bluuebunny/crossref_metadata_2025_split huggingface dataset:
Zoonotic avian influenza viruses evade human BTN3A3 restriction
Authors: Pinto Rute Maria , Bakshi Siddharth , Lytras Spyros , Zakaria Mohammad Khalid , Swingler Simon , Worrell Julie C , Herder Vanessa , Varjak Margus , Cameron-Ruiz Natalia , Rodriguez Mila Collados , Varela Mariana , Wickenhagen Arthur , Loney Colin , Pei Yanlong , Hughes Joseph , Valette Elise , Turnbull Matthew L , Furnon Wilhelm , Hargrave Kerrie E , Gu Quan , Orr Lauren , Taggart Aislynn , Boutell Chris , Grey Finn , Hutchinson Edward , Digard Paul , Monne Isabella , Wootton Sarah K , MacLeod Megan K L , Wilson Sam J , Palmarini Massimo 
Published: June 2022

AbstractCross-species transmission of avian influenza A viruses (IAVs) into humans could represent the first step of a future pandemic1. Multiple factors limiting the spillover and adaptation of avian IAVs in humans have been identified, but they are not sufficient to explain which virus lineages are more likely to cross the species barrier1,2. Here, we identified human BTN3A33 (butyrophilin subfamily 3 member A3) as a potent inhibitor of avian but not human IAVs. We determined that BTN3A3 is constitutively expressed in human airways and its antiviral activity evolved in primates. We show that BTN3A3 restriction acts at the early stages of virus replication by inhibiting avian IAV vRNA transcription. We identified residue 313 in the viral nucleoprotein (NP) as the genetic determinant of BTN3A3 sensitivity (313F, or rarely 313L in avian viruses) or evasion (313Y or 313V in human viruses). However, several serotypes of avian IAVs that spilled over into humans in recent decades evade BTN3A3 restriction. In these cases, BTN3A3 evasion is due to substitutions (N, H or Q) in NP residue 52 that is adjacent to residue 313 in the NP structure4. Importantly, we identified more than 150 avian IAV lineages with a BTN3A3-resistant genotype. In conclusion, sensitivity or resistance to BTN3A3 is another factor to consider in the risk assessment of the zoonotic potential of avian influenza viruses.
Cytomulate: accurate and efficient simulation of CyTOF data
Authors: Yang Yuqiu , Wang Kaiwen , Lu Zeyu , Wang Tao , Wang Xinlei 
Published: June 2022

AbstractRecently, many analysis tools have been devised to offer insights into data generated via Cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter and generative models like LAMBDA.
AuCoMe: inferring and comparing metabolisms across heterogeneous sets of annotated genomes
Authors: Belcour Arnaud , Got Jeanne , Aite Méziane , Delage Ludovic , Collen Jonas , Frioux Clémence , Leblanc Catherine , Dittami Simon M. , Blanquart Samuel , Markov Gabriel V. , Siegel Anne 
Published: June 2022

AbstractComparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe – a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics
Authors: Khaniabadi Pegah Moradi , Bouchareb Yassine , Al-Dhuhli Humoud , Shiri Isaac , Al-Kindi Faiza , Khaniabadi Bita Moradi , Zaidi Habib , Rahmim Arman 
Published: June 2022

AbstractObjectiveWe aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.MethodsThree hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times.ResultsNine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.ConclusionThe two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.
Improving de novo Protein Binder Design with Deep Learning
Authors: Bennett Nathaniel , Coventry Brian , Goreshnik Inna , Huang Buwei , Allen Aza , Vafeados Dionne , Peng Ying Po , Dauparas Justas , Baek Minkyung , Stewart Lance , DiMaio Frank , De Munck Steven , Savvides Savvas N. , Baker David 
Published: June 2022

AbstractWe explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.
Pre-neuronal processing of haptic sensory cues via dispersive high-frequency vibrational modes
Authors: Ding Yu , Vlasov Yurii 
Published: June 2022

AbstractSense of touch is one of the major perception channels. Neural coding of object textures conveyed by rodents’ whiskers has been a model to study early stages of haptic information uptake. While high-precision spike timing has been observed during whisker sweeping across textured surfaces, the exact nature of whisker micromotions that spikes encode remains elusive. Here, we discovered that a single micro-collision of a whisker with surface features generates vibrational eigenmodes spanning frequencies up to 10KHz. While propagating along the whisker, these high-frequency modes can carry up to 80% of shockwave energy, exhibit 100X smaller damping ratio, and arrive at the follicle 10X faster than low frequency components. The mechano-transduction of these energy bursts into a time-sequenced population spike trains may generate temporally unique “bar code” with ultra-high information capacity. This hypothesis of pre-neuronal processing of haptic signals based on dispersive temporal separation of the vibrational modal frequencies can shed light on neural coding of haptic signals in many whisker-like sensory organs across the animal world as well as in texture perception in primate’s glabrous skin.Significance StatementUnderstanding how the outside world is encoded in neurons spikes in sensory organs and how these neural codes contribute to perception remains elusive. Using a model system - a whisker of a mouse - we discovered that tiny whisker vibrations induced at the whisker tip by collisions with external objects generate a time series of energy bursts. This creates a temporally unique “bar code” of a time-sequenced population spike trains with ultra-high information capacity. We hypothesize that such a “pre-neuronal processing” of touch events into time-coded spikes can provide a conceptual link to understand neural coding in many whisker-like sensory organs across the animal world as well as in texture perception in primate’s glabrous skin.
A robust intensity distribution alignment for harmonization of T1w intensity values
Authors: Sederevičius Donatas , Bjørnerud Atle , Walhovd Kristine B. , Van Leemput Koen , Fischl Bruce , Fjell Anders M. 
Published: June 2022

AbstractVariations in image intensities between magnetic resonance imaging (MRI) acquisitions affect the subsequent image processing and its derived outcomes. Therefore, it is necessary to normalize images of different scanners/acquisitions, especially for longitudinal studies where a change of scanner or pulse sequence often happens. Here, we propose a robust intensity distribution alignment (RIDA) method to remove between-scan effects. The method is based on MRI T1w images acquired in close succession and robustly aligns two cumulative distribution functions (CDF) of voxel intensities to improve image-derived outcomes of a range of subcortical brain structures with different acquisition parameters. We compare RIDA with the other image harmonization methods: mica and RAVEL. We study three intra-scanner and three inter-scanner protocol variations among the same 20 participants scanned with Siemens 1.5T Avanto, 3T Skyra, and 3T Prisma scanners on the same day and use image-derived volumetric outputs from the Sequence Adaptive Multimodal Segmentation (SAMSEG) method. We find that CDF-based intensity harmonization (mica and RIDA) significantly reduces intensity differences, improves consistency in volume quantification, and increases spatial overlap between two images acquired in close succession. The improvements are most considerable if the intensity normalization is based on subcortical structures only (RIDA), excluding cortical regions, instead of the whole brain. However, the effect of the corrections varies considerably as a function of the compared scanners and sequences. In conclusion, the RIDA scaneffect normalization improves the consistency of image-derived measures, but its performance depends on several factors.
DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features
Authors: Luppino Federica , Adzhubei Ivan A. , Cassa Christopher A. , Toth-Petroczy Agnes 
Published: June 2022

AbstractDespite an increasing use of genomic sequencing in clinical practice, interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) are currently used to provide valuable evidence in variant classifications, but they often misclassify benign variants, contributing to potential misdiagnoses. Here, we developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense variants in actionable disease genes with improved performance over existing VEPs (20% decrease of false positive rate). Our tool has balanced specificity (82%) and sensitivity (94%) on clinical data, and the lowest misclassification rate on putatively benign variants among evaluated tools. DeMAG takes advantage of a novel epistatic feature, the ‘partners score’, which is based on evolutionary and structural partnerships of residues as estimated by evolutionary information and AlphaFold2 structural models. The ‘partners score’ as a general framework of epistatic interactions, can integrate not only clinical but functional information. We anticipate that our tool (demag.org) will facilitate the interpretation of variants and improve clinical decision-making.
Visuo-motor transformations in the intraparietal sulcus mediate the acquisition of endovascular medical skill
Authors: Paul Katja I. , Müller Karsten , Rousseau Paul-Noel , Glathe Annegret , Taatgen Niels A. , Cnossen Fokie , Lanzer Peter , Villringer Arno , Steele Christopher J. 
Published: June 2022

AbstractPerforming endovascular medical interventions safely and efficiently requires a diverse set of skills that need to be practised in dedicated training sessions. Here, we used multimodal magnetic resonance (MR) imaging to determine the structural and functional plasticity and core skills associated with skill acquisition. A training group learned to perform a simulator-based endovascular procedure, while a control group performed a simplified version of the task; multimodal MR images were acquired before and after training. Using a well-controlled interaction design, we found strong, multimodal evidence for the role of the intraparietal sulcus (IPS) in endovascular skill acquisition that is in line with previous work implicating the structure in simple visuo-motor and mental rotation tasks. Our results provide a unique window into the multimodal nature of rapid structural and functional plasticity of the human brain while learning a multifaceted and complex clinical skill. Further, our results provide a detailed description of the plasticity process associated with endovascular skill acquisition and highlight specific facets of skills that could enhance current medical pedagogy and be useful to explicitly target during clinical resident training.
Understanding the genome-wide transcription response to varying cAMP levels using phenomenological models in bacteria
Authors: Chakraborty Shweta , Singh Parul , Narain Seshasayee Aswin Sai 
Published: June 2022

AbstractAttempts to understand gene regulation by global transcription factors (TF) have largely been limited to expression studies under binary conditions of presence and absence of the TF. Studies addressing genome-wide transcriptional responses to changing TF concentration at high resolution are lacking. Here, we create a dataset containing the entireE.colitranscriptome as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill’s model to accurately summarise individual gene responses into 3 intuitively understandable parameters -k, nandEmaxreflecting the midpoint of dynamic range, non-linearity and sensitivity of a gene. cAMP-regulated genes show a small dynamic range with midpoints centred around wild-type cAMP concentrations, with genes activating in a switch-like fashion. Using this approach we show that cAMP-CRP affinity at promoters is well correlated to the sensitivity(Emax) of genes but not to the midpoints of dynamic range(k). Finally, genes belonging to different functional classes are tuned to differentk, nandEmax. We show phenomenological models to be a better alternative for studying gene expression trends compared to classical clustering methods with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network.
Publication games: in the web of reciprocity
Authors: Barta Zoltán 
Published: June 2022

1AbstractThe present processes of research assessment, i.e. focusing on one or a few, related, sciento-metrics, foster questionable authorship practices, like gifting authorship to non-contributing people. An especially harmful one of these unethical practices is the formation of publication cartels, where authors offer gift authorship to each other reciprocally. Here, by developing a simple model and a simulation of the publication process I investigate how beneficial cartels can be and what measure can be used to restrict them. My results indicate that publication cartels can significantly boost members’ productivity even if paper counts are weighted by the inverse of author number (the 1/n rule). Nevertheless, applying the 1/n rule generates conflicts of interest both among cartel members themselves and between members and non-members which might lead to the self-purification of the academic publishing industry.
Increased severity of chronic kidney disease in response to high potassium intake is dependent on mineralocorticoid receptor activation
Authors: Olivier Valérie , Arnoux Gregoire , Ramakrishnan Suresh , Sassi Ali , Roth Isabelle , Chassot Alexandra , Tournier Malaury , Dizin Eva , Hummler Edith , Rutkowski Joseph M. , Feraille Eric 
Published: June 2022

AbstractDietary treatment is seminal for management of chronic kidney disease (CKD). The aim of our project was to assess the effects of potassium intake on the progression of CKD. We used 2 mouse CKD models to analyze the effects of potassium intake on CKD : the unilateral ureteral obstruction (UUO) and the POD-ATTAC models. POD-ATTAC mice display a podocyte-specific apoptosis after the administration of a chemical inducer. We also studied the effect of mineralocorticoid receptor (MR) using UUO in kidney tubule-specific MR knockout mice.In both UUO and POD-ATTAC mice, high potassium diet increased interstitial fibrosis. High potassium diet also increased the abundance of the extracellular matrix protein fibronectin and decreased the abundance of the epithelial marker Na+-K+ ATPase. Consistently, POD-ATTAC mice fed with high potassium diet displayed lower glomerular filtration rate. Spironolactone, a MR antagonist, decreased fibrosis induced by high potassium diet in POD-ATTAC mice. However, kidney tubule-specific MR knockout did not improve the fibrotic lesions induced by UUO under normal or high potassium diets. Macrophages from high potassium-fed POD-ATTAC mice displayed higher mRNA levels of the pro-inflammatory chemokine MCP1. This effect was decreased by spironolactone, suggesting a role of MR signaling in myeloid cells in the pro-fibrotic effect of potassium-rich diet.High potassium intake generates more fibrosis leading to decreased kidney function in experimental CKD. MR signaling plays a pivotal role in this potassium-induced fibrosis. The effect of reducing potassium intake on CKD progression should be assessed in future clinical trials.Translational statementDietetic approach is a cheap and effective therapy to slow down the development of chronic kidney diseases and kidney fibrosis. Potassium-rich diets are protective against renal and cardiovascular events in the general population, albeit some conflicting data were obtained in patients with chronic kidney disease. We showed that potassium-rich diet accelerates fibrosis development, by enhancing kidney inflammation in two mouse models of chronic kidney disease. These data suggest that potassium-rich diets should not be advised in patients with chronic kidney disease, unless future clinical trials demonstrate any beneficial effect in these patients.
Biphasic JNK–Erk Signaling Separates Induction and Maintenance of Cell Senescence after DNA Damage
Authors: Netterfield Tatiana S. , Ostheimer Gerard J. , Tentner Andrea R. , Sorger Peter K. , Janes Kevin A. , Lauffenburger Douglas A. , Yaffe Michael B. 
Published: June 2022

SUMMARYGenotoxic stress in mammalian cells, including that caused by anti-cancer chemotherapy, can induce temporary cell cycle arrest, DNA damage-induced senescence (DDIS) or apoptotic cell death. Despite obvious clinical importance, it is unclear how the signals emerging from DNA damage are integrated together with other cellular signaling pathways monitoring the cell’s environment and/or internal state to control these different cell fates. Here, using a combination of single cell-based signaling measurements and tensor PLSR/PCA computational approaches, we show that the JNK and Erk MAPK signaling pathways regulate the initiation of senescence through the transcription factor AP-1 at early times after extrinsic DNA damage, and the Senescence Associated Secretory Phenotype, a hallmark of DDIS, at late times after damage. These results identify a time-based separation of function for the same signaling pathways beyond the classic DNA damage response that control the cell senescence decision and modulate the tumor microenvironment following genotoxic stress, and reveal a fundamental similarity between signaling mechanisms responsible for oncogene-induced senescence and senescence caused by extrinsic DNA damaging agents.
Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure
Authors: Langer Nicolas , Weber Maurice , Vieira Bruno Hebling , Strzelczyk Dawid , Wolf Lukas , Pedroni Andreas , Heitz Jonathan , Müller Stephan , Schultheiss Christoph , Tröndle Marius , Arango-Lasprilla Juan Carlos , Rivera Diego , Scarpina Federica , Zhao Qianhua , Leuthold Rico , Wehrle Flavia , Jenni Oskar G. , Brugger Peter , Zaehle Tino , Lorenz Romy , Zhang Ce 
Published: June 2022

AbstractBackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness.MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network.ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection.ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.