bluuebunny/crossref_metadata_2025_split huggingface dataset:
To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry
Authors: Abdul-Khalek Naim , Picciani Mario , Wimmer Reinhard , Overgaard Michael Toft , Wilhelm Mathias , Echers Simon Gregersen 
Published: October 2024

AbstractIdentifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the model’s applicability across various research fields. The model’s architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub athttps://github.com/wilhelm-lab/dlomix.
Diffusion-based artificial genomes and their usefulness for local ancestry inference
Authors: Szatkownik Antoine , Planche Léo , Demeulle Maïwen , Chambe Titouan , Ávila-Arcos María C. , Huerta-Sanchez Emilia , Furtlehner Cyril , Charpiat Guillaume , Jay Flora , Yelmen Burak 
Published: October 2024

AbstractThe creation of synthetic data through generative modeling has emerged as a significant area of research in genomics, offering versatile applications from tailoring functional sequences with specific attributes to generating high-quality, privacy-preserving in silico genomes. Notwithstanding these advancements, a key challenge remains: while some methods exist to evaluate artificially generated genomic data, comprehensive tools to assess its usefulness are still limited. To tackle this issue and present a promising use case, we test artificial genomes within the framework of population genetics and local ancestry inference (LAI).Building on previous work in deep generative modeling for genomics, we introduce a novel, frugal diffusion model and show that it produces high-quality genomic data. We then assess the performance of a downstream machine learning LAI model trained on composite datasets comprising both real and/or synthetic data. Our findings reveal that the LAI model achieves comparable performance when trained exclusively on real data versus high-quality synthetic data. Moreover, we highlight how data augmentation using high-quality artificial genomes significantly benefits the LAI model, particularly when real data is limited. Finally, we compare the conventional use of a single synthetic dataset to a robust ensemble approach, wherein multiple LAI models are trained on diverse synthetic datasets, and their predictions are aggregated.Our study highlights the potential of frugal diffusion-based generative models and synthetic data integration in genomics. This approach could improve fair representation across populations by overcoming data accessibility challenges, while ensuring the reliability of genomic analyses conducted on artificial data.
NeuroTD: A Time-Frequency Based Multimodal Learning Approach to Analyze Time Delays in Neural Activities
Authors: Huang Xiang , Kalafut Noah Cohen , Alatkar Sayali , Li Athan Z. , Dong Qiping , Chang Qiang , Wang Daifeng 
Published: October 2024

AbstractStudying temporal features of neural activities is crucial for understanding the functions of neurons as well as underlying neural circuits. To this end, recent researches employ emerging techniques including calcium imaging, Neuropixels, depth electrodes, and Patch-seq to generate multimodal time-series data that depict the activities of single neurons, groups of neurons, and behaviors. However, challenges persist, including the analysis of noisy, high-sampling-rate neuronal data, and the modeling of temporal dynamics across various modalities. To address these challenges, we developed NeuroTD, a novel deep learning approach to align multimodal time-series datasets and infer cross-modality temporal relationships such as time delays or shifts. Particularly, NeuroTD integrates Siamese neural networks with frequency domain transformations and complex value optimization for inference. We applied NeuroTD to three multimodal datasets to (1) analyze electrophysiological (ephys) time series measured by depth electrodes, identifying time delays among neurons across various positions, (2) investigate neural activity and behavioral time series data derived from Neuropixels and 3D motion captures, establishing causal relationships between neural activities and corresponding behavioral activities, and (3) explore gene expression and ephys data of single neurons from Patch-seq, identifying gene expression signatures highly correlated with time shifts in ephys responses. Finally, NeuroTD is open-source athttps://github.com/daifengwanglab/NeuroTDfor general use.
Spatial Transcriptomics Reveals the Temporal Architecture of the Seminiferous Epithelial Cycle and Precise Sertoli-Germ Synchronization
Authors: Chakravorty Arun , Simons Benjamin D. , Yoshida Shosei , Cai Long 
Published: October 2024

SummarySpermatogenesis is characterized by the seminiferous epithelial cycle, a periodic pattern of germ cell differentiation with a wave-like progression along the length of seminiferous tubules. While key signaling and metabolic components of the cycle are known, the transcriptional changes across the cycle and the correlations between germ cell and somatic lineages remain undefined. Here, we use spatial transcriptomics via RNA SeqFISH+ to profile 2,638 genes in 216,090 cells in mouse testis and identify a periodic transcriptional pattern across tubules that precisely recapitulates the seminiferous epithelial cycle, enabling us to map cells to specific timepoints along the developmental cycle. Analyzing gene expression in somatic cells reveals that Sertoli cells exhibit a cyclic transcriptional profile closely synchronized with germ cell development while other somatic cells do not demonstrate such synchronization. Remarkably, in mouse testis with drug-induced ablation of germ cells, Sertoli cells independently maintain their cyclic transcriptional dynamics. By analyzing expression data, we identify an innate retinoic acid cycle, a network of transcription factors with cyclic activation, and signaling from germ cells that could interact with this network. Together, this work leverages spatial geometries for mapping the temporal dynamics and reveals a regulatory mechanism in spermatogenesis where Sertoli cells oscillate and coordinate with the cyclical progression of germ cell development.Graphical Abstract
Prevalence of female athlete triad (FAT) in rhythmic gymnastics, an aesthetic sport, and its association with risks of body-image distortion and eating disorders
Authors: Yoshitani-Kuwabara Kayo , Yumen Yukina , Takayama Yumi , Kitayama Natsuho , Hanzawa Fumiaki , Sakane Naoki , Nagai Narumi 
Published: October 2024

AbstractBackgroundFemale athlete triad (FAT), a syndrome linking low energy availability (LEA), menstrual dysfunction (MD), and impaired bone health, has serious potential consequences for sports performance and health. In this cross-sectional study, we investigated prevalences of each FAT component in female athletes of aesthetic and non-aesthetic sports and in non-athlete females, and its association with body image (BI) distortion and eating behaviors.MethodsGoogle Forms was used to examine self-reported height and weight, menstruation conditions, history of stress fractures (HSF), BI, the Eating Attitude Test (EAT)-26 (indicator of eating disorder) scores, and eating behaviors; these attributes were compared between 3 groups (athletes of aesthetic sports [rhythmic gymnasts,n=40] and non-esthetic sports [volleyball players,n=46]; other females of the same age group [non-athletes,n=108]). To identify risk factors for each FAT component, multivariate logistic-regression analyses were used. LEA, MD, and HSF were dependent variables; rhythmic gymnasts, distorted BI, and EAT-26 scores were independent variables.ResultsRhythmic gymnasts had a lower body mass index (p<0.001), higher LEA and MD prevalence (bothp<0.001), and greater percentage of participants with ≥2 FAT criteria (p<0.05) compared with other groups; exhibited higher distorted BI scores (p<0.05) than volleyball players; and tended to overestimate their body shape. Regarding eating behaviors, rhythmic gymnasts had higher EAT-26 scores (p<0.001) than other groups and significantly tended to skip breakfast (p<0.05) than non-athletes. Multivariate logistic-regression analysis revealed that rhythmic gymnasts and body-shape overestimation were independently associated with LEA; rhythmic gymnasts and high EAT-26 scores were independently associated with MD.ConclusionsFAT prevalence was approximately 1.5 times higher among rhythmic gymnasts than among volleyball players and non-athletes. These findings suggest that rhythmic gymnasts are at a higher risk of FAT and being an athlete in aesthetic sports, body-shape overestimation, and higher EAT-26 scores are risk factors for FAT.
Trends in Substance Use-related Emergency Department Visits by Youth, 2018-2023
Authors: Renny Madeline H. , Stecher Yago , Vargas-Torres Carmen , Zebrowski Alexis M. , Merchant Roland C. 
Published: October 2024

AbstractBackgroundEmergency departments (EDs) are a promising location for initiating substance use interventions for youth. Our objective was to determine trends in substance use-related ED visits for youth from 2018-2023, and investigate the patient characteristics, types of substance involved, and ED visit disposition and revisits.MethodsWe conducted a retrospective review of electronic health records (EHRs) from six EDs in an urban healthcare system to identify 12-21-year-old patients with a substance use-related ED visit from 2018 through 2023. Visits were identified by International Classification of Diseases, 10thRevision Clinical Modification codes for substance use involving alcohol, cannabis, sedative/hypnotics, opioids, cocaine/stimulants, and psychoactive substances. The proportion of substance use-related visits each year was calculated by age group (12-14y, 15-17y, and 18-21y), sex, race/ethnicity, and substance type. We used 2-sample tests of binomial proportion to compare proportions. Logistic regression was used to assess characteristics associated with substance use-related visits, hospital admissions, and ED revisits.ResultsOf 151,764 ED visits for 12-21-year-olds, 4,556 (3.0%) were for substance use. From 2018-2023, substance use-related ED visits increased from 2.8% to 3.4% of all ED visits (p < 0.001) and were most often by 18-21-year-olds (79.4%), yet there were significant increases in visits by younger age groups (12-14y and 15-17y). Visits for females increased from 43.4% in 2018 to 52.4% in 2023 (p< 0.001). Although visits for alcohol were most frequent (53.1%), cannabis visits increased from 17.9% to 35.3%, with increases across all age groups (p < 0.001). Nineteen percent of visits involved patients that had an ED revisit for a substance use-related diagnosis within one year.ConclusionSubstance use-related ED visits increased from 2018 to 2023, with an increase in visits for cannabis over time. These findings can inform targeted ED-based interventions for substance use in youth.
Factors associated with tobacco smoking among beverage industrial workers and their spouses in Rwanda
Authors: Nsanzabera Charles , Rukundo Jean claude , Said Mustafe Yusuf , Ndayisenga Leonard 
Published: October 2024

AbstractBackgroundGlobally, smoking leads to over 7 million preventable deaths annually, with higher rates in men (16%) than women (7%). While smoking has declined in high-income countries, tobacco use in Rwanda is comparatively low, affecting 14% of men and 3% of women.ObjectiveThe study aimed to identify the factors associated with tobacco smoking among workers and their spouses in Rwanda.MethodThis research employed a cross-sectional study design conducted in a beverage manufacturing industry in Rwanda. The target population was 822 participants, including industry employees and their spouses, aged 30 to 75 years. Using the Cochrane formula, the initial sample size was determined to be 384, and after the non-response rate adjustment and correction, the final sample size was set at 440. The data collection was carried out from May to December 2018. A combination of stratified and simple random sampling was used to ensure the sample’s representativeness.ResultsThe analysis reveals 6.8% were smokers and several key predictors of smoking behavior. Participants with elevated cardiovascular disease (CVD) risk (≥10%) have significantly higher odds of smoking, with an adjusted odds ratio of 2.946 (95% CI: 1.102-7.875, p=0.03), suggesting that CVD risk is a crucial factor in smoking behavior due to overlapping lifestyle risks. Additionally, high serum uric acid (SUA) levels (≥7 mg/dl) are strongly associated with smoking, with an adjusted odds ratio of 4.278 (95% CI: 1.141-11.872, p=0.005), indicating that elevated SUA levels are over four times more likely to be linked to smoking. Age is another significant predictor, with participants aged 50 years or older being nearly three times more likely to smoke compared to younger individuals, as shown by an adjusted odds ratio of 2.766 (95% CI: 1.126-6.797, p=0.02). Participants with hypertension or those treated for hypertension have lower adjusted odds ratio of 0.380 (95% CI: 0.100-1.446, p=0.049).ConclusionThe study found that tobacco smoking is relatively rare in this population, with elevated cardiovascular disease risk. High serum uric acid levels, and older age identified as significant predictors of smoking.Author summaryWhat is already known on this topicTobacco smoking is a major global cause of preventable deaths, with higher smoking rates in men, and although smoking has declined in high-income countries, tobacco use remains relatively low in Rwanda.What this study addsThe study identifies key factors associated with smoking among industrial workers and their spouses in Rwanda, including elevated cardiovascular disease risk, high serum uric acid levels, and older age.How this study might affect research, practice, or policyThis study highlights important predictors of smoking in a Rwandan context, which could inform targeted public health strategies, smoking cessation programs, and policy decisions aimed at reducing tobacco use.
Phage CRISPR-like regulatory RNAs silence bacterial adaptive and innate immunity
Authors: Liu Chao , Wang Rui , Li Zhihua , Wang Wenhe , Yao Jiachen , Cheng Feiyue , Du Xiaoyu , Guan Linlin , Mestre Mario Rodríguez , Xu Jing , Xue Qiong , Du Jun , Zhou Tao , Shu Xian , Zhao Huiwei , Feng Yue , Pinilla-Redondo Rafael , Li Ming 
Published: October 2024

AbstractIn prokaryotes, arrayed CRISPR RNAs (crRNAs) guide Cas proteins to destroy phage DNA/RNA, while solitary crRNA-like RNAs (crlRNAs) program Cas proteins for auto-regulation or to control abortive infection (Abi)-inducing toxins that activate when CRISPR-Cas fails. Here, we report that phages exploit crlRNA mimics to hijack these multi-layered host defenses.Pseudomonas aeruginosaphages use crlRNAs to thwart CRISPR-Cas immunity by inhibiting Cas expression, or to block Abi by silencing an unprecedented RNA toxin that features consecutive proline codons. Remarkably, the anti-CRISPR protein AcrIF24 selectively inhibits Cas proteins loaded with host crRNAs/crlRNAs, while allowing those complexed with viral crlRNAs to synergistically block Abi responses, as viral crlRNAs have co-adaptively evolved shorter spacer sequences. Furthermore, viral crlRNAs frequently organize as multiplexed arrays, mirroring the architecture of bacterial CRISPRs. Our findings showcase how phage RNAs hijack Cas proteins to silence multi-layered bacterial defenses, and highlight the delicate synergy between RNA and protein-based anti-CRISPR strategies.
The RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain
Authors: Higginson Lauryn A. , Wang Xingjun , He Kevin , Torstrick Maggie , Kim Minhoo , Benayoun Bérénice A. , MacLean Adam , Chanfreau Guillaume F. , Morton Derrick J. 
Published: October 2024

AbstractIntracellular ribonucleases (RNases) are essential in all aspects of RNA metabolism, including maintaining accurate RNA levels. Inherited mutations in genes encoding ubiquitous RNases are associated with human diseases, primarily affecting the nervous system. Recessive mutations in genes encoding an evolutionarily conserved RNase complex, the RNA exosome, lead to syndromic neurodevelopmental disorders characterized by progressive neurodegeneration, such as Pontocerebellar Hypoplasia Type 1b (PCH1b). We establish a CRISPR/Cas9-engineeredDrosophilamodel of PCH1b to study cell-type-specific post-transcriptional regulatory functions of the nuclear RNA exosome complex within fly head tissue. Here, we report that pathogenic RNA exosome mutations alter activity of the complex, causing widespread dysregulation of brain-enriched cellular transcriptomes, including rRNA processing defects—resulting in tissue-specific, progressive neurodegenerative effects in flies. These findings provide a comprehensive understanding of RNA exosome function within a developed animal brain and underscore the critical role of post-transcriptional regulatory machinery in maintaining cellular RNA homeostasis within the brain.
A Novel Human TBCK- Neuronal Cell Model Results in Severe Neurodegeneration and Partial Rescue with Mitochondrial Fission Inhibition
Authors: Angireddy Rajesh , Karisetty Bhanu Chandra , Katsura Kaitlin A , Díaz Abdias , Murali Svathi , Smith Sarina , Ohl Laura , Clark Kelly , Kossenkov Andrew V. , Bhoj Elizabeth J.K. 
Published: October 2024

AbstractBackground and ObjectivesTBCK syndrome is a rare fatal pediatric neurodegenerative disease caused by biallelic loss-of-function mutations in theTBCKgene. Previous studies by our lab and others have implicated mTOR, autophagy, lysosomes, and intracellular mRNA transport, however the exact primary pathologic mechanism is unknown. This gap has prevented the development of targeted therapies.MethodsWe employed a human neural progenitor cell line (NPC), ReNcell VM, which can differentiate into neurons and astrocytes, to understand the role of TBCK in mTORC1 activity and neuronal autophagy and cellular mechanisms of pathology. We used shRNA technology to knockdown TBCK in ReNcells.ResultsThese data showed that loss of TBCK did not inhibit mTORC1 activity in neither NPC nor neurons. Additionally, analysis of eight patient-derived cells and TBCK knock down HeLa cells showed that mTORC1 inhibition is inconsistent across different patients and cell types. We showed that TBCK knockdown in ReNcells affected NPC differentiation to neurons and astrocytes. Specifically, differentiation defects are coupled to cell cycle defects in NPC and increased cell death during differentiation. RNAseq analysis indicated the downregulation of several different neurodevelopmental and differentiation pathways. We observed a higher number of LC3-positive vesicles in the soma and neurites of TBCK knockdown cells. Further, TBCK knockdown altered mitochondrial dynamics and membrane potential in NPC, neurons and astrocytes. We found partial mitochondrial rescue with the mitochondrial fission inhibitor mdivi- 1.DiscussionThis work outlines a new Human Cell Model for TBCK-related neurodegeneration and the essential role of mitochondrial health and partial rescue with mitochondrial fission inhibitor. This data, along with human neurons and astrocytes, illuminate mechanisms of neurodegeneration and provide a possible novel therapeutic avenue for affected patients.
Tit wit: environmental and genetic drivers of cognitive variation along an urbanization gradient
Authors: Thompson MJ , Gervais L , Bharath D , Caro SP , Chaine AS , Perrier C , Réale D , Charmantier Anne 
Published: October 2024

AbstractCognitive abilities can promote acclimation and adaptation to life in cities. However, the genetic versus environmental drivers of cognition have rarely been studied in the wild and there exists a major gap concerning the role of cognition in adaptation to novel urban contexts. To address this, we evaluate cognitive variation in wild great tits (Parus major;N= 393) along an urban gradient, and decipher the genetic basis of this variation using a combination of a common garden experiment, quantitative genetic analyses, and genome-wide association studies. Specifically, we measure inhibitory control abilities which affect how animals respond to novel resources and challenges. We find that wild urban and forest tits do not clearly differ in inhibitory control performance (number of errors or the latency to escape) during a motor detour task; a result that was consistent in birds from urban and forest origins reared in a common garden (N= 73).Cognitive performance was repeatable (R= 0.35 – 0.38) and showed low to moderate heritability in the wild (h2= 0.16 - 0.28 using social and genomic pedigrees). We identified five SNPs that were significantly associated with the number of errors during the task, explaining 21% of the cognitive variation. These SNPs are linked to genes related to serotonergic and dopaminergic systems that are known to play important roles in cognition. Altogether, our study finds limited evidence that inhibitory control abilities have evolved under novel urban contexts, yet reveals a genetic basis of this cognitive trait in great tits.
Conserved transcription factors coordinate synaptic gene expression through repression
Authors: Kentro James A. , Singh Gunjan , Pham Tuan M. , Currie Justin , Khullar Saniya , Medeiros Audrey T. , Tsiarli Maria , Larschan Erica , O’Connor-Giles Kate M. 
Published: October 2024

AbstractChemical synapses are the primary sites of communication in the nervous system. Synapse formation is a complex process involving hundreds of proteins that must be expressed in two cells at the same time. We find that synaptic genes are broadly and specifically coordinated at the level of transcription across developing nervous systems. How this spatiotemporal coordination is achieved remains an open question. Through genomic and functional studies inDrosophila, we demonstrate corresponding coordination of chromatin accessibility and identify chromatin regulators DEAF1 and CLAMP as broad repressors of synaptic gene expression outside windows of peak synaptogenesis. Disruption of either factor temporally dysregulates synaptic gene expression across neuronal subtypes, leading to excess synapse formation. We further find that DEAF1, which is linked to syndromic intellectual disability, is both necessary and sufficient to constrain synapse formation. Our findings reveal the critical importance of broad temporally coordinated repression of synaptic gene expression in regulating neuronal connectivity and identify two key repressors.