bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antagonizingcis-regulatory elements of a conserved flowering gene mediate developmental robustness
Authors: Lanctot Amy , Hendelman Anat , Udilovich Peter , Robitaille Gina M. , Lippman Zachary B. 
Published: November 2024

AbstractDevelopmental transitions require precise temporal and spatial control of gene expression. In plants, such regulation is critical for flower formation, which involves the progressive differentiation of stem cell populations within floral meristems followed by rapid sequential development of floral organs. Across plant taxa, these transitions are orchestrated by the F-box transcriptional co-factor geneUNUSUAL FLORAL ORGANS(UFO). The conserved and pleiotropic functions ofUFOoffer a useful framework for investigating how evolutionary processes have shaped the intricatecis-regulation of key developmental genes. By pinpointing a conserved promoter sequence in an accessible chromatin region of the tomato ortholog ofUFO, we engineeredin vivoa series ofcis-regulatory alleles that caused both loss- and gain-of-function floral defects. These mutant phenotypes were linked to disruptions in predicted transcription factor binding sites for known transcriptional activators and repressors. Allelic combinations revealed dosage-dependent interactions between opposing alleles, influencing the penetrance and expressivity of gain-of-function phenotypes. These phenotypic differences support that robustness in tomato flower development requires precise temporal control ofUFOexpression dosage. Bridging our analysis toArabidopsis, we found that although homologous sequences to the tomato regulatory region are dispersed within theUFOpromoter, they maintain similar control over floral development. However, phenotypes from disrupting these sequences differ due to the differing expression patterns ofUFO. Our study underscores the complexcis-regulatory control of dynamic developmental genes and demonstrates that critical short stretches of regulatory sequences that recruit both activating and repressing machinery are conserved to maintain developmental robustness.
Self-organization of spermatogenic wave coordinates sustained sperm production in the mouse testis
Authors: Sato Toshiyuki , Li Yuting I. , Jörg David J. , Komeya Mitsuru , Yamanaka Hiroyuki , Nakamura Hiroko , Hirano Kodai , Kondo Yohei , Aoki Kazuhiro , Takahashi Masahide , Nakata Hiroki , Kimura Hiroshi , Ogawa Takehiko , Simons Benjamin D. , Yoshida Shosei 
Published: November 2024

SUMMARYSpermatogenesis takes place in the testis, relying on the ordered turnover of differentiating cells supplied from stem cells. Classic histological analyses have revealed that this process shows hierarchical spatiotemporal patterning known as the spermatogenic cycle, wave, and descent of segmental order, indicative of currently underexplored mechanisms of tissue- and organ-scale homeostasis. Here, using mice, we conducted high-resolution, wide-field, and ‘ultra’ long-term live imaging studiesin vivoandex vivo, combined with whole-organ mapping of differentiation stages. Such trans-scale measures demonstrate how stereotypic local cell turnover is coordinated into characteristic phase waves propagating along the seminiferous tubules, further organized into organ-scale patterning over the tubule loops. Minimal mathematical modeling shows that such higher-order dynamics can emerge from the local coupling of autonomous oscillators, which are rooted in delayed feedback interplay between stem and differentiating cells via retinoic acid signaling. These findings highlight a self-organization mechanism underpinning organ-scale homeostasis and constant sperm production.
Quantifying the population-level impact of expanded antibiotic treatment for cholera outbreak management
Authors: Ahmed Sharia M. , LaPrete Cormac R. , Ciglenecki Iza , Azman Andrew S. , Leung Daniel T. , Keegan Lindsay T. 
Published: November 2024

AbstractBackgroundSince 2021, there has been a resurgence in the number of cholera cases, countries affected, and the case fatality risk. Partly due to concerns about antibiotic resistance, current cholera treatment guidelines reserve antibiotics for severely symptomatic cases, recommending only supportive care (e.g., oral rehydration) for non-severe cases. However, it has been suggested that the reduction in transmissibility from antibiotic treatment may result in circumstances under which treating mild or moderate cases with antibiotics may have population-level benefits. We developed a compartmental model of cholera transmission in a non-endemic setting to quantify the potential impact of expanded antibiotic treatment on disease burden and antibiotic use. Through simulations, we evaluated different outbreak scenarios, by varying the reproductive number, care-seeking behavior, and proportion of non-severe cases receiving antibiotics. We found that expanding antibiotic treatment could significantly reduce the final outbreak size under certain outbreak characteristics. Under these different transmission scenarios, treating non-severe symptomatic infections with antibiotics decreased cholera transmission and, in some cases, the total number of antibiotic doses used. In high transmission settings, the benefits of expanded treatment are less pronounced and the strategy may lead to increased antibiotic use, potentially increasing the risk of antibiotic resistance. We show that the effectiveness of expanded antibiotic treatment is highly dependent on achieving high care-seeking rates among non-severely symptomatic infections and tailoring the approach to specific outbreak conditions. While expanding antibiotic eligibility could enhance outbreak control in some settings, careful consideration of antibiotic resistance risks is necessary in high-transmission contexts.
Hospital-wide, dynamic, individualized prediction of central line-associated bloodstream infections - development and temporal evaluation of six prediction models
Authors: Albu Elena , Gao Shan , Stijnen Pieter , Rademakers Frank E. , Janssens Christel , Cossey Veerle , Debaveye Yves , Wynants Laure , Van Calster Ben 
Published: November 2024

SummaryBackgroundCentral line-associated bloodstream infections (CLABSI) are preventable hospital-acquired infections. Predicting CLABSI helps improve early intervention strategies and enhance patient safety.AimTo develop and temporally evaluate dynamic prediction models for continuous CLABSI risk monitoring.MethodsData from hospitalized patients with central catheter(s) admitted to University Hospitals Leuven between 2014 and 2017 were used to develop five dynamic models (a cause-specific landmark supermodel, two random forest models, and two XGBoost models) to predict 7-day CLABSI risk, accounting for competing events (death, discharge, and catheter removal). The models’ predictions were then combined using a superlearner model. All models were temporally evaluated on data from the same hospital from 2018 to 2020 using performance metrics for discrimination, calibration, and clinical utility.FindingsAmong 61629 catheter episodes in the training set, 1930 (3.1%) resulted in CLABSI, while in the test set of 44544 catheter episodes, 1059 (2.4%) experienced CLABSI.Among individual models, one XGBoost model reached an AUROC of 0.748. Calibration was good for predicted risks up to 5%, while the cause-specific and XGBoost models overestimated higher predicted risks. The superlearner displayed a modest improvement in discrimination (AUROC up to 0.751) and better calibration than the cause-specific and XGBoost models, but worse than the random forest models. The models showed clinical utility to support standard care interventions (at risk thresholds between 0.5-4%), but not to support advanced interventions (at thresholds 15-25%). A deterioration in model performance over time was observed on temporal evaluation.ConclusionHospital-wide CLABSI prediction models offer clinical utility, though temporal evaluation revealed dataset shift.
MRI-based surrogates of brain clearance in narcolepsy type 1
Authors: van Heese Eva M. , Gool Jari K. , Lammers Gert Jan , van der Werf Ysbrand D. , van Osch Matthias J.P. , Fronczek Rolf , Hirschler Lydiane 
Published: November 2024

AbstractBrain clearance involves the drainage of waste molecules from the brain, a process that is suggested to be amplified during sleep. Recently proposed MRI-based methods attempt to approximate human brain clearance with surrogate measures. The current study aimed to explore whether two brain clearance surrogates are altered in narcolepsy. We processed diffusion-weighted and functional resting-state images to extract two surrogates: Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS index), and dBOLD-CSF coupling. Both measures were analysed in 12 drug-free, awake people with narcolepsy type 1 and 11 age- and sex-matched controls, as well as in relation to clinical features. We also assessed the correlation between the DTI-ALPS index and dBOLD-CSF coupling. The DTI-ALPS index and dBOLD-CSF coupling amplitude identified were similar to previous research and did not show significant differences between narcolepsy and controls, nor significant relations with severity of excessive daytime sleepiness. We found a significant correlation between the DTI-ALPS index and dBOLD-CSF coupling amplitude. The hypothesis of altered brain clearance in narcolepsy type 1 is not supported by evidence from the current study. The two surrogates correlated with each other, suggesting that both offer different perspectives from the same underlying physiology. Yet, the suitability of the surrogates as brain clearance markers remains debatable. Whereas DTI is not exclusively sensitive to perivascular fluid, dBOLD-CSF coupling is reflecting large-scale CSF motion. Future work should explore other surrogate markers, preferably during sleep, to better understand the possible role of altered brain clearance in narcolepsy type 1 symptomatology.
Reference values and determinants of fractional exhaled nitric oxide in a representative adult population in Western Sweden
Authors: Abohalaka Reshed , Ercan Selin , Lehtimäki Lauri , Özuygur Ermis Saliha Selin , Lisik Daniil , Awad Bashir Muwada Bashir , Jadhav Radhika , Ekerljung Linda , Wennergren Göran , Lötvall Jan , Pullerits Teet , Backman Helena , Rådinger Madeleine , Nwaru Bright Ibeabughichi , Kankaanranta Hannu 
Published: November 2024

AbstractBackgroundFractional exhaled nitric oxide (FeNO) is used to differentiate asthma inflammatory phenotypes and guide its management. However, data on FeNO reference values in a representative adult population is limited.ObjectiveTo derive reference values and determinants of FeNO in a representative adult population.MethodsThe West Sweden Asthma Study is a clinical-epidemiological population- representative study of randomly selected adults in Western Sweden. From this cohort, 943 subjects participated in comprehensive clinical investigations, including skin prick testing (SPT), specific immunoglobulin E (sIgE) analysis, and FeNO measurement. Clinical allergy was defined as co-occurrence of atopy (positivity to SPT or sIgE) and self-reported allergic symptoms to the same allergen family. FeNO levels were analysed in relation to the presence or absence of clinical allergy, asthma, and other factors.ResultsThe 95thpercentile of FeNO ranged from 34 to 52 parts per billion (ppb) in the entire sample (N=943), and from 26 to 37 ppb among individuals without clinical allergy, asthma, or chronic obstructive pulmonary disease (COPD) (n=587), depending on age. Sex, smoking, clinical allergy, atopy, asthma, and hypertension influenced FeNO levels, meanwhile, age, asthma, clinical allergy, and reversibility- related variables were significant determinants of FeNO levels.ConclusionThe 95thpercentile (upper normal limit) for FeNO ranges from 34 to 52 ppb overall, and from 26 to 37 ppb in those without clinical allergy, asthma, or COPD, depending on age. These findings provide a guide for interpreting FeNO in the general population and in asthma and COPD clinics.
Unsupervised cardiac MRI phenotyping with 3D diffusion autoencoders reveals novel genetic insights
Authors: Ometto Sara , Chatterjee Soumick , Vergani Andrea Mario , Landini Arianna , Sharapov Sodbo , Giacopuzzi Edoardo , Visconti Alessia , Bianchi Emanuele , Santonastaso Federica , Soda Emanuel M. , Cisternino Francesco , Ieva Francesca , Di Angelantonio Emanuele , Pirastu Nicola , Glastonbury Craig A. 
Published: November 2024

AbstractBiobank-scale imaging provides a unique opportunity to characterise structural and functional cardiac phenotypes and how they relate to disease outcomes. However, deriving specific phenotypes from MRI data requires time-consuming expert annotation, limiting scalability and does not exploit how information dense such image acquisitions are. In this study, we applied a 3D diffusion autoencoder to temporally resolved cardiac Magnetic Resonance Imaging (MRI) data from 71,021 UK Biobank participants to derive latent phenotypes representing the human heart in motion. These phenotypes were reproducible, heritable (h2= [4 - 18%]), and significantly associated with cardiometabolic traits and outcomes, including atrial fibrillation (P= 8.5×10−29) and myocardial infarction (P= 3.7×10−12). By using latent space manipulation techniques, we directly interpreted and visualised what specific latent phenotypes were capturing in a given MRI. To establish the genetic basis of such traits, we performed a genome-wide association study, identifying 89 significant common variants (P <2.3×10−9) across 42 loci, including seven novel loci. Extensive multi-trait colocalisation analyses (PP.H4>0.8) linked these variants to various cardiac traits and diseases, revealing a shared genetic architecture spanning phenotypic scales. Polygenic Risk Scores (PRS) derived from latent phenotypes demonstrated predictive power for a range of cardiometabolic diseases and high risk individuals had substantially increased cumulative hazard rates across a range of diseases. This study showcases the use of diffusion autoencoding methods as powerful tools for unsupervised phenotyping, genetic discovery and disease risk prediction using cardiac MRI imaging data.
Accurate Machine Learning Model for Human Embryo Morphokinetic Stage Detection
Authors: Misaghi H , Cree L , Knowlton N 
Published: November 2024

AbstractPurposeThe ability to detect, monitor, and precisely time the morphokinetic stages of human embryos plays a critical role in assessing their viability and potential for successful implantation. In this context, the development and utilization of accurate and accessible tools for analysing embryo development are needed. This work introduces a highly accurate, machine learning model designed to predict 16 morphokinetic stages of pre-implantation human development, which is a significant improvement over existing models. This provides a robust tool for researchers and clinicians to use to automate the prediction of morphokinetic stage, allowing standardisation and reducing subjectivity between clinics.MethodA computer vision model was built on a public dataset for embryo Morphokinetic stage detection containing approximately 273,438 labelled images based on Embryoscope/+© embryo images. The dataset was split 70/10/20 into training/validation/test sets. Two different deep learning architectures were trained and tested, one using efficient net V2 and the other using efficient-net V2 with the addition of post-fertilization time as input. A new postprocessing algorithm was developed to reduce the noise in predictions of the deep learning model and detect the exact time of each morphokinetic stage change.ResultsThe proposed model reached an overall test accuracy of 87% across 17 morphokinetic stages on an independent test set. If only considering plus or minus one developmental stage, the accuracy rises to 97.1%.ConclusionThe proposed model shows state-of-the-art performance (17% accuracy improvement compared to the best models on the same dataset) to detect morphokinetic stages in static embryo images as well as detecting the exact moment of stage change in a complete time-lapse video.
Suppression of Huntington’s Disease Somatic Instability by Transcriptional Repression and Direct CAG Repeat Binding
Authors: Mathews Ella W. , Coffey Sydney R. , Gärtner Annette , Belgrad Jillian , Bragg Robert M. , O’Reilly Daniel , Cantle Jeffrey P. , McHugh Cassandra , Summers Ashley , Fentz Joachim , Schwagarus Tom , Cornelius Antje , Lingos Ioannis , Burch Zoe , Kovalenko Marina , Andrew Marissa A , Bennett Frank C. , Kordasiewicz Holly B. , Marchionini Deanna M. , Wilkinson Hilary , Vogt Thomas F. , Pinto Ricardo M. , Khvorova Anastasia , Howland David , Wheeler Vanessa C. , Carroll Jeffrey B. 
Published: November 2024

AbstractHuntington’s disease (HD) arises from a CAG expansion in thehuntingtin(HTT) gene beyond a critical threshold. A major thrust of current HD therapeutic development is lowering levels of mutantHTTmRNA (mHTT) and protein (mHTT) with the aim of reducing the toxicity of these product(s). Human genetic data also support a key role for somatic instability (SI) inHTT’s CAG repeat – whereby it lengthens with age in specific somatic cell types – as a key driver of age of motor dysfunction onset. Thus, an attractive HD therapy would address both mHTT toxicity and SI, but to date the relationship between SI and HTT lowering remains unexplored. Here, we investigated multiple therapeutically-relevant HTT-lowering modalities to establish the relationship between HTT lowering and SI in HD knock-in mice. We find that repressing transcription of mutantHtt(mHtt) provides robust protection from SI, using diverse genetic and pharmacological approaches (antisense oligonucleotides, CRISPR-Cas9 genome editing, theLacrepressor, and virally delivered zinc finger transcriptional repressor proteins, ZFPs). However, we find that small interfering RNA (siRNA), a potent HTT-lowering treatment, lowers HTT levels without influencing SI and that SI is also normal in mice lacking 50% of total HTT levels, suggesting HTT levels,per se, do not modulate SI intrans. Remarkably, modified ZFPs that bind the mHttlocus, but lack a repressive domain, robustly protect from SI, despite not reducing HTT mRNA or protein levels. These results have important therapeutic implications in HD, as they suggest that DNA-targeted HTT-lowering treatments may have significant advantages compared to other HTT-lowering approaches, and that interaction of a DNA-binding protein andHTT’s CAG repeats may provide protection from SI while sparing HTT expression.
Three positively charged binding sites on the eastern equine encephalitis virus E2 glycoprotein coordinate heparan sulfate- and protein receptor-dependent infection
Authors: Alcorn Maria D.H. , Sun Chengqun , Gilliland Theron C. , Lukash Tetyana , Crasto Christine M. , Raju Saravanan , Diamond Michael S. , Weaver Scott C. , Klimstra William B. 
Published: November 2024

SUMMARYNaturally circulating strains of eastern equine encephalitis virus (EEEV) bind heparan sulfate (HS) receptors and this interaction has been linked to its neurovirulence. Previous studies associated EEEV-HS interactions with three positively charged amino acid clusters on the E2 glycoprotein. One of these sites has recently been reported to be critical for binding EEEV to very-low-density lipoprotein receptor (VLDLR), an EEEV receptor protein. The proteins apolipoprotein E receptor 2 (ApoER2) isoforms 1 and 2, and LDLR have also been shown to function as EEEV receptors. Herein, we investigate the individual contribution of each HS interaction site to EEEV HS- and protein receptor-dependent infectionin vitroand EEEV replication in animals. We show that each site contributes to both EEEV-HS and EEEV-protein receptor interactions, providing evidence that altering these interactions can affect disease in mice and eliminate mosquito infectivity. Thus, multiple HS-binding sites exist in EEEV E2, and these sites overlap functionally with protein receptor interaction sites, with each type of interaction contributing to tissue infectivity and disease phenotypes.
Open-source Tools for CryoET Particle Picking Machine Learning Competitions
Authors: Harrington Kyle I. , Zhao Zhuowen , Schwartz Jonathan , Kandel Saugat , Ermel Utz , Paraan Mohammadreza , Potter Clinton , Carragher Bridget 
Published: November 2024

AbstractWe are launching a machine learning (ML) competition focused on particle picking in cryo-electron tomography (cryoET) data, a crucial task in structural biology. To support this, we have created a comprehensive suite of open-source tools to develop resources for our competition, including copick for dataset management, napari plugins for interactive visualization, utilities for converting particle picks to segmentation masks, and PyTorch tools for custom dataset sampling. These resources streamline the processes of data handling, labeling, and visualization, allowing participants to focus on model development. By leveraging these tools, competitors will be better equipped to tackle the unique challenges of cryoET data and push forward advancements in particle picking techniques.
Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding
Authors: Shan Tong , Lalor Edmund C. , Maddox Ross K. 
Published: November 2024

AbstractPitch and time are the essential dimensions defining musical melody. Recent electrophysiological studies have explored the neural encoding of musical pitch and time by leveraging probabilistic models of their sequences, but few have studied how the features might interact. This study examines these interactions by introducing “chimeric music,” which pairs two distinct melodies, and exchanges their pitch contours and note onset-times to create two new melodies, thereby distorting musical pattern while maintaining the marginal statistics of the original pieces’ pitch and temporal sequences. Through this manipulation, we aimed to dissect the music processing and the interaction between pitch and time. Employing the temporal response function (TRF) framework, we analyzed the neural encoding of melodic expectation and musical downbeats in participants with varying levels of musical training. Our findings revealed differences in the encoding of melodic expectation between original and chimeric stimuli in both dimensions, with a significant impact of musical experience. This suggests that the structural violation due to decoupling the pitch and temporal structure affect expectation processing. In our analysis of downbeat encoding, we found an enhanced neural response when participants heard a note that aligned with the downbeat during music listening. In chimeric music, responses to downbeats were larger when the note was also a downbeat in the original music that provided the pitch sequence, indicating an effect of pitch structure on beat perception. This study advances our understanding of the neural underpinnings of music, emphasizing the significance of pitch-time interaction in the neural encoding of music.Significance StatementListening to music is a complex and multidimensional auditory experience. Recent studies have investigated the neural encoding of pitch and timing sequences in musical structure, but they have been studied independently. This study addresses the gap in understanding of how the interaction between pitch and time affects their encoding. By introducing “chimeric music,” which decouples these dimensions in melodies, we investigate how this interaction influences the neural activities using EEG. Leveraging and the temporal response function (TRF) framework, we found that structural violations in pitch-time interactions impact musical expectation processing and beat perception. These results advance our knowledge of how the brain processes complex auditory stimuli like music, underscoring the critical role of pitch and time interactions in music perception.