bluuebunny/crossref_metadata_2025_split huggingface dataset:
Placental biomarkers in second trimester maternal serum are associated with postpartum hemorrhage: a secondary analysis of the NuMoM2b dataset
Authors: Rutherford Julienne N. , George Erin K. , Erickson Elise N. 
Published: December 2024

AbstractPostpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide, which is often attributed to retained placenta (RP) after delivery. There are no biomarkers currently used to predict a risk of developing RP/PPH prior to labor. The objective of this study was to determine relationships between placental biomarkers measured in the first and second trimesters and proxy measures of postpartum blood loss relative to preeclampsia status in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset. 2,192 participants had placental analytes drawn during the first and second trimesters (9-13 and 16-22 weeks gestation, respectively); the outcome was a composite of retained placenta and/or PPH requiring blood transfusion (RP/PPH). Using Kruskal-Wallis tests, median differences in levels of soluble fms-like tyrosine kinase-1 (sFlt-1), placental growth factor (PlGF), sFlt-1/PlGF ratio, soluble endoglin (sEng), beta subunit of human chorionic gonadotropin (β-hCG), inhibin A (INHA), and pregnancy-associated protein-A (PAPP-A) were assessed between women with (n=67) and without (n=2125) RP/PPH overall and stratified by preeclampsia status. Women with RP/PPH had significantly higher median levels of sEng, β-hCG, INHA, PAPP-A in the second trimester and sFlt-1was higher in both first and second trimesters, which was observed again when stratifying by preeclampsia status. Our findings indicate that biomarkers associated with angiogenesis, particularly when measured in the second trimester, are important targets for further study of RP and/or PPH pathophysiology and potential risk screening development.
Gene-Burden Meta-Analysis of 748,879 Individuals Identifies LGI1-ADAM23 Protein Complex Association with Epilepsy
Authors: Lal Jessica Castrillon , Leu Costin , Boßelmann Christian M. , Ivaniuk Alina , Pérez-Palma Eduardo , Lal Dennis 
Published: December 2024

AbstractEpilepsy affects over 50 million individuals globally and has a substantial genetic component that remains to be completely understood. Traditional studies have focused on severe, early-onset cases enrolled through clinical or research settings. Recent biobank-based approaches, leveraging large-scale population datasets, offer opportunities to explore genetic associations in broader epilepsy phenotypes, including milder, later-onset forms. We analyzed data from over 750,000 individuals across the UK Biobank, All of Us, and Massachusetts General Brigham Biobank, including 20,026 individuals with epilepsy. Rare coding variant burden testing revealed a significant association withLGI1, a known epilepsy gene. Other top-ranked genes, includingGABRG2, ATP1A3, andADAM23, demonstrated strong enrichment for epilepsy-associated variants. Notably,ADAM23, previously linked to epilepsy in dogs and strong brain expression in humans, emerged as a novel candidate, potentially contributing to human epilepsy through its direct interaction withLGI1. Phenome-wide analyses highlighted the pleiotropic effects of epilepsy genes, withLGI1andADAM23predominantly associated with epilepsy, while other genes such asKRIT1, TSC1, andTSC2exhibited broader systemic involvement. Our study shows the potential of population-scale genomic data and suggests that integrating these datasets with deep phenotyping will uncover more novel insights into epilepsy genetics in the future.
Sex-specific survival but not tissue wasting in the KPP mouse model of pancreatic cancer-induced cachexia
Authors: Weinzierl Natalia M. , Putri Jayarani , Spitler Kathryn M. , Talbert Erin E. 
Published: December 2024

ABSTRACTCancer cachexia, a multifactorial condition resulting in muscle and adipose tissue wasting, reduces the quality of life of many people with cancer. Despite decades of research, therapeutic options for cancer cachexia remain limited. Cachexia is highly prevalent in people with pancreatic ductal adenocarcinoma (PDAC), and many animal models of pancreatic cancer are used to understand mechanisms underlying cachexia. One such model is theKrasLSL-G12D,Ptf1aCre-ER/+,Ptenflox/flox(KPP) model, which utilizes an inducible Cre recombinase to allow tumor development to be initiated at any age by tamoxifen administration. In our previous work, tumors were induced in KPP mice at 4 weeks of age. However, mice are still rapidly growing at this age, and a portion of the body weight differences seen between control and KPP mice is likely due to slowed growth of KPP mice. In our current study, pancreatic tumors were induced to develop with tamoxifen in KPP mice after rapid postnatal growth has slowed at 10 weeks of age (KPP10). Similar to our previous findings, KPP10 mice had lower body, muscle, and adipose tissue weights compared to non-tumor mice, and these differences were similar between male and female mice. However, male mice experienced greater relative weight loss. Unexpectedly, we identified that overall survival was significantly shorter in female KPP10 mice compared to KPP10 males. Greater body weight at tumor induction was associated with longer survival, suggesting that the sex difference in survival may be related to differences in body weight between male and female mice.NEW & NOTEWORTHY-Although male mice experience greater relative body weight losses, similar skeletal muscle and adipose tissue wasting occurs between male and female mice in theKrasLSL-G12D,Ptf1aCre-ER/+,Ptenflox/flox(KPP) model of pancreatic-cancer induced cachexia.-Greater weight loss in males may be related to longer survival. However, differences in tamoxifen dose relative to body weight may have accelerated tumor formation in female mice and therefore may be a relevant consideration for inducible tumor models.
Predicting microbial transcriptome using genome sequence
Authors: Fu Guohao , Yan Yujing , Shao Bin 
Published: December 2024

AbstractWe present TXpredict, a transformer-based framework for predicting microbial transcriptomes using annotated genome sequences. By leveraging information learned from a large protein language model, TXpredict achieves an average Spearman correlation of 0.53 in predicting gene expression for new bacterial genomes. We further extend this framework to predict transcriptomes for 900 additional microbial genomes spanning 276 genera, 69% of which remain uncharacterized at the transcriptional level. Additionally, TXpredict enables the prediction of condition-specific gene expression, providing a powerful tool for understanding microbial adaptation and facilitating the rational design of gene regulatory sequences.
When do measured representational distances reflect the neural representational geometry?
Authors: Botero Veronica Bossio , Kriegeskorte Nikolaus 
Published: December 2024

AbstractThe representational geometry of a brain region can be characterized by the distances among neural activity patterns for a set of experimental conditions. Researchers routinely estimate representational distances from brain-activity measurements that either sparsely sample the underlying neural population (e.g. neural recordings) or pool across the activity of many neurons (e.g. fMRI voxels). Here we use theory and simulations to clarify under what circumstances representational distances estimated from brain-activity measurements reflect the representational geometry of the underlying neural population, and what distortions must be expected under other circumstances. We demonstrate that the estimated representational distances are undistorted if single neurons are sampled at random. For voxels that take non-negatively weighted linear combinations, the resulting geometry is linearly distorted, correctly reflecting the population-mean dimension, while downscaling all orthogonal dimensions, for which the averaging cancels a large portion of the signal. Surprisingly, removing the mean from voxel patterns recovers the underlying representational geometry exactly in expectation under idealized conditions. This explains why the correlation distance, the most popular measure of representational dissimilarity in neuroimaging studies, “works” so well, yielding geometries that can appear similar between fMRI and neural recordings. The Euclidean (or Mahalanobis) distance computed after removing the mean of each pattern (without normalizing its variance) is an attractive alternative to the correlation distance in that it corrects for the inflated relative contribution of the population-mean dimension, while avoiding the drawback of the correlation distance: it can be large for confusable low-norm patterns, failing to reflect decodability. Our results demonstrate that measured representational distances reflect the neural representational geometry when (1) single neurons are sampled at random or (2) the weights with which the measured responses sample the neurons are drawn i.i.d. and (2a) the weights are drawn from a zero-mean distribution or (2b) the population mean is the same for all conditions or (2c) the mean is removed from each estimated pattern. We discuss practical implications for analyses of neural representational geometries.
Transient glycan-shield reduction induces CD4-binding site broadly neutralizing antibodies in SHIV-infected macaques
Authors: Morris Daniel J. , Gorman Jason , Zhou Tongqing , Lora Jinery , Connell Andrew J. , Li Hui , Liu Weimin , Roark Ryan S. , Campion Mary S. , Carey John W. , Habib Rumi , Li Yingying , Martella Christian L. , Park Younghoon , Singh Ajay , Sowers Kirsten J. , Teng I-Ting , Wang Shuyi , Chohan Neha , Ding Wenge , Lauer Craig , Lewis Emily , Mason Rosemarie D. , Rando Juliette M. , Peyton Lowrey , Schramm Chaim A. , Wagh Kshitij , Korber Bette , Seaman Michael S. , Douek Daniel C. , Haynes Barton F. , Kulp Daniel W. , Roederer Mario , Hahn Beatrice H. , Kwong Peter D. , Shaw George M. 
Published: December 2024

SUMMARYBroadly neutralizing antibodies (bNAbs) targeting the HIV-1 CD4-binding site (CD4bs) occur infrequently in macaques and humans and have not been reproducibly elicited in any outbred animal model. To address this challenge, we first isolated RHA10, an infection-induced rhesus bNAb with 51% breadth. The cryo-EM structure of RHA10 with HIV-1 envelope (Env) resembled prototypic human CD4bs bNAbs with CDR-H3-dominated binding. Env-antibody co-evolution revealed transient elimination of two Env CD4bs-proximal glycans near the time of RHA10-lineage initiation, and these glycan-deficient Envs bound preferentially to early RHA10 intermediates, suggesting glycan deletions in infecting SHIVs could consistently induce CD4bs bNAbs. To test this, we constructed SHIV.CH505.D3 with CD4bs-proximal glycan deletions. Infection of 10 macaques resulted in accelerated CD4bs bNAb responses in 8, compared with 1 of 115 control macaques. Glycan hole-based immunofocusing coupled to Env-Ab co-evolution can consistently induce broad CD4bs responses in macaques and thus serve as a model for HIV vaccine design.HighlightsOut of 115 wildtype HIV-1 Env bearing SHIV infected macaques, only one macaque (T681) developed CD4bs bNAbsCD4bs bNAbs in macaque T681 recognized Env similarly to previously described CDR-H3 dominated human CD4bs bNAbs and exhibited comparable breadth and potencyTransient CD4bs-proximal glycan deletions in macaque T681 preceded bNAb inductionA novel SHIV with CD4bs-proximal glycan holes and enhanced CD4bs antigenicity immunofocused B cell responses to the CD4bs and elicited cross-clade neutralizing responses in 80% of macaques
Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes
Authors: Wang Shih-Ting , Anahtar Melodi , Kim Daniel M. , Samad Tahoura S. , Zheng Claire M. , Patel Sahil , Ko Henry , Ngambenjawong Chayanon , Wang Cathy S. , Kirkpatrick Jesse D. , Kumar Vardhman , Fleming Heather E. , Bhatia Sangeeta N. 
Published: December 2024

AbstractBreath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting, and subsequently applied to diagnose lung cancer using an autochthonousAlk-mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.
A widespread family of viral sponge proteins reveals specific inhibition of nucleotide signals in anti-phage defense
Authors: Chang Renee B. , Toyoda Hunter C. , Hobbs Samuel J. , Richmond-Buccola Desmond , Wein Tanita , Burger Nils , Chouchani Edward T. , Sorek Rotem , Kranzusch Philip J. 
Published: December 2024

SummaryCyclic oligonucleotide-based antiviral signaling systems (CBASS) are bacterial anti-phage defense operons that use nucleotide signals to control immune activation. Here we biochemically screen 57 diverseE. coliandBacillusphages for the ability to disrupt CBASS immunity and discover anti-CBASS 4 (Acb4) from theBacillusphage SPO1 as the founding member of a large family of >1,300 immune evasion proteins. A 2.1 Å crystal structure of Acb4 in complex with 3′3′-cGAMP reveals a tetrameric assembly that functions as a sponge to sequester CBASS signals and inhibit immune activation. We demonstrate Acb4 alone is sufficient to disrupt CBASS activationin vitroand enable immune evasionin vivo. Analyzing phages that infect diverse bacteria, we explain how Acb4 selectively targets nucleotide signals in host defense and avoids disruption of cellular homeostasis. Together, our results reveal principles of immune evasion protein evolution and explain a major mechanism phages use to inhibit host immunity.
ProteoPlotter: an executable proteomics visualization tool compatible with Perseus
Authors: Olabisi-Adeniyi Esther , McAlister Jason A. , Ferretti Daniela , Cox Juergen , Geddes-McAlister Jennifer 
Published: December 2024

AbstractMass spectrometry-based proteomics experiments produce complex datasets requiring robust statistical testing and effective visualization tools to ensure meaningful conclusions are drawn. The publicly-available proteomics data analysis platform, Perseus, is extensively used to perform such tasks, but opportunities to enhance visualization tools and promote accessibility of the data exist. In this study, we developed ProteoPlotter, a user-friendly, executable tool to complement Perseus for visualization of proteomics datasets. ProteoPlotter is built on the Shiny framework for R programming and enables illustration of multi-dimensional proteomics data. ProteoPlotter provides mapping of one-dimensional enrichment analyses, enhanced adaptability of volcano plots through incorporation of Gene Ontology terminology, visualization of 95% confidence intervals in principal component analysis plots using data ellipses, and customizable features. ProteoPlotter is designed for intuitive use by biological and computational researchers alike, providing descriptive instructions (i.e., Help Guide) for preparing and uploading Perseus output files. Herein, we demonstrate the application of ProteoPlotter towards microbial proteome remodeling under altered nutrient conditions and highlight the diversity of visualizations enabled with the platform for improved biological characterization. Through its comprehensive data visualization capabilities, linked to the power of Perseus data handling and statistical analyses, ProteoPlotter facilitates a deeper understanding of proteomics data to drive new biological discoveries.
Cortical Changes Associated with Isha Kriya Meditation Revealed by Encephalography in Novice and Experienced Meditators – a Longitudinal Pilot Study
Authors: Vishnubhotla Ramana V. , Reed Preeti U. , Subramaniam Balachundhar 
Published: December 2024

AbstractBackgroundIsha Kriya (IK) is a widely available meditation practice that incorporates breathing regulation that has shown to improve self-reported symptoms of stress, anxiety, and depression. An increasing amount of research has been published on the effects of various meditative practices on scalp electroencephalography (EEG). However, the effects of IK on cortical activity have not been reported previously.MethodsHealthy volunteers aged 18 years or older were invited to participate. Participants were categorized as novice or experienced in meditation. EEG spectral features, computed during the eyes-closed condition before and soon after each IK meditation practice, were evaluated both at the start and after 6 weeks of IK meditation training.ResultsThis longitudinal study examined the effects of IK meditation on cortical state and trait patterns in a cohort of eight participants who practiced IK meditation over a period of 6 weeks. Across the two sessions, a simultaneous increase in global periodic alpha power was observed in multiple subjects (N=6) but this was not observed in all subjects/sessions. We observed an increase in periodic theta band power, particularly in the frontal regions, which emerged as a common state effect in all participants. Longitudinally, we observed an increased periodic gamma power in the resting state EEG in all the experienced meditators in the parietal occipital region. The changes in novices on the other hand was in the alpha and beta bands.ConclusionOverall, in this pilot study, we report the changes in the quantitative EEG of the practitioners of Isha Kriya meditation over a 6-week cycle and investigated the difference between the start and end of that period at an individual level. We recommend future studies with a larger sample size and over a longer duration.Trial registrationThis trial is registered with the US National Institutes of Health onClinicalTrials.govwith the trial identification numberNCT03459690on February 14, 2018.
Mapping Grip Force to Muscular Activity Towards Understanding Upper Limb Musculoskeletal Intent using a Novel Grip Strength Model
Authors: Lai Yujun , Abdel-Messih Elvera , Carmichael Marc G. , Member Gavin Paul 
Published: December 2024

AbstractThis study aims to evaluate a grip strength model designed to elucidate the relationship between measured grip force and muscular activity and assess its impact on an upper limb musculoskeletal model. This work aims to develop the grip strength model by utilizing a piecewise linear function based on the Woods and Bigland-Ritchie EMG-force model, which derives its parameters from 10 adult participants performing isometric and dynamic gripping tasks. Experimental results demonstrate the model’s efficacy in estimating surface electromyography (sEMG) readings from force measurements, with a mean root mean square error (RMSE) of 0.2035 and a standard deviation of 0.1207. Moreover, incorporating sEMG readings associated with grip force does not significantly affect the optimization of muscle activation in the upper arm, as evidenced by kinematic data analysis from dynamic tasks. This validation underscores the model’s potential to enhance musculoskeletal model-based motion analysis pipelines without distorting results. Consequently, this research emphasizes the prospect of integrating external models into existing human motion analysis frameworks, presenting promising implications for physical Human-Robot Interactions (pHRI).
Climate and performance-driven architectural floorplan optimization using deep graph networks
Authors: Yang Yang , Luo Hanzhong , Adibhesami Mohammad Anvar 
Published: December 2024

PurposeThis study introduces a novel approach to generating and optimizing energy-efficient and climate-responsive architectural floorplans.Design/methodology/approachThe DGraph-cGAN model utilizes advanced deep-learning techniques to produce diverse, realistic layouts that meet specific design constraints and functional requirements.FindingsThe results show significant energy savings (32.1% overall) across different building types and climate conditions, with reductions in energy use intensity, CO2 emissions and annual energy costs. Case studies demonstrate notable improvements in energy savings, CO2 emission reduction, daylight autonomy, thermal comfort and cost savings.Practical implicationsThe DGraph-cGAN model has great potential for advancing architectural design optimization, with opportunities for further refinement and application in various contexts.Originality/valueThis study contributes to developing a novel approach to optimizing architectural floorplans using deep learning techniques. It provides a valuable tool for architects and designers to create energy-efficient, climate-responsive buildings.
Reshoring before and during the COVID-19 pandemic in an advanced economy
Authors: Olhager Jan , Harfeldt-Berg Magnus 
Published: December 2024

Purpose The purpose is to investigate how the coronavirus disease 2019 (COVID-19) pandemic changed relocation behavior in an advanced economy. We compare manufacturing relocations before and during the pandemic to identify differences and similarities over time and between offshoring and backshoring.Design/methodology/approach We use an exploratory longitudinal trend survey approach with data from two surveys, the first before the pandemic, 2010–2015, and the second during the pandemic, 2020–2022. Both rounds were targeted to the entire population of Swedish manufacturing plants with 50 or more employees. We captured the same set of data for offshoring and backshoring projects in both surveys.Findings The pandemic did not stop manufacturing relocations. The extent of offshoring decreased, but the extent of backshoring increased. Labor costs remained a key driver for offshoring, but a trade-off versus lead time, flexibility and risk were observed, suggesting a tension between labor cost and a strive for creating shorter supply chains for offshoring. Lead time, logistics costs, market proximity and risk formed a new backshoring construct, with an emphasis on short supply chains, and that increased significantly in importance. At the same time, the importance of quality decreased, creating a need to balance quality against the pursuit of short supply chains for backshoring. Thus, local supply chains seem to be a desired outcome for any manufacturing relocation, suggesting a move towards a multi-local supply chain setup for the global manufacturing footprint.Originality/value This is the first longitudinal survey study that captures offshoring as well as backshoring before and during the pandemic. The results offer unique insights into the COVID-19-induced impacts on manufacturing relocations as the same total population was sampled before and after the pandemic, and it provides empirical evidence that neither offshoring nor backshoring are “steady-state” concepts but changes over time.