bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fetal Growth Disorders Detection During First Trimester Gestation Through Comprehensive Maternal Circulating DNA Profiling
Authors: Cortese Rene , Cataldo Kylie , Hummel Justin , Smith Gracie , Ortega Madison , Richey Madison , Winn Hung , Gozal David , Goodman Jean Ricci 
Published: December 2024

STRUCTURED ABSTRACTBACKGROUNDEarly diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in circulating DNA (cirDNA) samples isolated from maternal blood, as early as the first gestational trimester. To study whether markers in maternal cirDNA may facilitate FGD early detection, we profiled plasma cirDNA from maternal samples prospectively collected during first gestational trimester.METHODSPlasma cirDNA was isolated from samples prospectively collected during first trimester gestation (n=56). Small, Large and Appropriate for Gestational Age (SGA n=11, LGA n=18, and AGA n=29, respectively) status was determined at birth according to weight and gestational age. cirDNA amount, fragmentation, mitochondrial/nuclear ratio and cirDNA methylation profiles were quantified using qPCR-based assays. Machine learning approaches were applied to build a molecular signature for prediction of LGA and SGA. Prediction accuracy was assessed by Receiving-Operating Curve (ROC) analysis, and Positive and Negative Predictive values (PPV and NPV, respectively) were calculated.RESULTSTotal concentration of plasma cirDNA, cirDNA fragmentation and ratio of mitochondrial/nuclear cirDNA were increased in SGA and LGA compared to AGA pregnancies. DNA methylation profiles also shown distinctive patterns. Out of the 10 selected loci, we detected 5 genes (HSD2,RASSF1,CYP19A1,IL10, andLEP) showing significant differential methylation differences (p<0.05) across the SGA, AGA and LGA samples at first trimester. We combined these molecular and epigenetic cirDNA markers in a signature that reliably discriminates between FGD and AGA pregnancies with high accuracy (AUC>0.95), achieving 88.8% PPV and 85.7% NPV.CONCLUSIONSOur findings show that maternal blood cirDNA profiles accurately detects early gestation FGD. The proposed novel marker panel hold great potential for implementation of low invasive approaches for reliable prediction of FGDs, enabling a disruptive path toward precision medicine in FGD.
mHMG-DTI: a drug-target interaction prediction framework combining modified Hierarchical Molecular Graphs and improved Convolutional Block Attention Module
Authors: Yang Zerui , Li Yinqiao , Matsuda Yudai , Song Linqi 
Published: December 2024

AbstractDrug-target interactions (DTIs) are fundamental to understanding the therapeutic mechanisms of drugs, yet accurately predicting these interactions remains a significant challenge in drug discovery. Current computational approaches often fail to capture essential molecular motifs and spatial information of proteins, limiting their effectiveness, particularly when encountering proteins or compounds absent in the training datasets. To address these limitations, we propose mHMG-DTI, a novel framework that leverages an improved Convolutional Block Attention Module (iCBAM) for enhanced protein feature extraction and modified Hierarchical Molecular Graphs (mHMGs) for comprehensive molecular encoding. This hierarchical approach not only captures detailed local structures and broader connectivity patterns but also incorporates guiding knowledge to improve feature representation. Across a total of 16 experimental evaluations on four benchmark datasets spanning both classification and regression tasks, mHMG-DTI surpasses existing baseline models in 11 cases. These results highlight the potential of mHMG-DTI to enhance DTI prediction accuracy, thereby accelerating the drug discovery process and providing valuable insights into drug resistance and side effect mechanisms.Contactzeruiyang2-c@my.cityu.edu.hk
Genetic ancestry and population structure in the All of Us Research Program cohort
Authors: Sharma Shivam , Nagar Shashwat Deepali , Pemu Priscilla , Zuchner Stephan ,   SEEC Consortium, Mariño-Ramírez Leonardo , Meller Robert , Jordan I. King 
Published: December 2024

AbstractThe NIH All of Us Research Program (All of Us) aims to build one of the world’s most diverse population biomedical datasets in support of equitable precision medicine. For this study, we analyzed participant genomic variant data to assess the extent of population structure and to characterize patterns of genetic ancestry for theAll of Uscohort (n=297,549). Unsupervised clustering of genomic principal component analysis (PCA) data revealed a non-uniform distribution of genetic diversity and substantial population structure in theAll of Uscohort, with dense clusters of closely related participants interspersed among less dense regions of genomic PC space. Supervised genetic ancestry inference was performed using genetic similarity betweenAll of Usparticipants and global reference population samples. Participants show diverse genetic ancestry, with major contributions from European (66.4%), African (19.5%), Asian (7.6%), and American (6.3%) continental ancestry components. Participant genetic similarity clusters show group-specific genetic ancestry patterns, with distinct patterns of continental and subcontinental ancestry among groups. We also explored how genetic ancestry changes over space and time in the United States (US). African and American ancestry are enriched in the southeast and southwest regions of the country, respectively, whereas European ancestry is more evenly distributed across the US. The diversity ofAll of Usparticipants’ genetic ancestry is negatively correlated with age; younger participants show higher levels of genetic admixture compared to older participants. Our results underscore the ancestral genetic diversity of theAll of Uscohort, a crucial prerequisite for genomic health equity.
Integrative multiomics analysis of metabolic dysregulation induced by occupational benzene exposure in mice
Authors: Scofield Sydney , Koshko Lisa , Stilgenbauer Lukas , Booms Alix , Berube Roxanne , Kassotis Christopher , Lin Chung-Ho , Jang Hyejeong , Kim Seongho , Stemmer Paul , Lempradl Adelheid , Sadagurski Marianna 
Published: December 2024

AbstractBackgroundType 2 Diabetes Mellitus (T2DM) is a significant public health burden. Emerging evidence links volatile organic compounds (VOCs), such as benzene to endocrine disruption and metabolic dysfunction. However, the effects of chronic environmentally relevant VOC exposures on metabolic health are still emerging.ObjectiveBuilding on our previous findings that benzene exposure at smoking levels (50 ppm) induces metabolic impairments in male mice, we investigated the effects of occupationally relevant, below OSHA approved, benzene exposure on metabolic health.MethodsAdult male C57BL/6 mice were exposed to 0.9ppm benzene 8 hours a day for 9 weeks. We assessed measures of metabolic homeostasis and conducted RNA and proteome sequencing on insulin-sensitive organs (liver, skeletal muscle, adipose tissue).ResultsThis low-dose exposure caused significant metabolic disruptions, including hyperglycemia, hyperinsulinemia, and insulin resistance. Transcriptomic analysis of liver, skeletal muscle, and adipose tissue identified key changes in metabolic and immune pathways especially in liver. Proteomic analysis of the liver revealed mitochondrial dysfunction as a shared feature, with disruptions in oxidative phosphorylation, mitophagy, and immune activation. Comparative analysis with high-dose (50 ppm) exposure showed both conserved and dose-specific transcriptomic changes in liver, particularly in metabolic and immune responses.ConclusionsOur study is the first to comprehensively assess the impacts of occupational benzene exposure on metabolic health, highlighting mitochondrial dysfunction as a central mechanism and the dose-dependent molecular pathways in insulin-sensitive organs driving benzene-induced metabolic imbalance. Our data indicate that current OSHA occupational exposure limits for benzene are insufficient, as they could result in adverse metabolic health in exposed workers, particularly men, following chronic exposure.
Distinct Nuclear and Cytoplasmic Transcriptomic Signatures Reveal Transcription Alone Is Insufficient to Determine Glucose-Induced Transcriptomic Dynamics
Authors: Bagheri Atefeh , Kim Jinsil , Jiang Peng 
Published: December 2024

AbstractGlucose profoundly influences cellular transcriptomes, but whether these changes are primarily driven by transcription remains unclear. Traditional bulk RNA sequencing, which interrogates total mRNA from whole cells, obscures distinct dynamics of nuclear and cytoplasmic transcriptomes. Nuclear RNA levels primarily reflect transcriptional activity, whereas cytoplasmic RNA levels are shaped by both transcription and post-transcriptional processes, such as RNA export, stability, and degradation. In this study, we systematically investigate glucose-induced transcriptomic responses in a subcellular location- and cell type-specific manner using three cell lines: FHC (normal colonic epithelial cells), MCF10A (normal breast epithelial cells), and MCF7 (metastatic breast cancer cells). Our findings reveal that, although nuclear and cytoplasmic mRNA levels show strong global correlations, glucose-induced changes in mRNA abundance exhibit minimal concordance between the nucleus and cytoplasm. Additionally, glucose-induced changes in exon inclusion levels often diverge between the nucleus and cytoplasm, underscoring the importance of post-transcriptional processes in shaping the cytoplasmic transcriptome response to glucose level changes. Glucose-induced differentially expressed genes (DEGs) and differentially spliced exons (ΔPSI) are enriched in distinct pathways exhibiting unique enrichment patterns depending on the subcellular location and cell line. These findings underscore the complexity of glucose-induced transcriptomic regulation, demonstrating that transcription alone is insufficient to explain the observed dynamics.
Impact of dopaminergic modulation on the state transitions of striatal medium spiny neuron sub-types – a computational study
Authors: Anisetty Nitin , Manchanda Rohit 
Published: December 2024

AbstractMedium spiny neurons (MSN) of the striatum are known for their bistable membrane potential leading to two states – a hyperpolarized down-state and relatively depolarized up-state. Glutamatergic inputs from the hippocampus play a key role in switching the cell to up-state. This gating is known to play a key role in regulating when other synaptic inputs, such as from the cortex, should generate action potentials and when they should be considered as noise. Therefore, it is crucial for the transition between states to occur normally as abnormal gating has implications in conditions such as Schizophrenia. Although dopamine is reported to modulate ion channels of MSN sub-types – dMSN and iMSN, it’s influence on the state transition times and up-state dwell times are being reported here. This was done using biophysically constrained spiny models of dMSN and iMSN with explicit dopamine receptors. Additionally, strong correlation between state transition times and spiking frequencies of MSN sub-types was observed.
FGF21 Analogue PF-05231023 on Alcohol Consumption and Neuronal Activity in the Nucleus Accumbens
Authors: Cooley Bart J. , Hanbury-Brown Cassandra V. Occelli , Choi Eun A. , Heller Willow A. , Lim Alyssa W. , Lawrence Andrew J. , Haber Paul S. , McNally Gavan P. , Millan E. Zayra 
Published: December 2024

AbstractFibroblast growth factor 21 (FGF21) is a liver-derived hormone known to suppress alcohol consumption in mice and non-human primates. However, the role of FGF21 in modulating environmental and behavioural factors driving alcohol consumption—such as cue-driven responses and effortful actions to obtain alcohol—and its effects on neural activity related to consumption, remain unclear. Here, we evaluated the impact of PF-05231023, a long-acting FGF21 analogue, across multiple dimensions of alcohol consumption and motivation. PF-05231023 reduced alcohol intake and preference in a dose-and sex-specific manner; diminished approach behaviours following an alcohol but not sucrose cue; and decreased lever-pressing under a progressive-ratio schedule, both alone and when combined with the GLP-1 agonist Exendin-4. Additionally, PF-05231023 altered the microstructure of alcohol consumption by shortening drinking bouts and increased the recruitment of nucleus accumbens (Acb) neurons associated with bout termination. These findings demonstrate that PF-05231023 broadly suppresses alcohol-motivated behaviours and that targeting FGF21 signaling in combination with GLP-1 agonists may enhance therapeutic efficacy. Mechanistically, the observed reductions in alcohol consumption following PF-05231023 appear to involve diminished alcohol palatability and modulation of neuronal activity from distinct subsets of Acb neurons.
Non-Canonical Cytochrome P450 Enzymes in Nature
Authors: Nguy Andy K. L. , Ireland Kendra A. , Kayrouz Chase M. , Cáceres Juan Carlos , Greene Brandon L. , Davis Katherine M. , Seyedsayamdost Mohammad R. 
Published: December 2024

AbstractCytochrome P450s (CYPs) are a superfamily of thiolate-ligated heme metalloenzymes principally responsible for the hydroxylation of unactivated C–H bonds. The lower-axial cysteine is an obligatory and universally conserved residue for the CYP enzyme class. Herein, we challenge this paradigm by systematically identifying non-canonical CYPs (ncCYPs) that do not harbor a cysteine ligand. Our bioinformatic search reveals 20 distinct ncCYP families with diverse ligands encoded in microbial genomes. We characterize a native serine-ligated CYP with a high-spin ferric resting state. Its crystal structure clearly shows a typical CYP fold and a serine alkoxide as a lower axial heme ligand. In addition, we report the discovery and characterization of the first native selenocysteine-ligated CYP in nature. Our findings radically expand the CYP metalloenzyme family.
Geography is a stronger predictor of diversification of monogenean parasites (Platyhelminthes) than host relatedness in characid fishes of Middle America
Authors: Alda Fernando , Mendoza-Franco Edgar F. , Hanson-Regan William , Reina Ruth G. , Bermingham Eldredge , Torchin Mark 
Published: December 2024

AbstractHost-parasite associations have historically been considered compelling examples of coevolution and useful in examining cospeciation. However, modern molecular methods have revealed more complex dynamics than previously assumed, with host-switching events appearing commonly across taxa and challenging traditional views of strict coevolution in host-parasite relationships. Monogenean parasites are considered highly host-specific and have long served as models for probing evolution of host-parasite associations, particularly in differentiating geographic and phylogenetic patterns of parasite diversification. We investigated the phylogeographic patterns of monogenean ectoparasites associated with four species of characid fishes across Panama, Nicaragua, and Mexico. Our hypothesis posited that parasite diversity and community structure would be more strongly correlated with host species (suggesting cospeciation) than with geographic location (indicative of allopatric speciation). We found high genetic differentiation among parasites and their hosts across different locations. However, while geography explained the genetic structure of both host fishes and parasites, the observed patterns were neither congruent nor parallel. Parasite community structure and genetic similarity were consistently better explained by geographic location than by host species, although both factors played a significant role. Contrary to our predictions, we found no evidence of cospeciation. Instead, the diversification of these monogenean parasites appears to be primarily driven by their ability to switch hosts. At this taxonomical scale, host-switching is mediated by the geographical proximity of potential hosts, underscoring the importance of spatial factors in parasite evolution.
Collecting, detecting and handling non-wear intervals in longitudinal light exposure data
Authors: Guidolin Carolina , Zauner Johannes , Hartmeyer Steffen Lutz , Spitschan Manuel 
Published: December 2024

AbstractIn field studies using wearable light loggers, participants often need to remove the devices, resulting in non-wear intervals of varying and unknown duration. Accurate detection of these intervals is an essential step during data pre-processing. Here, we deployed a multi-modal approach to collect non-wear time during a longitudinal light exposure campaign and systematically compare non-wear detection strategies. Healthy participants (n=26; mean age 28±5 years, 14F) wore a near-corneal plane light logger for one week and reported non-wear events in three ways: pressing an “event marker” button on the light logger, placing it in a black bag, and using an app-based Wear log. Wear log entries, checked twice daily, served as ground truth for non-wear detection, showing that non-wear time constituted 5.4±3.8% (mean±SD) of total participation time. Button presses at the start and end of non-wear intervals were identified in >85.4% of cases when considering time windows beyond one minute for detection. To detect non-wear intervals based on black bag use and lack of motion, we employed an algorithm detecting clusters of low illuminance and clusters of low activity. Performance was higher for illuminance (F1=0.76) than activity (F1=0.52). Light exposure metrics derived from the full dataset, a dataset filtered for non-wear based on self-reports, and a dataset filtered for non-wear using the low illuminance clusters detection algorithm showed minimal differences. Our results highlight that while non-wear detection may be less critical in high-compliance cohorts, systematically collecting and detecting non-wear intervals is feasible and important for ensuring robust data pre-processing.
Reactivation of reward representations associated with the reinforcement of behavior
Authors: Tong Ai Phuong S. , Sreekumar Vishnu , Inati Sara K. , Zaghloul Kareem A. , Woolrich Mark W. 
Published: December 2024

AbstractReward-related representations are found distributed throughout many human subcortical and neocortical regions that support different neural processes. These representations get reinstated for different but related tasks. There is rising evidence that representations reactivate during learning to reinforce prior rewarding choices. However, there remains a critical lack of understanding for whether and how reactivations in humans can facilitate behavioral reinforcement. To investigate this, we recorded from the temporal lobe and prefrontal cortex with intracranial electrocorticography while human subjects learnt two-choice decisions across two different scenes. Crucially, subjects were able to straightforwardly reactivate knowledge gained in a prior scene to make decisions in the current scene, but only when the reward contingencies stayed the same between the two scenes. Using a Bayesian learner, we inferred reward expectation from choice behavior, and measured representations of these reward expectations in electrocorticography data. We found that the representations of reward expectation in the medial temporal lobe and orbitofrontal cortex were reactivated, only when the subjects could straightforwardly transfer knowledge between the two scenes. In the anterior temporal lobe, the reactivation of reward representations increased as learning increased, suggesting an area of plasticity during learning. This work presents a novel framework for measuring the reinforcement, or reactivation, of representations from human electrophysiology during the reinforcement of behavior.Significance statementThis study provides a framework for how the brain processes and reinstates reward-related information during the reinforcement of behavior. Using intracranial electrocorticography, the researchers found that reward information is encoded in distant brain regions, the medial temporal lobe and orbitofrontal cortex. These representations are dynamically reinstated across scenes when reward contingencies align and when knowledge can be transferred. Additionally, reward-related representations in the anterior temporal lobe become increasingly reinforced or reinstated as learning progresses, revealing an area of plasticity during learning. These findings offer new insights into the neural mechanisms underlying decision-making and highlight potential avenues for addressing conditions like addiction or depression, where reward processing and decision-making are disrupted.
Computationally reconstructing the evolution of cancer progression risk
Authors: Cao Kefan , Schwartz Russell 
Published: December 2024

AbstractUnderstanding the evolution of cancer in its early stages is critical to identifying key drivers of cancer progression and developing better early diagnostics or prophylactic treatments. Early cancer is difficult to observe, though, since it is generally asymptomatic until extensive genetic damage has accumulated. In this study, we develop a computational approach to infer how once-healthy cells enter into and become committed to a pathway of aggressive cancer. We accomplish this through a strategy of using tumor phylogenetics to look backwards in time to earlier stages of tumor development combined with machine learning to infer how progression risk changes over those stages. We apply this paradigm to point mutation data from a set of cohorts from the Cancer Genome Atlas (TCGA) to formulate models of how progression risk evolves from the earliest stages of tumor growth, as well as how this evolution varies within and between cohorts. The results suggest general mechanisms by which risk develops as a cell population commits to aggressive cancer, but with significant variability between cohorts and individuals. These results imply limits to the potential for earlier diagnosis and intervention while also providing grounds for hope in extending these beyond current practice.AvailabilityThe code used to conduct the analysis is available at:https://github.com/kefanc2/CancerRisk
Gait Adaptations Under Functional Asymmetry: Exploring the Role of Step Width, Step Length, and CoM in Lateral Stability
Authors: Baček Tomislav , Oetomo Denny , Tan Ying 
Published: December 2024

AbstractBipedal gait is inherently unstable, requiring a complex interplay between foot placement and centre of mass (CoM) movement to maintain balance. While various factors are known to impact walking balance, few studies have explored the specific effects of functional asymmetry on lateral stability. This study investigates how step length, step width, and CoM adaptations impact lateral gait stability in healthy young adults walking with and without a functional asymmetry induced by fully extending the left knee. The results show that step length remains unaffected by functional asymmetry regardless of speed, while step width increases under the constraint. This adjustment increases the base of support; however, the concurrent increase in lateral CoM movement reduces overall lateral stability. These findings offer valuable insights into fundamental gait adaptation and stability mechanisms, with potential implications for designing rehabilitation strategies for individuals with gait asymmetry.