bluuebunny/crossref_metadata_2025_split huggingface dataset:
Critical dynamics predict cognitive performance and are disrupted by epileptic spikes, antiseizure medication and slow-wave activity
Authors: Müller Paul Manuel , Miron Gadi , Holtkamp Martin , Meisel Christian 
Published: August 2024

AbstractThe brain criticality hypothesis postulates that brain dynamics are set at a phase transition where information processing is optimized. Long-range temporal correlations (TCs) characterizing the dissipation of information within a signal have been shown to be a hallmark of brain criticality. However, the experimental link between cognitive performance, criticality and thus TCs has remained elusive due to limitations in recording length, and spatial and temporal resolution. In this study, we investigate multi-day invasive EEG recordings of 104 persons with epilepsy together with an extensive cognitive test battery. We show that short TCs predict cognitive impairment. Further, we show that heterogeneous factors, including interictal epileptiform discharges (IEDs), antiseizure medications (ASMs) and intermittent periods with slow-wave activity (SWSs), all act directly to perturb critical dynamics and thus cognition. Our work suggests critical dynamics to be the setpoint to measure optimal network function, thereby providing a unifying framework for the heterogeneous mechanisms impacting cognition in conditions like epilepsy.
Rapid Whole Genome Characterization of High-Risk Pathogens Using Long-Read Sequencing to Identify Potential Healthcare Transmission
Authors: Wu Chin-Ting , Shropshire William C. , Bhatti Micah M , Cantu Sherry , Glover Israel K , Anand Selvalakshmi Selvaraj , Liu Xiaojun , Kalia Awdhesh , Treangen Todd J. , Chemaly Roy F , Spallone Amy , Shelburne Samuel 
Published: August 2024

AbstractObjectiveRoutine use of whole genome sequencing (WGS) has been shown to help identify transmission of pathogens causing healthcare-associated infections (HAIs). However, the current gold standard of short-read, Illumina-based WGS is labor and time-intensive. In light of recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing, we sought to establish a low resource utilization approach capable of providing accurate WGS-based comparisons of HAI pathogens within a time frame allowing for infection prevention and control (IPC) interventions.MethodsWGS was prospectively performed on antimicrobial-resistant pathogens at increased risk of potential healthcare transmission using the ONT MinION sequencer with R10.4.1 flow cells and Dorado basecalling algorithm. Potential transmission was assessed via Ridom SeqSphere+ for core genome multilocus sequence typing and MINTyper for reference-based core genome single nucleotide polymorphisms using previously published cut-off values. The accuracy of our ONT pipeline was determined relative to Illumina-based WGS data generated from the same genomic DNA sample.ResultsOver a six-month period, 242 bacterial isolates from 216 patients were sequenced by a single operator. Compared to the Illumina gold-standard data, our ONT pipeline achieved a Q score of 60 for assembled genomes, even with a coverage rate of as low as 40X. The mean time from initiating DNA extraction to complete genetic analysis was 2 days (IQR 2-3.25 days). We identified five potential transmission clusters comprising 21 isolates (8.7% of all sequenced strains). Combining ONT WGS data with epidemiological data, >70% (15/21) of the isolates originated from patients with potential healthcare transmission links.ConclusionsVia a stand-alone ONT pipeline, we detected potentially transmitted HAI pathogens rapidly and accurately, aligning closely with epidemiological data. Our low-resource method has the potential to assist in the efficient detection and deployment of preventative measures against HAI transmission.
Uptake of the Interim Canada Dental Benefit: An investigation of data from the first 18 months of the program
Authors: Goubran Saif , de Jesus Vivianne Cruz , Menon Anil , Olatosi Olubukola O. , Schroth Robert J. 
Published: August 2024

AbstractIntroductionIn 2022, the Government of Canada introduced the Interim Canada Dental Benefit (CDB) to support Canadian families with children < 12 years of age. This program operated from October 1, 2022, to June 30, 2024, with two application periods. The purpose of this study was to analyze data on applications accepted by the Canada Revenue Agency (CRA) during the first 18 months of the program.MethodsThis study used available data sourced from the CRA for applicants as of March 29, 2024, and assessed as of April 5, 2024. Data covered the entirety of the first period (October 1, 2022–June 30, 2023) of the Interim CDB and the first nine months of the second period (July 1, 2023–March 29, 2024). The rate of child participation was calculated using population data from Statistics Canada (2021).ResultsOver the first 18 months of the Interim CDB, a total of 410,920 applications were submitted and $403M distributed; $197M for 204,270 applications in period 1 and $175M for 173,160 applications in the first nine months of period 2. A total of 91.8% of applicants had a net family income < $70,000, receiving the maximum benefit amount. The provinces with the highest rate of child participation were Manitoba (77.1/1,000 period 1; 74.9/1,000 period 2), Ontario (82.5/1,000 period 1; 72.2/1,000 period 2), Nova Scotia (73.4/1,000 period 1; 71.1/1,000 period 2), and Saskatchewan (72.3/1,000 period 1; 68.2/1,000 period 2). Overall, there was an increase in the number of applications approved in period 2 compared to period 1.ConclusionsUptake in the first three quarters of period 2 remained consistent and in many instances, revealed higher rates of applications by parents for the Interim CDB. Analyzing this data will aid in policy recommendation for enhancement of the Canadian Dental Care Program.
Examining the Role of Social Environment on COVID-19 Infections and Vaccine Uptake Among Migrants in Japan: Findings from Nationwide Surveys in 2021 and 2023
Authors: Teng Yuanyuan , Sabel Clive E. , Hanibuchi Tomoya , Nakaya Tomoki 
Published: August 2024

AbstractStudies have shown that migrants and ethnic minority groups were unevenly affected by the COVID-19 pandemic, with most evidence coming from Western countries. This study explores the experience of migrants in Japan during the COVID-19 pandemic, examining the association between social environment (i.e., population density, neighborhood deprivation, ethnic density, and social networks) and both COVID-19 infections and vaccination uptake. Data from two nationwide online surveys conducted in 2021 and 2023 were used to analyze these associations during the middle and waning stages of the pandemic. We found that migrants in Japan were more vulnerable to COVID-19 infection but were more receptive to vaccination compared to Japanese nationals. However, not all migrant groups experienced the same outcomes. The effects of social environmental factors on infection and vaccination acceptance were complex and could change, as the pandemic progressed. Environmental factors such as neighborhood population density were not necessarily determinants of COVID-19 infection. More social contacts with Japanese nationals could mitigate vaccination hesitancy, while larger social ties with co-national in the neighborhoods could lead to a higher infection rate. In anticipation of future pandemics, customized programs should be prepared to address the unique healthcare needs of migrants.
Incorporating computer vision on smart phone photographs into screening for inflammatory arthritis: results from an Indian patient cohort
Authors: Phatak Sanat , Saptarshi Ruchil , Sharma Vanshaj , Shah Rohan , Zanwar Abhishek , Hegde Pratiksha , Chakraborty Somashree , Goel Pranay 
Published: August 2024

AbstractBackgroundConvolutional neural networks (CNNs) have been used to classify medical images; few studies use smartphone photographs that are scalable at point of care. We previously showed proof of principle that CNNs could detect inflammatory arthritis in three hand joints. We now studied a screening CNN to differentiate from controls.MethodsWe studied consecutive patients with early inflammatory arthritis and healthy controls, all examined by a rheumatologist (15% by two). Standardized photographs of the hands were taken using a studio box, anonymized, and cropped around joints. We fine-tuned pre-trained CNN models on our dataset (80% training; 20% test set). We used an Inception-ResNet-v2 backbone CNN modified for two class outputs (Patient vs Control) on uncropped photos. Inception-ResNet-v2 CNNs were trained on cropped photos of Middle finger Proximal Interphalangeal (MFPIP), Index finger PIP (IFPIP) and wrist. We report representative values of accuracy, sensitivity, specificity.ResultsWe studied 800 hands from 200 controls (mean age 37.8 years) and 200 patients (mean age 49.6 years; 134 with rheumatoid arthritis amongst other diagnoses). Two rheumatologists had a concordance of 0.89 in 404 joints. The wrist was commonly involved (173/400) followed by the MFPIP (134) and IFPIP (128). The screening CNN achieved excellent accuracy (98%), sensitivity (98%) and specificity (98%) in predicting a patient compared to controls. Joint-specific CNN accuracy, sensitivity and specificity were highest for the wrist (80%, 88%, 72%) followed by the IFPIP (79%, 89%,73%) and MFPIP (76%, 91%, 70%).ConclusionComputer vision without feature engineering can distinguish between patients and controls based on smartphone photographs with good accuracy, showing promise as a screening tool prior to joint-specific CNNs. Future research includes validating findings in diverse populations, refining models to improve specificity in joints and integrating this technology into clinical workflows.
Tesorai Search: Large pretrained model boosts identifications in mass spectrometry proteomics without the need for Percolator
Authors: Burq Maximilien , Stepec Dejan , Restrepo Juan , Zbontar Jure , Urazbakhtin Shamil , Crampton Bryan , Tiwary Shivani , Miao Melissa , Cox Juergen , Cimermancic Peter 
Published: August 2024

AbstractCurrent database search algorithms for mass spectrometry proteomics typically fail to identify up to 75% of spectra. To mitigate the issue, tools such as the widely used Percolator and PeptideProphet leverage machine learning for rescoring. Though this can boost peptide and protein identification rates, the fact that they are trained to separate targets from decoys can lead to inaccurate false-discovery rate (FDR) estimates. In this paper, we propose a novel approach to peptide-spectrum matching, based on a pre-trained large deep learning model, which does not utilize decoys during training and does not require training a new model for every new sample. We trained the Tesorai model on over 100M real peptide-spectrum pairs and demonstrated that the approach performs robustly across a wide range of use cases including standard trypsin-digested human samples, immunopeptidomics, metaproteomics, single-cell and isobaric-labeled samples. In addition to providing robust FDR control, our method increases identifications by up to 170%. Furthermore, we incorporated it into a cloud-native, end-to-end system that can process hundreds of samples in just a few hours. To facilitate new discoveries, we made the model publicly available atwww.tesorai.com.
LactobacillusRestructures the Micro/Mycobiome to Combat Glycoprotein-130 Associated Microtubule Remodeling and Right Ventricular Dysfunction in Pulmonary Arterial Hypertension
Authors: Prisco Sasha Z. , Blake Madelyn , Kazmirczak Felipe , Moon Ryan , Vogel Neal , Moutsoglou Daphne , Thenappan Thenappan , Prins Kurt W. 
Published: August 2024

AbstractEmerging data demonstrate systemic and local inflammation regulate right ventricular (RV) adaption in preclinical and human pulmonary arterial hypertension (PAH). Pathological RV inflammation is targetable as antagonism of glycoprotein-130 (GP130) signaling counteracts pathological microtubule remodeling and improves RV function in rodents. Microtubules control several aspects of cardiomyocyte biology including cellular and nuclear size/structure, t-tubule homeostasis, and the proper localization of connexin-43. The intestinal microbiome regulates systemic inflammation, but the impact of the gut microbiome on the GP130-microtubule axis in RV failure is unknown. Here, we examined how the anti-inflammatory bacteria,Lactobacillus, modulated cellular and physiological RV phenotypes in preclinical and clinical PAH.Lactobacillussupplementation restructured the gut micro/mycobiome, suppressed systemic inflammation, combatted pathological GP130-mediated RV cardiomyocyte microtubule remodeling, and augmented RV function in rodent PAH. Moreover,Lactobacilluswas associated with superior RV adaption in human PAH. These data further support the hypothesis that inflammation negatively impacts RV adaption in PAH, and identify the gut microbiome as a potentially targetable regulator of RV function in PAH.
Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning
Authors: Rejuan Rifat , Aulisa Eugenio , Li Wei , Thompson Travis , Kumar Sanjoy , Canic Suncica , Wang Yifan 
Published: August 2024

AbstractMicrofluidic devices (MDs) present a novel method for detectingcirculating tumor cells(CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.Author summaryEarly detection is currently the most effective method to combat cancer, as it maximizes treatment options and improves potential survival rates. However, the cost of early detection presents a significant barrier, limiting access for underrepresented groups and discouraging industrial partners from investing in the research and development of screening devices. This study provides an in-silico conceptual validation for the development of an innovative hyperuniform microchip designed to identify circulating tumor cells (CTCs) without the need for biomarker labeling. We created a cell-based modeling framework to examine CTC dynamics in erythrocyte-laden plasma flow, producing an extensive dataset of CTC trajectories that reflect two distinct CTC phenotypes. Two machine learning architectures were utilized to analyze this dataset and classify the phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising and cost-effective method for early cancer detection.
Tumor-associated macrophages confer resistance to chemotherapy (Trifluridine/Tipiracil) in digestive cancers by overexpressing Thymidine Phosphorylase
Authors: Malier Marie , Laverriere Marie-Hélène , Henry Maxime , Yakoubi Malika , Bellaud Pascale , Arellano Cécile , Sébillot Anthony , Thomas Fabienne , Josserand Véronique , Girard Edouard , Roth Gael S , Millet Arnaud 
Published: August 2024

AbstractPyrimidine analogs are part of the first-line chemotherapy regimen for gastrointestinal cancers. Trifluridine combined with tipiracil, a specific thymidine phosphorylase inhibitor, in TAS-102 has recently emerged as a potential alternative in the face of primary or secondary chemoresistance to 5-fluorouracil. Despite its promise, in the current study, we report that macrophage-specific overexpression of thymidine phosphorylase results in macrophage-induced chemoresistance to TAS-102 that is insensitive to tipiracil inhibition. In addition, we demonstrate the human specificity of this mechanism, as mouse macrophages do not express significant levels of thymidine phosphorylase. To study the importance of macrophages in chemoresistance to trifluridine, we developed a humanized mouse model with tumor-implanted human macrophages and demonstrated their important role in treatment resistance to pyrimidine analogs. We also showed in human colorectal cancer that macrophages represent a major source of thymidine phosphorylase expression leading to chemoresistance.SignificanceThymidine phosphorylase overexpression in TAMs confers chemoresistance to TAS-102 in digestive cancers.
Burst Estimation through Atomic Decomposition (BEAD): A Toolbox to find Oscillatory Bursts in Brain Signals
Authors: Anand Abhishek , Murthy Chandra , Ray Supratim 
Published: August 2024

AbstractRecent studies have shown that brain signals often show oscillatory bursts of short durations, which have been linked to various aspects of computation and behavior. Traditional methods often use direct spectral estimators to estimate the power of brain signals in spectral and temporal domains, from which bursts are identified. However, direct spectral estimators are known to be noisy, such that even stable oscillations may appear bursty. We have previously shown that the Matching Pursuit (MP) algorithm, which uses a large overcomplete dictionary of basis functions (called “atoms”) to decompose the signal directly in the time domain, partly addresses this concern and robustly finds long bursts in synthetic as well as real data. However, MP is a greedy algorithm that can give non-optimal solutions and requires a large-sized dictionary. To address these concerns, we extended two other algorithms – orthogonal MP (OMP) and OMP using Multiscale Adaptive Gabor Expansion (OMP-MAGE), to perform burst duration estimation. We also develop a novel algorithm, called OMP using Gabor Expansion with Atom Reassignment (OMP-GEAR). These algorithms overcome the limitations of MP and can work with a significantly smaller dictionary size. We find that, in synthetic data, OMP, OMP-MAGE and OMP-GEAR converge faster than MP. Also, OMP-MAGE and OMP-GEAR outperform both MP and OMP when the dictionary size is small. Finally, OMP-GEAR significantly outperforms OMP-MAGE when the bursts are overlapping. Importantly, the burst durations obtained using MP and OMP with a very large-sized dictionary are comparable to that obtained using OMP-MAGE with a much smaller-sized dictionary in real data obtained from two monkeys passively viewing static gratings which induced gamma bursts in the primary visual cortex. OMP-GEAR yields slightly smaller burst durations, but all the estimated burst durations are still significantly larger than the duration estimated using traditional methods. These results suggest that gamma bursts are longer than previously reported. Raw data from two monkeys, as well as codes for both traditional and new methods, are publicly available as part of this toolbox.
Optimal methods for analyzing targeted pairwise knockout screens
Authors: Chou Juihsuan , Anvar Nazanin Esmaeili , Elghaish Reem , Chen Junjie , Hart Traver 
Published: August 2024

AbstractBackgroundSynthetic lethality offers a promising strategy for cancer treatment by targeting genetic vulnerabilities unique to tumor cells, leading to selective tumor cell death. However, single-gene knockout screens often miss functional redundancy due to paralog genes. Multiplex CRISPR systems, including various Cas9 and Cas12a platforms, have been developed to assay genetic interactions, yet no systematic comparison of method to identify synthetic lethality from CRISPR screens has been conducted.ResultsWe evaluated data from four in4mer CRISPR/Cas12a screens in cancer cell lines, using three bioinformatic approaches to identify synthetic lethal interactions: delta log fold change (dLFC), Z-transformed dLFC (ZdLFC), and rescaled dLFC (RdLFC). Both ZdLFC and RdLFC provided more consistent identification of synthetic lethal pairs across cell lines compared to the unscaled dLFC method.ConclusionsThe ZdLFC method offers a robust framework for scoring synthetic lethal interactions from paralog screens, providing consistent results across different cell lines without requiring a training set of known positive interactors.
Structural insights into the mechanism of phosphate recognition and transport by human XPR1
Authors: Zhang Wenhui , Chen Yanke , Guan Zeyuan , Wang Yong , Tang Meng , Du Zhangmeng , Zhang Jie , Cheng Meng , Zuo Jiaqi , Liu Yan , Wang Qiang , Liu Yanjun , Zhang Delin , Yin Ping , Ma Ling , Liu Zhu 
Published: August 2024

AbstractXPR1 is the only known protein responsible for transporting inorganic phosphate (Pi) out of cells, a function conserved from yeast to mammals. Human XPR1 variants lead to cerebral calcium-phosphate deposition, which are associated with a hereditary neurodegenerative disorder known as primary familial brain calcification (PFBC). Here, we present the cryo-EM structure of human XPR1 in both its Pi-unbound form and various Pi-bound states. XPR1 features 10 transmembrane α-helices that form an ion channel-like architecture. Multiple Pi recognition sites are arranged along the channel, facilitating Pi ion transport. Two arginine residues, subject to pathogenic mutation in PFBC families, line the translocation channel and serve to bind Pi ion. Clinically linked mutations in these arginines impair XPR1’s Pi transport activity. To gain dynamic insights into the channel-like transport mechanism, we conducted molecular dynamics simulations. The simulations reveal that Pi ion undergoes a stepwise transition through the sequential recognition sites during the transport process. Together with functional analyses, our results suggest that the sequential arrangement of Pi recognition sites likely enable XPR1 to use a “relay” process to facilitate Pi ion passage through the channel, and they establish a framework for the interpretation of disease-related mutations and for the development of future therapeutics.One Sentence SummaryCombined cryo-EM, molecular dynamics simulations and functional studies demonstrate that human XPR1 employs a channel-like transport mechanism to export inorganic phosphate out of cells