bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acoustofluidic patterning for improved microtissue histology
Authors: Deshmukh Dhananjay V. , Vuille-dit-Bille Emilie , Joshi Saumitra , Gerber Nicola , Fux Christine , Eichenberger Annina , Bono Epifania , Rimann Markus , Heub Sarah , Migliozzi Daniel , Boder-Pasche Stephanie , Weder Gilles , Dual Jurg , Tibbitt Mark W. 
Published: February 2025

AbstractMicrotissues (such as organoids or spheroids) are useful to model healthy or diseased human tissues and organs, with application in drug testing, tissue engineering, and personalized medicine. To characterize their morphology and phenotype, microtissues are often monitored using histology and high-resolution imaging. While this technique is well-established and standard for macroscopic tissue sections, it remains challenging to execute and has low throughput in the context of these sub-millimeter structures, mainly due to the fact that their size makes them difficult to locate within the embedding medium. To enhance the efficiency of microtissue histology, we developed an acoustofluidic device that arranges microtissues in a coplanar arrangement within HistoGel, a widely used embedding medium. Pre-patterning microtissues within HistoGel streamlines downstream processing by positioning the centers of mass of the microtissues at a pre-defined plan in the sample, simplifying sectioning, labeling, and imaging. We validated the method using differently sized HepG2 microtissues and tumor microtissues from osteosarcoma cells (Saos-2, MG-63, HOS), demonstrating that this method can be applied to different microtissue shapes, sizes, and types, without modifying established post-processing techniques. Our approach substantially enhances the microtissue histology workflow and broadens possibilities for microtissue analysis.
Physical Activity, Symptoms, Quality of Life and Exercise Program Preferences in People with Chronic Lymphocytic Leukemia
Authors: Miles Ellie E. , Nicol Jennifer L. , Fowler Hatti , Roberts Amelia , Hulton Andrew T. , Jeary Caitlin , Walewska Renata , Iyengar Sunil , Hanson Erik D. ,   CLL Support Association, Sitlinger Andrea , Bartlett David B. 
Published: February 2025

AbstractBackgroundChronic lymphocytic leukaemia (CLL) has a heterogeneous lifelong course. Most patients experience significant symptoms which negatively impact their quality of life (QoL). Although physical activity and exercise may help manage symptoms, it is unclear what disease-related factors drive physical inactivity in people with CLL.MethodsThis study explored physical activity in CLL and assessed relationships in treatment stage, symptoms, quality of life, and preferences for physical activity using an online questionnaire.Results128 individuals with CLL (66M/62F) completed the questionnaire. Treated CLL (N=55) exhibited worse QoL (p=0.018) and lower engagement in physical activity (p=0.045) compared to treatment naïve (N=73). Both groups reported high fatigue (∼77%) and insomnia (∼55%) which were associated with less likelihood of being physically active. Physically active participants reported better QoL (p=0.020), physical functioning (P=0.003) and role functioning (p=0.020) as well as lower levels of fatigue (p=0.036), pain (p=0.017) and symptom burden (p=0.026). Although 79% of respondents wanted to engage in exercise programs for their CLL, 70% reported never receiving exercise guidance from their healthcare professionals.ConclusionFindings highlight a significant need for interventions to increase physical activity in people with CLL. Furthermore, there is considerable interest from the CLL community in receiving exercise guidance.
Hierarchical Clustering with an Ensemble of Principle Component Trees for Interpretable Patient Stratification
Authors: Pfeifer Bastian , Bloice Marcus D. , Sirocchi Christel , Loecher Markus 
Published: February 2025

AbstractPatient stratification plays a crucial role in personalized medicine by identifying distinct subgroups of patients based on their molecular and/or clinical characteristics. However, many unsupervised machine learning-based stratification techniques fail to identify the essential biomarker traits associated with each patient group. In this paper, we present a novel approach for interpretable patient stratification using hierarchical ensemble clustering. Our method leverages feature sampling in conjunction with principal component analysis (PCA) to capture the most significant patterns and contributing biomarkers. We demonstrate the effectiveness of our approach using machine learning benchmark datasets and real-world data from The Cancer Genome Atlas (TCGA), showcasing the improved interpretability of the detected patient clusters.
Minding the Gap in Sentinel Surveillance Networks: An Analysis of Brazilian Indigenous Areas
Authors: Oliveira Juliane F. , Vasconcelos Adriano O. , Alencar Andrêza L. , Cunha Maria Célia L. S. , Marcilio Izabel , Barral-Netto Manoel , Ramos Pablo Ivan P. 
Published: February 2025

AbstractBackgroundOptimizing the allocation of sentinel surveillance sites, particularly aiming early pathogen detection, remains a persistent challenge. Equally critical is ensuring the inclusion of vulnerable and often underserved populations as targets when prioritizing surveillance sites.ObjectiveThis study assesses the current coverage of the respiratory pathogen surveillance network in Brazil and proposes an optimized rearrangement of sentinel locations that balance the coverage of Indigenous populations while integrating country-wide human mobility patterns.MethodsWe collected the locations of Indigenous populations living in Special Indigenous Sanitary Districts (DSEIs) from the Brazilian Ministry of Health, and derived mobility route estimates using the Ford-Fulkerson algorithm applied to input air, road, and water transportation data for the whole country. To optimize sentinel city selection for sample collection, we applied a linear optimization algorithm designed to maximize two key objectives: 1) representation of Indigenous regions and 2) coverage of human mobility patterns, thereby enhancing early pathogen detection. Validation of the strategy was performed by obtaining the current list of cities in Brazil’s influenza sentinel network combined with a health attraction index from the Brazilian Institute of Geography and Statistics, which enabled us to assess the suitability and potential benefits of our optimized surveillance network.ResultsOur optimization model provides actionable recommendations for pathogen sampling locations, enhancing early detection. By selecting 199 cities, we create a more representative sentinel network, covering all DSEIs by rearranging 108 cities (58.3%), addressing gaps in 9 of 34 previously uncovered regions. This improves nationwide mobility coverage by 16.8 percentage points—from 52.4% to 69.2%—compared to Brazil’s current sentinel system. Additionally, all newly selected cities serve as hubs for medium to high-complexity healthcare, with 37.9% of DSEI cities lacking coverage in the existing flu sentinel network.ConclusionsWe propose a strategic framework for sentinel site placement that maximizes DSEI coverage and aligns with human mobility patterns across Brazil, improving the overall effectiveness of disease surveillance, particularly in these critical regions and often underserved populations.
Physical Activity Phenotypes in Endometriosis Using Unsupervised Learning via Functional Mixture Models
Authors: Tricoche Bryan T. , Caceres Billy A. , Shaw Leslee , Garber Carol Ewing , Konigorski Stefan , Kolli Sahiti , Fuchs Thomas , Ensari Ipek 
Published: February 2025

AbstractIntroductionEndometriosis is a chronic condition associated with severe pelvic pain, dysmenorrhea, infertility, and worsening quality of life. Regular physical activity (PA) is effective for pain management and reducing chronic disease symptoms, yet individuals with endometriosis are more likely to be insufficiently active. This study aims to investigate latent profiles of daily PA trajectories in this population via clustering.MethodsThe study sample included 173 adults (4,895 person-level days) with a confirmed diagnosis of endometriosis enrolled in theAll of Us Research Program. PA data was collected from participants using Fitbit wrist-worn trackers. We used 30 consecutive days of data from each individual, allowing up to 10 days of missingness (which were imputed using multiple imputed chained equations). Functional mixture models (FMMs) identified latent PA trajectory clusters using daily step counts as the clustering variable. The optimal number of clusters was determined using the Bayesian information criterion (BIC).ResultsThe best-fitting FMM identified K=4 distinct clusters (BICK=4= –11884.442, BICK=3= –11892.236, BICK=5= –11907.917). TheHigh Activephenotype exhibited the highest volume and variability of step counts over the sample period (Mean = 12777.0, SD=5248.2) as well as the highest volume of moderate-to-vigorous PA (MVPA) minutes (Mean = 70.8). TheHigh Moderatephenotype exhibited the second highest volume and variability of step counts (Mean = 9125.4, SD = 3631.0) and second highest MVPA minutes. TheLow Moderatephenotype exhibited the second lowest volume and variability of step counts (Mean = 6067.5, SD =2797.0) and the second lowest MVPA minutes (Mean = 24.3). TheInsufficiently Activecluster exhibited the lowest volume and variability of step counts (Mean = 4235.7, SD = 2174.7) and the lowest MVPA (Mean = 16.1).ConclusionThis is the first study to investigate and report distinct PA profiles among a national sample of individuals living with endometriosis, and via objectively estimated PA data. Identifying distinct clusters of PA patterns in endometriosis based on within– and between individual PA variance may help identify those at risk and inform the development of personalized interventions to promote PA and improve overall health outcomes.
Plant plasticity in the face of climate change – CO2offsetting effects to warming and water deficit in wheat. A review
Authors: Gawinowski Meije , Chenu Karine , Deswarte Jean-Charles , Launay Marie , Bancal Marie-Odile 
Published: February 2025

AbstractFuture crop production will depend on plant plasticity in response to increases in atmospheric CO2, mean temperature, heatwave and drought events. The present review intends to highlight the impact of interactions between high CO2levels, warming and water deficit in existing published experimental data in the case of wheat. To do so, we identified experiments quantifying the effects of such interactions on traits related to crop productivity and water use. We used the collected data to estimate plasticity indices assessing compensation and interaction between elevated CO2 and adverse climatic conditions, bringing a new perspective on the matter. In the studied data, even though there is an important variability, we found that crop productivity tends to decrease despite the positive effects of the rise in CO2concentration. Conversely, with elevated CO2, water consumption tends to decrease despite the warmer conditions. We hypothesized that the positive effect of CO2on crop productivity is greater under drought conditions, which is confirmed in 54% of the experiments. This review highlights the need to acquire further experimental data under possible future conditions to calibrate and validate crop models: their range of validity requires more thorough testing under the wide range of projected environmental conditions.
Evaluating Transcriptomic Integration for Cyanobacterial Constraint-based Metabolic Modelling
Authors: Pugsley Thomas , Hanke Guy , Duffy Christopher Daniel Patrick 
Published: February 2025

1AbstractMetabolic modelling has wide-ranging applications, including for the production of high-value compounds, understanding complex disease and analysing community interactions. Integrating transcriptomic data with genome-scale metabolic models is crucial for deepening our understanding of complex biological systems, as it enables the development of models tailored to specific conditions, such as particular tissues, environments, or experimental setups. Relatively little attention has been given to the assessment of such integration methods in predicting intracellular fluxes. While a few validation studies offer some insights, their scope remains limited, particularly for organisms like cyanobacteria, for which little metabolic flux data are available. Cyanobacteria hold significant biotechnological potential due to their ability to synthesize a wide range of high-value compounds with minimal resource inputs [21]. The impact of specific methodological decisions on integration, however, has scarcely been assessed beyond human models, with no thorough exploration of parameter choices in valve-based integration methods. By implementing a novel analysis pipeline, we evaluated these methodological decisions using the genome-scale model forSynechocystissp. PCC 6803 (iSynCJ816 [17]) with existing transcriptomic data in biomass-optimised scenarios. Our analyses indicate that selecting an appropriate integration method may not always be straightforward and depends on the initial model configuration - a factor which is often overlooked during integration. By evaluating sets of methods, we identified a trade-off between the buffering of light into the system and maintenance of flux near system boundaries. We suggest that the use of the lazy-step mapping function with importance-based scaling results in the best predictions, particularly when these can be validated with experimental data. When using one-size-fits-all scaling with the lazy-step mapping function, it appears preferable to use light buffering to avoid inappropriate bound changes near the photosystems, a factor which importance-based scaling may help to compensate for. In cases where no experimental data can be used for validation, the novel thresholding approach could be adopted as this showed some improvements upon the standard Lazy method.
ERGA-BGE genome ofCoenonympha oedippus: an IUCN endangered European butterfly species occurring in two ecotypes
Authors: Čelik Tatjana , Lokovšek Tjaša , Bužan Elena , Böhne Astrid , Monteiro Rita , Fernández Rosa , Escudero Nuria , Gut Marta , Aguilera Laura , Ferreira Francisco Câmara , Cruz Fernando , Gómez-Garrido Jèssica , Alioto Tyler S. , Bortoluzzi Chiara 
Published: February 2025

AbstractThe reference genome of the false ringlet (Coenonympha oedippus) will serve as a valuable resource for uncovering the genetic mechanism underlying the species′ adaptability to two ecologically distinct habitats. Through this genome we might be able to determine whether (i) each ecotype is monophyletic, indicating that the ecological divergence represents an early stage of speciation, (ii) the ecotypes have evolved through divergent evolution of habitat preference, or (iii) the differences between ecotypes are solely due to phenotypic plasticity or epigenetic variation. This reference genome is also a prerequisite for the planning, design, and implementation of conservation measures for this endangered species, taking into account its intraspecific diversity. Furthermore, it holds broader implications for population genomic studies of the species-rich genusCoenonympha, which includes some of the most endangered butterfly taxa in Europe. The complete genome sequence was assembled into 30 contiguous chromosomal pseudomolecules (sex chromosomes included). This chromosome-level assembly encompasses 0.39 Gb, composed of 385 contigs and 62 scaffolds, with contig and scaffold N50 values of 2.8 Mb and 14.2 Mb, respectively.
Asymmetric nature of MscL opening revealed by molecular dynamics simulations
Authors: Rogacheva Olga N. , Kopec Wojciech 
Published: February 2025

ABSTRACTWe employed all-atom unbiased molecular dynamics to simulate the L17A, V21A mutant of MscL. Under a tension of 30 mN/m, the closed state adopts a funnel-like conformation. Subsequently, five chains of MscL undergo sequential transitions, leading to the formation of the asymmetric states (S1, S2, etc.). After the first chain crossed the barrier, the next transition favors the neighboring clockwise chain. This study focuses on the S1 state. Within its "open" fragment, this state is similar to the expanded state of M. acetivorans MscL, and has a conductance 10 times lower than the open state. We applied committor analysis and a nonlinear regression model to construct a reaction coordinate for the transition between the closed and the S1 state as a linear combination of interatomic distances and contacts. The main contributions to the reaction coordinate are: 1) the disruption of the "cytoplasmic" contact sites between the considered chain and two adjacent chains 2) the delipidation of the lipid-binding pocket, formed by the I82, V86, and V22 residues. The result is the pulling of the considered chain via the "cytoplasmic" tension sensor. The free energy profile along the reaction coordinate was calculated using the umbrella sampling approach. The S1 state is approximately 5 kJ/mol more favorable than the closed state under tension, and the height of the free energy barrier for the transition towards the S1 state is approximately 10 kJ/mol. This barrier is in a reasonable agreement with the corresponding average transition time, estimated to be 133 ± 13 ns.Abstract Figure
Presaccadic modulation of lateral interactions
Authors: Mueller de Melo Gabriela , de Oliveira Pitorri Isabella , Rohenkohl Gustavo 
Published: February 2025

ABSTRACTLateral interactions are pervasive in early visual processing, contributing directly to processes such as object grouping and segregation. This study examines whether saccade preparation - known to affect visual perception - modulates lateral interactions. In a psychophysical task, participants were instructed to detect a Gabor target flanked by two adjacent Gabors, while they either prepared a saccade to the target or maintained central fixation. Flanker gratings could be iso- or orthogonally oriented to the target and were positioned at three different distances (4λ,8λ, and 16λ). Contrast thresholds for target detection were estimated in each condition using a 3-down/1-up staircase procedure. The results showed that in both presaccadic and fixation conditions, the target was suppressed at the shortest flanker distance (4λ), revealed by markedly higher thresholds in iso-oriented compared to orthogonal flanker configurations. Lateral interaction effects were completely abolished at their largest separation (16λ. Interestingly, at the intermediate flanker distance (8λ), we observed an increase in suppression of targets presented during the presaccadic period, but not in the fixation condition. This result suggests that saccade preparation can modulate lateral interactions, promoting suppressive effects over larger distances. These findings are consistent with the visual remapping phenomenon observed before saccade execution, especially the convergent remapping of receptive fields in oculomotor and visual areas. Finally, this presaccadic expansion of inhibitory lateral interactions could assist target selection by suppressing homogeneous peripheral signals - such as iso-oriented collinear patterns - while prioritizing the processing of more salient visual information.
In pemphigus, cell detachment, but not autoantibody binding, induces cell-wide, long-lasting transcriptomic and proteomic changes
Authors: Hartmann Veronika , Guo Sen , Rahimi Siavash , Radine Uta , Malheiros Danielle , Salviano-Silva Amanda , Bumiller-Bini Hoch Valeria , Cipolla Gabriel A. , Calonga-Solis Veronica , Künstner Axel , Burmester Imke , Ludwig Ralf J. , Hariton William V.J. , Hammers Christoph M. , Müller Eliane J. , Busch Hauke , Hundt Jennifer E. 
Published: February 2025

AbstractDesmoglein 1 (DSG1) and desmoglein 3 (DSG3) are adhesion molecules that maintain intercellular connections between epidermal, hair follicle, and mucosal keratinocytes. Autoantibodies (AAbs) targeting these molecules ultimately lead to the blister formation characteristic of pemphigus vulgaris (PV) or pemphigus foliaceus (PF). To investigate the molecular events following autoantibody binding up to 48 hours, we quantified transcriptome and proteome dynamics during split formation in a human skin organ culture (HSOC) model for PV and in a 2D cell-culture model for PV and endemic PF. Treatment of the cells in 2D culture with PX43, a single-chain variable fragment targeting DSG1/3, or with endemic PF anti-DSG1 IgG yielded neither a significant transcriptome nor a proteome response over time relative to the respective IgG controls. When treating the HSOC model with mouse antibody AK23 (targeting DSG3) or with PX43, only the latter induced split formation. In the absence of split formation, no differentially regulated pathways were detected at the transcriptomic level. Split formation, observed as early as 5 hours post-injection, was associated with significant and sustained upregulation of IFNγ and TNFα-related genes, mediated by upstream NFκB, MAPK, and JAK-STAT pathways. The gene expression changes, corroborated by proteomics data, were strongly correlated with early wounding and keratinocyte detachment, as well as the transcriptome profile in the skin from PV patients, while inversely associated with keratinocyte differentiation and cell stretching. The co-occurrence of well-wide and long-lasting transcriptome and proteome responses with split formation suggests that PF-IgG and PV-related AAbs neither induce downstream transcriptome nor proteome changes directly. Rather, these changes appear to be secondary effects resulting from reduced adhesion and mechanical-stress-induced split formation of keratinocytesin vitroand most likely in patient skinin vivo.
Induction of Chimera States in Hindmarsh-Rose Neurons through Astrocytic Modulation: Implications for Learning Mechanisms
Authors: Azad Fatemeh , Shouraki Saeed Bagheri , Nazari Soheila , Chan Mansun 
Published: February 2025

AbstractChimera states, a form of partial synchronization in neural networks, are characterized by the coexistence of synchronized and asynchronous regions. These states are crucial for various cognitive functions, such as learning and information processing. Conversely, abnormal synchronization—often referred to as hyper-synchronization—can lead to pathological conditions such as epilepsy and Parkinson’s disease. Understanding the mechanisms underlying synchronization can provide valuable insights for developing effective therapeutic strategies for these disorders. Astrocyte, a primary type of glial cell, plays a pivotal role in modulating neural synchrony. They influence the synchronization threshold of neurons by providing feedback through the release of gliotransmitters, promoting group firing of neurons within the astrocyte’s domain. This research aims to explore how astrocytes can facilitate the conversion of hyper-synchronized states into healthy chimera states within neural networks. This process is vital for maintaining normal brain function and may be critical to advancing treatments for neurological conditions. We analyzed how astrocytes can induce chimera states in nonlocally two-dimensional Hindmarsh-Rose neurons, which serve as realistic models of neuronal ensembles. Our findings demonstrate that astrocytes can effectively transition unhealthy hyper-synchronization states into healthy chimera states. Furthermore, by analyzing time spans, spatiotemporal patterns, inter-spike interval distributions (ISI), and phase plane diagrams of 2D H-R neurons, we validated our hypothesis about the crucial role of astrocytes in the development of chimera states. The outcomes may pave the way for innovative therapeutic approaches to restore normal neural activity patterns, ultimately improving patient outcomes in conditions such as epilepsy and Parkinson’s disease.