bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emotion regulation and cognitive function as mediating factors for the association between lifetime abuse and risky behaviors in women of color
Authors: Villalba Karina , Domenico Lisa H. , Cook Robert L. , O’Connor Julia , Michael-Samaroo Kyndester , Pino Espejo Maria Jose Del , Martin Pilar , Dévieux Jessy G. 
Published: January 2023

AbstractBackgroundLifetime abuse (i.e., childhood abuse, intimate partner violence) has been linked to risky behavioral outcomes (i.e., alcohol use, risky sex). Women who experience lifetime abuse have poor emotion regulation and may have problems with executive functioning, which could help explain the relationship between lifetime abuse and risky behaviors. However, research on executive functioning and emotion regulation as mediators of this relationship has been limited. In the present study, we examined this association. We hypothesized that lifetime abuse would be related to greater difficulty in emotion regulation and executive function, which would also be associated with greater alcohol use and risky sex.MethodsThis cross-sectional study included 150 women with a history of lifetime abuse who were assessed for alcohol use using the AUDIT Score; emotion regulation was measured using the Difficulties with Emotion Regulation Scale (DERS); risky sex was measured using question: “in the last 90 days, how many people did you have anal or vaginal sex without using a condom? Executive function was assessed using the NIH Toolbox.ResultsThe mediation model followed the self-regulation theory, which proposes executive function as the higher-order cognitive process. Results showed that executive function deficit and poor emotion regulation significantly mediated the relationship between lifetime abuse and alcohol use severity (indirect effect = .097, SE .031, 95% CI = .035 to .158).ConclusionOur findings suggest executive function and emotion regulation as a potential mechanism for alcohol use severity in women who experienced lifetime abuse (i.e., childhood abuse and intimate partner violence).
Westwards expansion of the European catfishSilurus glanisin the Douro River (Portugal)
Authors: Gkenas Christos , Martelo Joana , Ribeiro Diogo , Gago João , Santos Gil , Dias Diogo , Ribeiro Filipe 
Published: January 2023

ABSTRACTThe current study reports the first occurrence and the spread of the European catfishSilurus glanis(Family: Siluridae) in the Portuguese section of the Douro River, suggesting a potential expansion of its distribution in Portugal either via westward dispersal across international rivers and/or human-assisted introductions into new reservoirs and drainages. European catfish has unique features (e.g., opportunistic predator, hunting, and aggregation behaviour) that make it highly suitable for establishing self-sustaining populations in new areas and likely contribute to its invasion success. The species may severely affect native prey communities and modify food web structure and ecosystem functioning. Efficient and sustainable management actions are needed to prevent further introductions in the future.
Tryptophan metabolism determines outcome in tuberculous meningitis: a targeted metabolomic analysis
Authors: Ardiansyah Edwin , Pacheco Julian Avila , Hoang Nhat Le Thanh , Dian Sofiati , Vinh Dao Nguyen , Hai Hoang Thanh , Bullock Kevin , Alisjahbana Bachti , Netea Mihai G , Estiasari Riwanti , Bich Tram Trinh Thi , Donovan Joseph , Heemskerk Dorothee , Hong Chau Tran Thi , Bang Nguyen Duc , Ganiem Ahmad Rizal , Ruslami Rovina , Koeken Valerie ACM , Hamers Raph L , Imran Darma , Maharani Kartika , Kumar Vinod , Clish Clary B. , van Crevel Reinout , Thwaites Guy , van Laarhoven Arjan , Thuong Thuong Nguyen Thuy 
Published: January 2023

AbstractBackgroundCellular metabolism is critical for the host immune function against pathogens, and metabolomic analysis may help understand the characteristic immunopathology of tuberculosis. We performed targeted metabolomic analyses in a large cohort of patients with tuberculous meningitis (TBM), the most severe manifestation of tuberculosis, focusing on tryptophan metabolism.MethodsWe studied 1069 Indonesian and Vietnamese adults with TBM (26.6% HIV-positive), 54 non-infectious controls, 50 with bacterial meningitis, and 60 with cryptococcal meningitis. Tryptophan and downstream metabolites were measured in cerebrospinal fluid (CSF) and plasma using targeted liquid chromatography mass-spectrometry. Individual metabolite levels were associated with survival, clinical parameters, CSF bacterial load and 92 CSF inflammatory proteins.ResultsCSF tryptophan was associated with 60-day mortality from tuberculous meningitis (HR=1.16, 95%CI=1.10-1.24, for each doubling in CSF tryptophan) both in HIV-negative and HIV-positive patients. CSF tryptophan concentrations did not correlate with CSF bacterial load nor CSF inflammation but were negatively correlated with CSF interferon-gamma concentrations. Unlike tryptophan, CSF concentrations of an intercorrelating cluster of downstream kynurenine metabolites did not predict mortality. These CSF kynurenine metabolites did however correlate with CSF inflammation and markers of blood-CSF leakage, and plasma kynurenine predicted death (HR 1.54, 95%CI=1.22-1.93). These findings were mostly specific for TBM, although high CSF tryptophan was also associated with mortality from cryptococcal meningitis.ConclusionTBM patients with a high baseline CSF tryptophan or high systemic (plasma) kynurenine are at increased risk of mortality. These findings may reveal new targets for host-directed therapy.FundingThis study was supported by National Institutes of Health (R01AI145781) and the Wellcome Trust (110179/Z/15/Z and 206724/Z/17/Z).
Detection of circular RNAs and their potential as biomarkers predictive of drug response
Authors: Nguyen Julia , Mammoliti Anthony , Nair Sisira Kadambat , So Emily , Abbas-Aghababazadeh Farnoosh , Eeles Christoper , Smith Ian , Smirnov Petr , He Housheng Hansen , Tsao Ming-Sound , Haibe-Kains Benjamin 
Published: January 2023

ABSTRACTThe introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.
Epileptic seizure suppression: a computational approach for identification and control using real data
Authors: Brogin João A. F. , Faber Jean , Reyes-Garcia Selvin Z. , Cavalheiro Esper A. , Bueno Douglas D. 
Published: January 2023

AbstractEpilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space, it can thus be used for the artificial reconstruction and attenuation of seizures as well. Considering this, the first objective of this work is to propose an SI approach using AR models to describe real epileptiform activity. The second objective is to provide a strategy for reconstructing and mitigating such activity artificially, considering non-hybrid and hybrid controllers − designed from ictal and interictal events, respectively. The results show that AR models of relatively low order represent epileptiform activities fairly well and both controllers are effective in attenuating the undesired activity while simultaneously driving the signal to an interictal stage. These findings may lead to customized models based on each signal, brain region or patient, from which it is possible to better define shape, frequency and duration of external stimuli that are necessary to attenuate seizures.Author summaryEpilepsy is perhaps one of the most studied brain disorders and it is still not sufficiently well understood. The use of computational models is useful in this case since several simulations can be run using them, such that experience and insight about seizures can be gained without necessarily carrying out experiments. These models are usually designed with or without some knowledge about the brain region or phenomenon. Seizure attenuation approaches have been proposed for the first case, but they are limited to the type of seizure correctly described by the model. The present work proposes a similar procedure for the second one (where only the data are available and nothing else is assumed), which is regarded as more realistic due to its direct application on the signals and can lead to customized models for each activity, brain region or patient, defining important information such as the shape, frequency and duration of the external stimuli that must be applied to mitigate a seizure.
ATP-releasing SWELL1 channel in spinal microglia contributes to neuropathic pain
Authors: Chu Jiachen , Yang Junhua , Zhou Yuan , Chen Jianan , Chen Kevin Hong , Zhang Chi , Cheng Henry Yi , Koylass Nicholas , Liu Jun O. , Guan Yun , Qiu Zhaozhu 
Published: January 2023

ABSTRACTFollowing peripheral nerve injury, extracellular ATP-mediated purinergic signaling is crucial for spinal cord microglia activation and neuropathic pain. However, the mechanisms of ATP release remain poorly understood. Here, we show that volume-regulated anion channel (VRAC) is an ATP-releasing channel and is activated by inflammatory mediator sphingosine-1-phosphate (S1P) in microglia. Mice with microglia-specific deletion of Swell1 (also known as Lrrc8a), a VRAC essential subunit, had reduced peripheral nerve injury-induced increase in extracellular ATP in spinal cord. The mutant mice also exhibited decreased spinal microgliosis, dorsal horn neuronal hyperactivity, and both evoked and spontaneous neuropathic pain-like behaviors. We further performed high-throughput screens and identified an FDA-approved drug dicumarol as a novel and potent VRAC inhibitor. Intrathecal administration of dicumarol alleviated nerve injury-induced mechanical allodynia in mice. Our findings suggest that ATP-releasing VRAC in microglia is a key spinal cord determinant of neuropathic pain and a potential therapeutic target for this debilitating disease.
Information integration during bioelectric regulation of morphogenesis in the embryonic frog brain
Authors: Manicka Santosh , Pai Vaibhav P. , Levin Michael 
Published: January 2023

AbstractSpatiotemporal bioelectric states regulate multiple aspects of embryogenesis. A key open question concerns how specific multicellular voltage potential patterns differentially activate distinct downstream genes required for organogenesis. To understand the information processing mechanisms underlying the relationship between spatial bioelectric patterns, genetics, and morphology, we focused on a specific spatiotemporal bioelectric pattern in theXenopusectoderm that regulates embryonic brain patterning. We used machine learning to design a minimal but scalable bioelectric-genetic dynamical network model of embryonic brain morphogenesis that qualitatively recapitulated previous experimental observations. A causal integration analysis of the model revealed a simple higher-order spatiotemporal information integration mechanism relating the spatial bioelectric and gene activity patterns, where the latter is expressed as a function of the causal influence of the voltages of groups of cells. Specific aspects of this mechanism include causal apportioning (certain cell positions are more important for collective decision making), informational asymmetry (depolarized cells are more influential than hyperpolarized cells), long distance influence (genes in a cell are variably sensitive to voltage of faraway cells), and division of labor (different genes are sensitive to different aspects of voltage pattern). The asymmetric information-processing character of the mechanism led the model to predict an unexpected degree of plasticity and robustness in the bioelectric prepattern that regulates normal embryonic brain development. Ourin vivoexperiments verified these predictions via molecular manipulationsin Xenopusembryos. This work shows the power of using a minimalin silicoapproach to drastically reduce the parameter spacein vivo, making hard biological questions tractable. These results provide insight into the collective decision-making process of cells in interpreting bioelectric pattens that guide large-scale morphogenesis, suggesting novel applications for biomedical interventions and new tools for synthetic bioengineering.
Learning cell-specific networks from dynamics and geometry of single cells
Authors: Zhang Stephen Y , Stumpf Michael P H 
Published: January 2023

AbstractCell dynamics and biological function are governed by intricate networks of molecular interactions. Inferring these interactions from data is a notoriously difficult inverse problem. The majority of existing network inference methods work at the population level to construct population-averaged representations of gene interaction networks, and thus do not naturally allow us to infer differences in gene regulation activity across heterogeneous cell populations. We introduce locaTE, an information theoretic approach that leverages single cell dynamical information together with geometry of the cell state manifold to infer cell-specific, causal gene interaction networks in a manner that is agnostic to the topology of the underlying biological trajectory. We find that factor analysis can give detailed insights into the inferred cell-specific GRNs. Through extensive simulation studies and applications to three experimental datasets spanning mouse primitive endoderm formation, pancreatic development, and haematopoiesis, we demonstrate superior performance and the generation of additional insights compared to standard static GRN inference methods. We find that locaTE provides a powerful, efficient and scalable network inference method that allows us to distill cell-specific networks from single cell data.Graphical abstractCell-specific network inference from estimated dynamics and geometryLocaTE takes as input a transition matrixPthat encodes inferred cellular dynamics as a Markov chain on the cell state manifold. By considering the coupling (Xτ,X−τ), locaTE produces an estimate of transfer entropy for each celliand each pair of genes (j, k). Downstream factor analyses can extract coherent patterns of interactions in an unsupervised fashion.
Weakly-supervised Temporal Segmentation of Cell-cycle Stages with Center-cell Focus using Recurrent Neural Networks
Authors: Jose Abin , Roy Rĳo , Stegmaier Johannes 
Published: January 2023

AbstractTraining deep-learning models for biomedical images has always been a problem due to the lack of annotated data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcript, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from LiveCellMiner and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training.
Fourier transform approach to homogenization of frequency-dependent heat transfer in porous media
Authors: To Quy Dong , Bonnet Guy 
Published: January 2023

PurposeThe purpose of this paper is to solve the local problem involving strong contrast heterogeneous conductive material, with application to gas-filled porous media with both perfect and imperfect Kapitza boundary conditions at the bi-material interface. The effective parameters like the dynamic conductivity and the thermal permeability in the acoustics of porous media are also derived from the cell solution.Design/methodology/approachThe Fourier transform method is used to solve frequency-dependent heat transfer problems. The periodic Lippmann–Schwinger integral equation in Fourier space with source term is first formulated using discrete Green operators and modified wavevectors, which can then be solved by iteration schemes.FindingsNumerical examples show that the schemes converge fast and yield accurate results when compared with analytical solution for benchmark problems.Originality/valueThe formulation of the method is constructed using static and dynamic Green operators and can be applied to pixelized microstructure issued from tomography images.
Creating value networks through a learning society for Indigenous women tourism entrepreneurs
Authors: Shrestha Roshis Krishna , L'Espoir Decosta Jean-Nöel Patrick , Shrestha Rupa 
Published: January 2023

Purpose
This study aims to integrate social embeddedness with learning society philosophy to explore how grassroots associations of Indigenous women tourism entrepreneurs can leverage their social network to co-create value.

Design/methodology/approach
A critical feminist perspective considers the intersectional experiences of Indigenous women tourism entrepreneurs from the rural Manasalu region of Nepal. Twenty-one semi-structured interviews with local tourism stakeholders were carried out. Hermeneutics in tandem with Indigenous methods of analysis ensured consideration of Indigenous ontologies and social locations beyond being merely theoretically driven.

Findings
A paradox of Indigenous women’s empowerment emerged where several efforts for empowerment presented themselves as a double-edged sword. Individuals’ social capital and social support for the sustenance and stability of grassroots associations ensure collective and continuous learning through a value-creation framework.

Research limitations/implications
Collective self-reflection and self-determination for knowledge creation and sharing amongst social ties shed new light on the role of an Indigenous standpoint on value creation.

Originality/value
To the best of the authors’ knowledge, this is the first study that captures how the intersectionality of Indigenous women entrepreneurs in grassroots associations use their social capital through contesting, leveraging and learning to transform their social network into a value network.
The impact of emotional intelligence, increasing job demands behaviour and subjective well-being on teacher performance: teacher-gender differences
Authors: Amirian Seyed Morteza , Amirian Seyedeh Khadijeh , Kouhsari Masoumeh 
Published: January 2023

PurposeThe purpose of this paper is to explore the critical roles of emotional intelligence, increasing job demands behaviour and subjective well-being in teachers' performance throughout their gender.Design/methodology/approachIn this study, the authors used multi-group structural equation modelling and mediation analysis of a sample of 602 primary school teachers in Iran.FindingsThis study found that emotional intelligence significantly affected teachers' performance. Teachers' emotional intelligence and increasing job demands behaviour were significant predictors of teacher performance in both genders. Furthermore, increasing job demand behaviour had a stronger mediating effect than subjective well-being on the relationship between emotional intelligence and teachers' performance in both genders.Originality/valueThis model is an attempt to examine possible gender differences on the relationships between teachers' emotional intelligence and their job performance by mediating roles of subjective well-being and increasing job demands behaviours in a specific societal and educational context.
“Even though we are elders living alone”: has social support reshaped the care of older adults during the COVID-19 pandemic?
Authors: Wannachot Washiraporn , Phukrongpet Pimporn , Rattanasuteerakul Kanokporn , Daovisan Hanvedes 
Published: January 2023

Purpose
This study aims to explore how social support has reshaped the care of older adults living alone in northeast Thailand during the COVID-19 pandemic.

Design/methodology/approach
This study used a qualitative method using a narrative gerontological perspective with a descriptive design. Purposive sampling was conducted from 20 in-depth narrative interviews between November 2021 and February 2022 in Maha Sarakham province, northeast Thailand. Data transcriptions were performed using a narrative analytical process.

Findings
The in-depth narrative interviews indicated that older adults living alone during the coronavirus pandemic valued a comfortable life, a sense of place, favourable living arrangements, self-reliance, life goals and meaningful life. The gerontological analysis showed that social support for older adults living alone included a social safety net, networking, supporting needs, physical care and mental health responses to the COVID-19 pandemic.

Originality/value
To the best of the authors’ knowledge, this is the first qualitative narrative gerontological study to explore how social support reshaped the care of older adults living alone during the COVID-19 pandemic in northeast Thailand.