bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid speciation in the holopelagic ctenophoreMnemiopsisfollowing glacial recession
Authors: Ketchum Remi N. , Smith Edward G. , Toledo Leandra M. , Leach Whitney B. , Padillo-Anthemides Natalia , Baxevanis Andreas D. , Reitzel Adam M. , Ryan Joseph F. 
Published: October 2024

AbstractUnderstanding how populations diverge is one of the oldest and most compelling questions in evolutionary biology. An in depth understanding of how this process operates in planktonic marine animals, where barriers for gene flow are seemingly absent, is critical to understanding the past, present, and future of ocean life.Mnemiopsisplays an important ecological role in its native habitat along the Atlantic coast of the Americas and is highly destructive in its non-native habitats in European waters. Although historical literature described three species ofMnemiopsis, the lack of stable morphological characters has led to the collapse of this group into a single species,Mnemiopsis leidyi. We generate high-quality reference genomes and use a whole-genome sequencing approach to reveal that there are two species ofMnemiopsisalong its native range and show that historical divergence between the two species coincides with historical glacial melting. We define a hybridization zone between species and highlight that environmental sensing genes likely contribute to the invasive success ofMnemiopsis. Overall, this study provides insights into the fundamental question of how holopelagic species arise without clear barriers to gene flow and sheds light on the genomic mechanisms important for invasion success in a highly invasive species.
Large Serine Integrase Off-Target Discovery with Deep Learning for Genome Wide Prediction
Authors: Bakalar Matthew H. , Biondi Thomas , Liang Xiaoyu , Santesmasses Didac , Bara Anne M. , Mehta Japan B. , Wang Jie , Hazelbaker Dane Z. , Finn Jonathan D. , O’Connell Daniel J. 
Published: October 2024

AbstractLarge Serine Integrases (LSIs) hold significant therapeutic promise due to their ability to efficiently incorporate gene-sized DNA into the human genome, offering a method to integrate healthy genes in patients with monogenic disorders or to insert gene circuits for the development of advanced cell therapies. To advance the application of LSIs for human therapeutic applications, new technologies and analytical methods for predicting and characterizing off-target recombination by LSIs are required. It is not experimentally tractable to validate off-target editing at all potential off-target sites in therapeutically relevant cell types because of sample limitations and genetic variation in the human population. To address this gap, we constructed a deep learning model named IntQuery that can predict LSI activity genome-wide. For Bxb1 integrase, IntQuery was trained on quantitative off-target data from 410,776 crypticattBsequences discovered by Cryptic-seq, an unbiased in vitro discovery technology for LSI off-target recombination. We show that IntQuery can accurately predict in vitro LSI activity, providing a tool forin silicooff-target prediction of large serine integrases to advance therapeutic applications.
Elevated developmental temperatures below the lethal limit reduceAedes aegyptifertility
Authors: Pekľanská Miriama , van Heerwaarden Belinda , Hoffmann Ary A. , Nouzová Marcela , Šíma Radek , Ross Perran A. 
Published: October 2024

AbstractAedes aegyptimosquitoes are the principal vectors of dengue and continue to pose a threat to human health, with ongoing urbanization, climate change, and trade all impacting the distribution and abundance of this species. Hot periods are becoming increasingly common and their impacts on insect mortality have been well established, but they may have even greater impacts on insect fertility. In this study, we investigated the impacts of high temperatures onAe. aegyptifertility both within and across generations. Mosquitoes developing under elevated temperatures exhibited higher critical thermal maxima (CTmax) reflecting developmental acclimation, but their fertility declined with increasing developmental temperature. In females, elevated developmental temperatures decreased fecundity while in males it tended to decrease egg hatch proportions and the proportion of individuals producing viable offspring. Rearing both sexes at 35°C increased fecundity in the subsequent generation but effects of elevated temperatures persisted across gonotrophic cycles within the same generation. Moreover, exposure of adults to 35°C further decreased fertility beyond the effects of developmental temperature alone. These findings highlight sub-lethal impacts of elevated temperatures onAe. aegyptifertility and plastic responses to thermal stress within and across generations. This has significant implications for mosquito populations thriving in increasingly warmer environments.
Inferring Gene Regulatory Network Based on scATAC-seq Data with Gene Perturbation
Authors: Shao Wei , Liu Yuti , Zhang Shuang , Chen Shuang , Liu Qiao , Zeng Wanwen 
Published: October 2024

AbstractGene regulatory networks (GRNs) are critical blueprints for understanding gene regulation and the intricate interactions that drive biological processes. Recent advances have highlighted the potential of single-cell ATAC-seq (scATAC-seq) data in GRN inference, offering unprecedented insights into how chromatin accessibility plays an important part in gene regulation. However, existing methods often fall short in providing a quantitative and holistic depiction of regulatory relationships, particularly in capturing the strength, direction, and type of gene regulation simultaneously. In this paper, we present a novel approach that addresses these limitations by leveraging genetically perturbed scATAC-seq data to infer more comprehensive and accurate GRNs. Our method advances the field by integrating pre- and post-perturbation chromatin accessibility data, enabling the construction of GRNs that more accurately reflect the dynamic regulatory landscape. Through rigorous evaluation on seven real datasets, we demonstrate the method’s superior performance in reconstructing GRNs with enhanced precision and interpretability. This work significantly contributes to the field by providing a robust framework for GRN inference, with broad implications for understanding gene regulation in complex biological systems.
Prognostic Stratification of Familial Hypercholesterolemia Patients Using AI Algorithms: A Gender-Specific Approach
Authors: Zamora A , Masana L , Civeira F , Ibarretxe D , Fanlo-Maresma M , Vila A , Tembra Manuel Suárez , Victoria Marco-Benedí , Alvarez-Sala-Walther Luis A. , Camacho C 
Published: October 2024

AbstractBackgroundFamilial Hypercholesterolemia (FH) is the most prevalent autosomal dominant disorder, affecting about 1 in 200-250 individuals, with an estimated 30 million patients globally. It is the leading cause of early and aggressive coronary artery disease (CAD).ObjectiveTo developed an artificial intelligence (AI) and machine learning (ML) algorithm for cardiovascular risk stratification in a FH population, emphasizing sex-specific differences and model explainability.MethodsWe analyzed patients with genetically confirmed FH or a score greater than 8 on the Dutch Lipid Clinics Network (DLCN) criteria from the National Registry of the Spanish Atherosclerosis Society, including individuals enrolled from January 2010 to December 2017. The model utilized a comprehensive dataset incorporating family history, clinical characteristics, laboratory results, genetic data, imaging studies, and lipid-lowering treatment details. Eighty percent of the population was allocated for training the AI algorithm, while the remaining 20% was used for testing and 70/30 population for internal validation. A Histogram-based Gradient Boosting Classification Tree was used. The stability of the AI system was assessed using K-fold cross-validation. Shap methodology analyzed the influence of different variables by sex. Youden’s J statistic established the optimal cutoff point for identifying very high cardiovascular risk.ResultsA total of 1.764 patients were included (51.8% women), among whom 264 experienced major adverse cardiovascular events (MACE), with 8% being women. Notably, 52% experienced a cardiovascular event before age 50, and 37% had subclinical atherosclerosis. The final model incorporated 82 variables, achieving metrics of precision for MACE accuracy (0.92), recall (0.89), F1 score (0.91), and ROC (0.88; 95% CI, 0.85-0.90), revealing significant sex-based differences. Women showed a lower association with MACE compared to men, although this effect diminished with the inclusion of multiple variables, particularly in younger women. In the model, age, GGT levels, and subclinical disease significantly impacted risk for women, while year of birth, age at initiation of statin treatment and HbA1c levels were more influential for men. The optimal risk threshold was 0.25 for the association with of MACEConclusionAI-ML algorithms are promising tools for enhancing vascular risk stratification in patients with FH, revealing critical sex-based differences.
Effect of high-altitude exposure on skeletal muscle mitochondrial subcellular distribution, ultrastructure and respiration in sea-level residents
Authors: Schytz Camilla Tvede , Nielsen Joachim , Ørtenblad Niels , Lundby Anne-Kristine Meinild , Jacobs Robert Acton , Lundby Carsten 
Published: October 2024

AbstractEndurance exercise performance is associated with a well-developed skeletal muscle mitochondrial network, which is composed of subsarcolemmal mitochondria interconnected with intermyofibrillar mitochondria bending around the myofibrils. High-altitude exposure is typically incorporated in elite sport training regimens, but little is known about how this network adapts to an environment characterised by tissue hypoxia. For this reason, we investigated how high-altitude exposure affects mitochondrial subcellular distribution, ultrastructure, respiratory control, and intrinsic mitochondrial respiratory capacity. Nine healthy and recreationally active sea-level residents (eight males and one female) resided at an altitude of 3454 m with biopsies collected from the vastus lateralis muscle before and after 7 and 28 days at high altitude. The muscular mitochondrial volume density (MitoVD) increased after high-altitude exposure, driven by an increase in the intermyofibrillarMitoVD. This was however accompanied by a decreased cristae surface area per skeletal muscle fibre volume (MuscularCD) because of a decline in the cristae surface area per mitochondrial volume (MitoCD). Despite a reducedMuscularCD, mass-specific maximal coupled respiration (OXPHOS_CII+CI+ETF) increased slightly, and was considerably elevated when normalised toMuscularCD, suggesting intrinsic adaptations to high altitude. The difference between cristae-specificOXPHOS_CII+CI+ETFand an associated cristae-specific leak respiration (Leak_CII+CI+ETF) indicated a markedly higher degree of coupling between the electron flow in the electron transport system and ATP production. As the effect size 95% confidence intervals includes trivial effects the results need to be substantiated. In conclusion, high-altitude exposure altered mitochondrial subcellular distribution, ultrastructure and induced intrinsic respiratory adaptations.
Cascading consequences of shifting ice phenology in an exploited lake
Authors: Goto Daisuke , Dunlop Erin S. , Young Joelle D. , Jackson Donald A. 
Published: October 2024

AbstractIce phenology (onset and breakup of ice cover) regulates the timings of seasonal life history events of many animal and plant species in high-latitude lakes in the Northern Hemisphere, promoting the coexistence of species that share resources like food and shelter. Increasingly warmer, more variable Earth’s climate is, however, shifting ice phenology, modifying habitat conditions, and in turn reshaping the population and interaction dynamics of many species, threatening their persistence. Applying multivariate autoregressive state-space time series modeling to extensive historical and contemporary monitoring records (spanning 36 to 113 years) of limnological and biological surveys and fishery catches, we explore how shifting ice phenology propagates through a food web over time by modulating thermal profiles and limnological properties in Lake Simcoe, a large, intensely exploited system in southern Ontario, Canada. Analysis shows that increasingly earlier ice breakups, later ice freeze-ups, earlier thermal stratification onset, and later turnover since the early 1980s, attenuated algal production. This in turn, combined with increasingly more abundant invasive species (dreissenids) that promoted greater water clarity over prolonged stratification periods, diminished zooplankton abundances. These limnological changes filtered through plankton communities further modified the population and community dynamics of higher trophic-level species like fish with differential thermal preferences, interactively with time-varying non-climate drivers. Although management interventions–reductions in nutrient loading and fishing effort in particular–in recent decades aided the recoveries of depleted cold-adapted fish populations (including lake trout, lake whitefish, and cisco), their interaction patterns and strengths with species adapted to warmer conditions were reshuffled by advanced timings of ice breakup and delayed timings of ice freeze-up. These findings reveal how complex food web dynamics can emerge from ecologically divergent responses that reshape species interactions within and among trophic levels under shifting ice phenology filtered through bottom-up processes, likely buffering destabilizing effects of a changing climate.
Preserving Hidden Hierarchical Structure: Poincaré Distance for Enhanced Genomic Sequence Analysis
Authors: Ali Sarwan , Mansoor Haris , Chourasia Prakash , Khan Imdad Ulla , Pattersn Murray 
Published: October 2024

AbstractThe analysis of large volumes of molecular (genomic, proteomic, etc.) sequences has become a significant research field, especially after the recent coronavirus pandemic. Although it has proven beneficial to sequence analysis, machine learning (ML) is not without its difficulties, particularly when the feature space becomes highly dimensional. While most ML models operate with the conventional Euclidean distance, the hidden hierarchical structure present among a set of phylogenetically related sequences is difficult to represent in Euclidean space without losing much information or requiring many dimensions. Since such hierarchical structure can be informative to analysis tasks such as clustering and classification, we propose two measures for generating a distance matrix from a set of sequences based on distance in the Poincaré disk model of hyperbolic geometry, or thePoincaré distance, for short. Such a distance measure can allow to embedding of even a fully resolved phylogenetic tree in just two dimensions with minimal distortion to any hierarchical structure. Our first approach is based purely on the classical Poincaré distance, while the other approach modifies this distance by combining the Euclidean norms and the dot product between the sequence representations. A thorough analysis of both measures demonstrates its superiority in a variety of genomic and proteomic sequence classification tasks in terms of efficiency, accuracy, predictive performance, and the capacity to capture significant sequence correlations. These approaches perform better than existing state-of-the-art methods across the majority of evaluation metrics.
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: Data from the Araraquara Cohort Study
Authors: Victor Audêncio , Almeida Francielly , Xavier Sancho Pedro , Rondó Patrícia H.C. 
Published: October 2024

AbstractBackgroundLow birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algorithms, including Random Forest, XGBoost, Catboost, and LightGBM.MethodsMachine learning algorithms (Random Forest, XGBoost, Catboost, and LightGBM) were trained and evaluated using cross-validation and the SMOTE technique to correct class imbalance. Model performance was measured using the AUROC metric, and variable importance was analyzed with Shapley values to ensure model interpretability.ResultsThe XGBoost model achieved the best performance with an AUROC of 0.94. Catboost and Random Forest also showed excellent results, confirming the effectiveness of these models in predicting LBW.ConclusionMachine learning, combined with SMOTE, proved to be an effective approach for predicting LBW. XGBoost stood out as the most accurate model, but Catboost and Random Forest also provided solid results. These models can be applied to identify high-risk pregnancies, improving perinatal outcomes through early interventions.
Men’s Internet Sex Addiction Predicts Sexual Objectification of Women Even After Taking Pornography Consumption Frequency into Account
Authors: Nováková Pavla , Chvojka Edita , Ševčíková Anna , Blinka Lukas , Wright Paul , Kane Steven 
Published: October 2024

AbstractExcessive online video pornography consumption is associated with sexual objectification, particularly in male consumers. However, previous studies have not considered that there is a subgroup of internet users whose consumption may become excessive due to their internet sex addiction. Such users may, in response to internet sex addiction symptoms such as craving, have increased levels of sexual objectification. In a sample of 1,272 male consumers of online video pornography (Mage = 32.93, SDage = 9.44), we examined whether internet sex addiction is linked to sexual objectification. We fitted a series of structural equation models and found that men who scored higher on internet sex addiction were more likely to objectify women. More importantly, this link did not cease when controlling for the frequency of online video pornography consumption. Our findings suggest that there are other mechanisms related to addictive symptomatology, than just the link through online video pornography consumption, that may contribute to sexual objectification. Addiction-related factors may have a unique role in fostering sexual objectification. Isolating internet sex addiction as a potential driver highlights the need to address objectifying behaviors in individuals struggling with this addiction.
Urban Rural Disparities of Determinants of Antenatal Care in Senegal among Women of Reproductive Age: Evidence from 2023 Senegal Demographic Health Survey
Authors: Khan Majlish Mushfiqur Rahman , Tawhid Sultanul Arafean , Kibria Gulam 
Published: October 2024

AbstractBackgroundAntenatal care (ANC) is critical for ensuring positive pregnancy outcomes. Despite global efforts, ANC utilization remains suboptimal in many developing countries, including Senegal. This study aims to identify and distinguish between the key socio-demographic and economic factors influencing ANC utilization among urban and rural women in Senegal.MethodsThe analysis was conducted on 5107 women who had at least one birth in the last five years preceding the survey. Data were obtained from the 2023 Senegal Demographic and Health Survey (DHS). ANC utilization, defined as receiving four or more ANC visits during pregnancy, was the outcome variable. Independent variables included age, region, education, wealth index, total children ever born, pregnancy intention, employment status, media exposure, births in last five years and women autonomy. Bivariate analyses and multiple logistic regression were conducted to identify the significant variables.ResultsThe final sample included 4,785 women after excluding those with missing data. Higher education level (Adjusted Odds Ratio (AOR) = 3.649), rich wealth index (AOR=1.581), low (AOR=1.792) and high (AOR=1.692) media exposure were significantly associated with higher ANC in urban residences. On the contrary, in rural residences, primary (AOR=1.60), secondary (AOR=2.01) and higher (AOR=5.79) education level, intended pregnancy (AOR=1.33), low (AOR=1.48) and high (OR=1.35) media exposure and women autonomy (AOR=1.29) were significantly associated with higher ANC.ConclusionsThis study underscores the influence of different socio-economic and demographic factors on ANC utilization in urban and rural residences in Senegal. Policy interventions should prioritize enhancing education, economic opportunities, media awareness and women’s autonomy, particularly in rural areas, to improve ANC coverage.
Health worker acceptability of an HIV testing mobile health application within a rural Zambian HIV treatment programme
Authors: Montaner Andrés , Mumbalanga Mulundu , Umuhoza Marie-Chantal , Kinge Constance Wose , Okonji Emeka , Ligenda Godfrey , Mothibi Eula , Chirwa Ben , Pisa Pedro , Chasela Charles 
Published: October 2024

AbstractBackgroundAs more people living with HIV are identified and prescribed antiretroviral treatment in Zambia, detecting new HIV infections to complete the last mile of epidemic control is challenging. To address this, innovative targeted testing strategies are essential. Therefore, Right to Care Zambia developed and implemented a novel digital health surveillance application, Lynx, in three Zambian provinces—Northern, Luapula, and Muchinga in 2018. Lynx offers real-time HIV testing data with geo-spatial analysis for targeted testing, and has proven effective in enhancing HIV testing yield. This cross-sectional mixed methods study assessed the acceptability of Lynx among HIV testing healthcare workers in Zambia.MethodsA quantitative Likert scale (1–5) survey was administered to 176 healthcare workers to gauge Lynx’s acceptability. Additionally, six qualitative key person interviews and five focus group discussions were conducted to gain an in-depth understanding of acceptability, and identify relevant barriers and facilitators. Quantitative data were analysed by averaging survey responses and running descriptive statistics. Qualitative data were transcribed and analysed in thematic coding. Data triangulation was utilised between the data sources to verify findings.ResultsOverall, the average survey score of perceived ease of use was 3.926 (agree), perceived usefulness was 4.179 (strongly agree) and perceived compatibility was 3.574 (agree). Survey questions related to network requirements, resource availability, and IT support had the most “strongly disagree” responses. The qualitative data collection revealed that Lynx was perceived as useful, and easy to use. Training for staff and regular updates were identified as facilitators, while conflicting work priorities and inconsistent IT support were identified barriers.ConclusionLynx was identified as acceptable by health workers due to its perceived usefulness, staff trainings, and regular updates. For a mobile health intervention to be embraced in rural Zambian settings, key facilitators include robust IT support, comprehensive training, user feedback-based updates, and consideration of facility staff priorities.