bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Surgical Technique for Individual Control of the Muscles of the Rabbit Lower Hind-limb
Authors: Baggaley Michael , Sawatsky Andrew , Ross Stephanie A. , Herzog Walter 
Published: January 2024

AbstractLittle is known regarding the precise muscle, bone, and joint actions resulting from individual and simultaneous muscle activation(s) of the lower limb. Anin situexperimental approach is described herein to control the muscles of the rabbit lower hind-limb, including the medial and lateral gastrocnemius, soleus, plantaris, and tibialis anterior. The muscles were stimulated using nerve-cuff electrodes placed around the innervating nerves of each muscle. Animals were fixed in a stereotactic frame with the ankle angle free to rotate in the sagittal plane to quantify the behaviour of the lower hind-limb muscles. To demonstrate the efficacy of the experimental technique, isometric plantarflexion torque was measured at a 90 ° ankle joint angle at a stimulation frequency of 100, 60, and 30 Hz. Individual muscle torque and the torque produced during simultaneous activation of all plantarflexor muscles are presented for four animals. These results demonstrate that the experimental approach was reliable, with insignificant variation in torque between repeated contractions. The experimental approach described herein provides the potential for measuring a diverse array of muscle properties which is important to improve our understanding of musculoskeletal biomechanics.HighlightsA reliable surgical technique was developed for isolated activation of the plantarflexor muscles and the tibialis anterior in the rabbit. Joint torque data are presented for four rabbits at a single joint angle and three stimulation frequencies.
Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks
Authors: Candia Julián , Ferrucci Luigi 
Published: January 2024

AbstractPathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.
Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging
Authors: Cheng Huiyong , Miller Dawson , Southwell Nneka , Porcari Paola , Fischer Joshua L. , Taylor Isobel , Michael Salbaum J. , Kappen Claudia , Hu Fenghua , Yang Cha , Keshari Kayvan R. , Gross Steven S. , D’Aurelio Marilena , Chen Qiuying 
Published: January 2024

AbstractMass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
A Scoping Review on Influence of Socioeconomic Status on Antenatal Care Utilization and Pregnancy Outcomes in Sub-Saharan Africa
Authors: Esther Adeyemo Queen , Yahaya Haphsheitu , Esther Ajayi O. , Aboagye-Mensah Priscilla , Blessing Adeyemo J. , Ikome Theckla E. 
Published: January 2024

AbstractMaternal and perinatal mortality are the most adverse pregnancy outcomes of public health concerns. Although, slowly declining, Sub-Saharan Africa (SSA), has been reported as one of the regions with the highest incidence globally. Regions in SSA where these problems are prominent have been reported to have poor access to antenatal care services. Since socioeconomic factors are major factors influencing the use of antenatal care services and pregnancy outcomes. This study, therefore, aimed to explore the socioeconomic determinants of antenatal care utilization and pregnancy outcomes in Sub-Saharan countries. Studies were systematically searched using credible search engines, whereby 82 studies based on the selection criteria from eight countries with reported maximum burden of study were found. Consistently across all reviewed studies, poor socioeconomic status was a significant determinant of Antenatal care utilization thus leading to poor pregnancy outcomes, particularly, low income, and education. The impact of occupation on the other hand has been poorly studied. Poor socioeconomic factors also limit the use of antenatal care services, increasing the burden of the deaths. The study therefore submits that, interventions, and policies to reduce maternal and perinatal mortality should focus on improving pregnant women’s’ lives by improving access to antenatal care services pre- and postnatal period.
Picturing plant biodiversity from airborne environmental DNA
Authors: Thuillet Anne-Céline , Morisot Didier , Renno Jean-François , Scarcelli Nora , Serret Julien , Mariac Cédric 
Published: January 2024

ABSTRACTWhile eDNA approaches have gained interest over the past decades all types of organisms have not been addressed evenly. In particular terrestrial plants have been the subject of less attention. Here we address the possibility to represent plant biodiversity from airborne environmental DNA (eDNA) sampling and metabarcoding. We collected air using a biological air sampler in the Botanical Garden of Montpellier (France) and compared the list of revealed plant species to the botanical inventory of the Garden. Ninety-two plant species could be detected from three sampling points across the 4,6 ha of the Garden, after one hour sampling allowing to filter 9 m3of air. We recorded the plants carrying flowers at the time of the experiment, which allowed us to estimate that plants flowering at the time of the sampling could be detected 10 times more easily than plants that were not, given the number of plants carrying flowers. However, flowering is far from being required as a vast majority of plants still was detected without flowering. We also show that not all species orders are detected with the same probability, tree species being better represented in the sample than herbal plants, given the number of trees present in the garden. Finally using diagnostic species, present only once in the garden, we estimate that the maximum sampling distance allowed by the biological air sampler is at least 110 m. Our study underlines that air sampling is a promising method for monitoring terrestrial plant biodiversity and highlights the parameters that should be adjusted to optimize the approach.
Mapping planktonic communities: a network approach to assess the role of scale and centrality on their diversity and composition
Authors: Cunillera-Montcusí David , Bengtsson Mia , Matthews Blake , Preiler Christian , Horváth Zsófia , Vad Csaba F. , Ptacnik Robert 
Published: January 2024

AbstractThe distribution of habitats across a landscape and their centrality gradient are key elements defining the effective pathways of dispersal, and thus of metacommunity assembly. Understanding how centrality shapes diversity patterns is essential for predicting the impact of future landscape changes on diversity. While alpine lakes have been extensively studied, often considering the fluvial network as a potential landscape, small planktonic communities have frequently been overlooked as potential dispersers due to their assumed ubiquity. In this study, we investigate the diversity patterns of alpine lake planktonic communities along lake networks constructed at different scales, ranging from 6.5 to 650 km and the fluvial network. We sampled 55 lakes in the northern Alps (16S, 18S, phytoplankton and zooplankton) and calculated several diversity metrics (alpha, beta diversity and LCBD) and multivariate analysis. We then constructed several networks responding to different scales, determined their centrality gradients, and finally explored their relationship with the diversity of each planktonic group. We expected that a groups’ diversity would relate differently across scales based on body size, but the outcomes were varied. Bacterioplankton and zooplankton diversity were both affected across scales higher than 100 km, whereas phytoplankton appeared completely unrelated to centrality. Nonetheless, we could observe that when significant, the relationships between diversity and centrality were shared among organisms. These findings not only underscore that planktonic organisms are influenced by landscape configurations larger than the fluvial system but also emphasise the critical role of dispersal for these groups and the scales at which it impacts metacommunity assembly.Significance statementWhile dispersal is widely recognized as a key driver of assembly, some groups and systems remain insufficiently explored to fully grasp the impact of landscape and dispersal on their assembly. Planktonic communities have traditionally been considered ubiquitous and detached from regional-level structure, primarily due to their small size, leading to the notion that “everything is everywhere”. Additionally, alpine lake communities have traditionally been perceived as solely connected through fluvial systems. In this study, we challenge these notions by demonstrating how planktonic communities are indeed influenced by the relative positioning of lakes in the landscape, with significant impacts occurring at larger scales, spanning hundreds of kilometres. However, not all planktonic groups responded uniformly to the analysed factors, emphasizing the marked differences among groups and the diverging drivers shaping planktonic metacommunities.
Integrative analysis in head and neck cancer reveals distinct role of miRNome and methylome as tumour epigenetic drivers
Authors: Mandić Katarina , Milutin Gašperov Nina , Božinović Ksenija , Dediol Emil , Krasić Jure , Sinčić Nino , Grce Magdalena , Sabol Ivan , Barešić Anja 
Published: January 2024

AbstractBackgroundHead and neck cancer is the sixth most common malignancy worldwide, with the relatively low 5-year survival rate, mainly because it is diagnosed at a late stage. Infection with HPV is well known etiology, which affects nature of these cancers and patients’ survival. Besides, it is considered that the main driving force for this type of cancer could be epigenetics. In this study we aimed to find potential epigenetic biomarkers, by integrating miRNome, methylome, and transcriptome analyses.MethodsFrom the fresh head and neck cancer tissue samples, we chose a group for miRNome, methylome and transcriptome profiling, in comparison to adequate control samples. Bioinformatics analyses are performed in R v4.2.2. Count normalization and group differential expression for mRNA and the previously obtained miRNA count data was performed with DESeq2 v1.36. Gene set enrichment analysis was performed and visualized using gProfiler2 v0.2.1 Identification of miRNA targets was performed by querying in miRTarBase using multiMiR v1.18.0. Annotation of CpG sites merging into islands was obtained from RnBeads.hg19 v1.28.0. package. For the integrative analysis we performed kmeans clustering using stats v4.2.2 package, using 8-12 clusters and nstart 100.ResultsWe found that transcriptome analysis divides samples into cancers and controls clusters, with no relation to HPV status or cancer anatomical location. Differently expressed genes (n=2781) were predominantly associated with signaling pathways of tumour progression. We identified a cluster of genes under control of the transcription factor E2F, that are significantly underexpressed in cancer tissue, as well as T cell immunity genes and genes related to regulation of transcription. Among overexpressed genes in tumours we found those that belong to cell cycle regulation and vasculature. A small number of genes were found significantly differently expressed in HPV positive versus HPV negative tumours (for example NEFH, ZFR2, TAF7L, ZNF541, and TYMS).ConclusionsIn this comprehensive study on an overlapping set of samples where the integration of miRNome, methylome and transcriptome analysis were performed for head and neck cancer, we demonstrated that the majority of genes were associated exclusively with miRNome or methylome and, to a lesser extent, under control of both epigenetic mechanisms.
A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data
Authors: Zingaro Alberto , Ahmad Zan , Kholmovski Eugene , Sakata Kensuke , Dede’ Luca , Morris Alan K. , Quarteroni Alfio , Trayanova Natalia A. 
Published: January 2024

ABSTRACTStroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the CHA2DS2-VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.
Obesity Facilitates Sex-Specific Improvement In Cognition And Neuronal Function In A Rat Model Of Alzheimer’s Disease
Authors: Lai Aaron Y. , Almanza Dustin Loren V. , Ribeiro Jessica A. , Hill Mary E. , Mandrozos Matthew , Koletar Margaret M. , Stefanovic Bojana , McLaurin JoAnne 
Published: January 2024

ABSTRACTObesity reduces or increases the risk of developing Alzheimer’s disease (AD) depending on whether it is assessed in mid-life or late-life. There is currently no consensus on the relationship between obesity and AD or the mechanism or their interaction. Here, we aim to differentiate the cause-and-effect relationship between obesity and AD in a controlled rat model of AD. We induced obesity in 9-month-old TgF344-AD rats, that is pathology-load wise similar to early symptomatic phase of human AD. To more accurately model human obesity, we fed both TgF344-AD and non-transgenic littermates a varied high-carbohydrate-high-fat diet consisting of human food for 3 months. Obesity increased overall glucose metabolism and slowed cognitive decline in TgF344-AD rats, specifically executive function, without affecting non-transgenic rats. Pathological analyses of prefrontal cortex and hippocampus showed that obesity in TgF344-AD rats produced varied effects, with increased density of myelin and oligodendrocytes, lowered density and activation of microglia that we propose contributes to the cognitive improvement. However, obesity also decreased neuronal density, and promoted deposition of amyloid-beta plaques and tau inclusions. After 6 months on the high-carbohydrate-high-fat diet, detrimental effects on density of neurons, amyloid-beta plaques, and tau inclusions persisted while the beneficial effects on myelin, microglia, and cognitive functions remained albeit with a lower effect size. By examining the effect of sex, we found that both beneficial and detrimental effects of obesity were stronger in female TgF344-AD rats indicating that obesity during early symptomatic phase of AD is protective in females.
Intense storms affect sinking particle fluxes after the North Atlantic diatom spring bloom
Authors: Romanelli Elisa , Giering Sarah Lou Carolin , Estapa Margaret , Siegel David A. , Passow Uta 
Published: January 2024

AbstractThe sinking of large particles (i.e., marine snow) has long been recognized as a key pathway for efficient particulate organic carbon (POC) export to the ocean interior during the decline of spring diatom blooms. Recent work has suggested that particles smaller than marine snow can also substantially contribute to POC export. However, a detailed characterization of small and large sinking particles at the end of blooms is missing. Here, we separately collected suspended and small and large sinking particles using Marine Snow Catchers and assessed their biogeochemical composition after the North Atlantic spring bloom in May 2021. During the three weeks of sampling, when four intense storms (maximum wind speeds 37 – 50 kts) created high turbulent energy dissipation rates and deepened the mixed layer, we observed two distinct sedimentation episodes. During the storm periods, sinking particles were dominated by small (diameter < 0.1 mm), slow-sinking (~18 m d−1), silica-rich particles that carried a moderate POC flux (< 6 mmol C m−2d−1) to 500 m depth. Once the storms ceased, the volume of large (diameter > 0.1 mm), fast-sinking (> 75 m d−1), carbon-rich marine snow aggregates (not fecal pellets) increased exponentially and POC fluxes at 100 m depth were more than fourfold greater (30±12 mmol C m−2d−1) than those during the previous event. The aggregates consisted of a mixed post-bloom plankton community. Our data suggest that the intense storms determined the timing, type, and magnitude of POC flux at the end of a spring phytoplankton bloom.
Existence and Stability Criteria for Global Synchrony and for Synchrony in two Alternating Clusters of Pulse-Coupled Oscillators Updated to Include Conduction Delays
Authors: Vedururu Srinivas Ananth , Canavier Carmen C. 
Published: January 2024

AbstractPhase Response Curves (PRCs) have been useful in determining and analyzing various phase-locking modes in networks of oscillators under pulse-coupling assumptions, as reviewed in Mathematical Biosciences, 226:77-96, 2010. Here, we update that review to include progress since 2010 on pulse coupled oscillators with conduction delays. We then present original results that extend the derivation of the criteria for stability of global synchrony in networks of pulse-coupled oscillators to include conduction delays. We also incorporate conduction delays to extend previous studies that showed how an alternating firing pattern between two synchronized clusters could enforce within cluster synchrony, even for clusters unable to synchronize themselves in isolation. To obtain these results, we used self-connected neurons to represent clusters. These results greatly extend the applicability of the stability analyses to networks of pulse-coupled oscillators since conduction delays are ubiquitous and strongly impact the stability of synchrony. Although these analyses only strictly apply to identical oscillators with identical connections to other oscillators, the principles are general and suggest how to promote or impede synchrony in physiological networks of neurons, for example. Heterogeneity can be interpreted as a form of frozen noise, and approximate synchrony can be sustained despite heterogeneity. The pulse-coupled oscillator model can not only be used to describe biological neuronal networks but also cardiac pacemakers, lasers, fireflies, artificial neural networks, social self-organization, and wireless sensor networks.AMS Subject Classification37N25, 39A06, 39A30, 92B25, 92C20