in the context of different categories of shareholders. In doing so, it combines the analysis of both ownership concentration and structure in the EFMs.
SanMove: next location recommendation via self-attention network
Authors: Wang Bin , Li Huifeng , Tong Le , Zhang Qian , Zhu Sulei , Yang Tao 
Published: February 2023

PurposeThis paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.Design/methodology/approachThe authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.FindingsThe authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.Originality/valueThe authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.