bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-world structure facilitates the rapid emergence of scene category information in visual brain signals
Authors: Kaiser Daniel , Häberle Greta , Cichy Radoslaw M. 
Published: March 2020

AbstractIn everyday life, our visual surroundings are not arranged randomly, but structured in predictable ways. Although previous studies have shown that the visual system is sensitive to such structural regularities, it remains unclear whether the presence of an intact structure in a scene also facilitates the cortical analysis of the scene’s categorical content. To address this question, we conducted an EEG experiment during which participants viewed natural scene images that were either “intact” (with their quadrants arranged in typical positions) or “jumbled” (with their quadrants arranged into atypical positions). We then used multivariate pattern analysis to decode the scenes’ category from the EEG signals (e.g., whether the participant had seen a church or a supermarket). The category of intact scenes could be decoded rapidly within the first 100ms of visual processing. Critically, within 200ms of processing category decoding was more pronounced for the intact scenes compared to the jumbled scenes, suggesting that the presence of real-world structure facilitates the extraction of scene category information. No such effect was found when the scenes were presented upside-down, indicating that the facilitation of neural category information is indeed linked to a scene’s adherence to typical real-world structure, rather than to differences in visual features between intact and jumbled scenes. Our results demonstrate that early stages of categorical analysis in the visual system exhibit tuning to the structure of the world that may facilitate the rapid extraction of behaviorally relevant information from rich natural environments.
Topoisomerase III-ß is required for efficient replication of positive-sense RNA viruses
Authors: Prasanth K. Reddisiva , Hirano Minato , Fagg W. Samuel , McAnarney Eileen T. , Shan Chao , Xie Xuping , Hage Adam , Pietzsch Colette A. , Bukreyev Alexander , Rajsbaum Ricardo , Shi Pei-Yong , Bedford Mark T. , Bradrick Shelton S. , Menachery Vineet , Garcia-Blanco Mariano A. 
Published: March 2020

AbstractBased on genome-scale loss-of-function screens we discovered that Topoisomerase III-ß (TOP3B), a human topoisomerase that acts on DNA and RNA, is required for yellow fever virus and dengue virus-2 replication. Remarkably, we found that TOP3B is required for efficient replication of all positive-sense-single stranded RNA viruses tested, including SARS-CoV-2. While there are no drugs that specifically inhibit this topoisomerase, we posit that TOP3B is an attractive anti-viral target.
Lactate dehydrogenase, a Risk Factor of Severe COVID-19 Patients: A Retrospective and Observational Study
Authors: Han Yi , Zhang Haidong , Mu Sucheng , Wei Wei , Jin Chaoyuan , Xue Yuan , Tong Chaoyang , Zha Yunfei , Song Zhenju , Gu Guorong 
Published: March 2020

AbstractBACKGROUNDThe World Health Organization (WHO) has recently declared coronavirus disease 2019 (COVID-19) a public health emergency of global concern. Updated analysis of cases might help identify the characteristic and risk factors of the illness severity.METHODSWe extracted data regarding 47 patients with confirmed COVID-19 from Renmin Hospital of Wuhan University between February 1 and February 18, 2020. The degree of severity of COVID-19 patients (severe vs. non-severe) was defined at the time of admission according to American Thoracic Society (ATS) guidelines for community-acquired pneumonia (CAP).RESULTSThe median age was 64.91 years, 26 cases (55.31%) were male of which, and 70.83% were severe cases. Severe patients had higher APACHE II (9.92 vs 4.74) and SOFA (3.0 vs 1.0) scores on admission, as well as the higher PSI (86.13 vs 61.39), Curb-65 (1.14 vs 0.48) and CT semiquantitative scores (5.0 vs 2.0) when compared with non-severe patients. Among all univariable parameters, APACHE II, SOFA, lymphocytes, CRP, LDH, AST, cTnI, BNP, et al were significantly independent risk factors of COVID-19 severity. Among which, LDH was most positively related both with APACHE II (R = 0.682) and SOFA (R = 0.790) scores, as well as PSI (R = 0.465) and CT (R = 0.837) scores. To assess the diagnostic value of these selected parameters, LDH (0.9727) had maximum sensitivity (100.00%) and specificity (86.67%), with the cutoff value of 283. As a protective factor, lymphocyte counts less than 1.045 ⨯ 109 /L showed a good accuracy for identification of severe patients with AUC = 0.9845 (95%CI 0.959-1.01), the maximum specificity (91.30%) and sensitivity (95.24%). In addition, LDH was positively correlated with CRP, AST, BNP and cTnI, while negatively correlated with lymphocyte cells and its subsets, including CD3+, CD4+ and CD8+ T cells (P < 0.01).CONCLUSIONSThis study showed that LDH could be identified as a powerful predictive factor for early recognition of lung injury and severe COVID-19 cases. And importantly, lymphocyte counts, especially CD3+, CD4+, and CD8+ T cells in the peripheral blood of COVID-19 patients, which was relevant with serum LDH, were also dynamically correlated with the severity of the disease.FUNDINGKey Project of Shanghai Municipal Health Bureau (2016ZB0202)
Anaesthetic managment and clinical outcomes of parturients with COVID-19: a multicentre, retrospective, propensity score matched cohort study
Authors: Zhang Yuan , Chen Rong , Wang Jie , Gong Yuan , Zhou Qin , Cheng Hui-hui , Xia Zhong-yuan , Chen Xiangdong , Meng Qing-tao , Ma Daqing 
Published: March 2020

AbstractObjectiveTo analyse the clinical features of COVID-19 parturients, and to compare anaesthetic regimen and clinical outcomes in parturients with or without COVID-19 undergoing cesarean delivery.MethodData were extracted from the electronic medical record of 3 medical institutions in Hubei Province, China, from June 1, 2019 to March 20, 2020 according to inclusion and exclusion criteria. After propensity score matching with demographics, the clinical and laboratory characteristics of parturients with or without COVID-19 were analysed. The anaesthetic regimen and clinical outcomes of themselves and their infants were compared in these two groups of parturients.ResultsA total of 1,588 patients without SARS-CoV-2 infection undergoing cesarean delivery were retrospectively included. After achieving a balanced cohort through propensity score matching, 89 patients (COVID-19 group), who were diagnosed with COVID-19 by SARS-CoV-2 nucleic acid test and CT scan matched with 173 patients without COVID-19 (Control group). The SARS-CoV-2 infected parturients in the early stages of COVID-19 outbreak was much more than during the later stage. The main clinical characteristics of parturients with COVID-19 were fever (34.8%), cough (33.7%), an increased plasma CRP (52.8%) and a decreased lymphocyte counting (33.7%). A high rate of emergency and a high incidence of anaesthesia-related complications, such as pharyngalgia, multiple puncture, intraoperative hypotension, nausea, vomiting, vertigo and chills in the COVID-19 parturients. In addition, the parturients with COVID-19 had a long duration of operation and hospital stay, and an increased intraoperative oxytocin utilization and postoperative oxygen therapy. The newborns from the SARS-CoV-2 infected mothers, who received general anaesthesia, had a high risk of Apgar score ≤ 8 at 1 and 5 minutes after delivery and a higher rate of neonatal intensive care unit (NICU) admission.ConclusionsAnaesthesia-related complications occur more frequently in the COVID-19 parturients and their newborns have a high risk of distress.
Mechanical Ventilator Milano (MVM): A Novel Mechanical Ventilator Designed for Mass Scale Production in Response to the COVID-19 Pandemics
Authors: Galbiati C. , Bonivento W. , Caravati M. , De Cecco S. , Dinon T. , Fiorillo G. , Franco D. , Gabriele F. , Kendziora C. L. , Kochanek I. , Ianni An. , McDonald A. , Tosatti L. Molinari , Malosio M. , Minuzzo D. , Pordes S. H. , Prini A. , Razeto A. , Razeti M. , Rescigno M. , Sablone D. , Scapparone E. , Testera G. , Tartaglia R. , Wang H. , Zardoni A. 
Published: March 2020

We present here the design of the Mechanical Ventilator Milano (MVM), a novel mechanical ventilator designed for mass scale production in response to the COVID-19 pandemics, to compensate for the dramatic shortage of such ventilators in many countries. This ventilator is an electro-mechanical equivalent of the old, reliable Manley Ventilator. Our design is optimized to permit large sale production in short time and at a limited cost, relying on off-the-shelf components, readily available worldwide from hardware suppliers. Operation of the MVM requires only a source of compressed oxygen (or compressed medical air) and electrical power. The MVM control and monitoring unit can be connected and networked via WiFi so that no additional electrical connections are necessary other than the connection to the electrical power.At this stage the MVM is not a certified medical device. Construction of the first prototypes is starting with a team of engineers, scientists and computing experts. The purpose of this paper is to disseminate the conceptual design of the MVM broadly and to solicit feed-back from the scientific and medical community to speed the process of review, improvement and possible implementation.
A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts
Authors: Yeo Yao-Yu , Yeo Yao-Rui , Yeo Wan-Jin 
Published: March 2020

AbstractThe ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2000 and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.Author SummaryCoronavirus disease 2019 (COVID-19) has escalated into a global pandemic and has recently emerged in the US. While some countries have managed to contain COVID-19 efficiently, other countries previously thought to have been well-prepared for outbreaks due to higher living standards and healthcare quality have witnessed an unexpected number of cases. It is currently unclear how the US can cope with the COVID-19 pandemic, especially after mishaps during the initial stages. Our study combines conditions unique to the US and transmission dynamics in regions affected most by COVID-19 to produce a quasi-worse-case scenario of COVID-19 in the US and shows the importance of healthcare preparedness during pandemics.
Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County
Authors: Yadlowsky Steve , Shah Nigam , Steinhardt Jacob 
Published: March 2020

SummaryTo reliably estimate the demand on regional health systems and perform public health planning, it is necessary to have a good estimate of the prevalence of infection with SARS-CoV-2 (the virus that causes COVID-19) in the population. In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified.Our goal is to estimate the prevalence of SARS-CoV-2 infections, i.e. the true number of people currently infected with the virus, divided by the total population size.Our analysis suggests that as of March 17, 2020, there are 6,500 infections (0.34% of the population) of SARS-CoV-2 in Santa Clara County. Based on adjusting the parameters of our model to be optimistic (respectively pessimistic), the number of infections would be 1,400 (resp. 26,000), corresponding to a prevalence of 0.08% (resp. 1.36%). If the shelter-in-place led to R0 < 1, we would expect the number of infections to remain about constant for the next few weeks. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later.
TERL: Classification of Transposable Elements by Convolutional Neural Networks
Authors: da Cruz Murilo Horacio Pereira , Domingues Douglas Silva , Saito Priscila Tiemi Maeda , Paschoal Alexandre Rossi , Bugatti Pedro Henrique 
Published: March 2020

AbstractTransposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. They are capable of transpose and generate multiple copies of themselves throughout genomes. These sequences can produce a variety of effects on organisms, such as regulation of gene expression. There are several types of these elements, which are classified in a hierarchical way into classes, subclasses, orders and superfamilies. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed information about these sequences. Most methods that classify TE sequences use handcrafted features such as k-mers and homology based search, which could be inefficient for classifying non-homologous sequences. Here we propose a pipeline, transposable elements representation learner (TERL), that use four preprocessing steps, a transformation of one-dimensional nucleic acid sequences into two-dimensional space data (i.e., image-like data of the sequences) and apply it to deep convolutional neural networks (CNNs). CNN is used to classify TE sequences because it is a very flexible classification method, given it can be easily retrained to classify different categories and any other DNA sequences. This classification method tries to learn the best representation of the input data to correctly classify it. CNNs can also be accelerated via GPUs to provide fast results. We have conducted six experiments to test the performance of TERL against other methods. Our approach obtained macro mean accuracies and F1-score of 96.4% and 85.8% for the superfamily sequences from RepBase and 95.7% and 91.5% for the order sequences from RepBase respectively. We have also obtained macro mean accuracies and F1-score of 95.0% and 70.6% for sequences from seven databases into superfamily level and 89.3% and 73.9% for the order level respectively. We surpassed accuracy, recall and specificity obtained by other methods on the experiment with the classification of order level sequences from seven databases and surpassed by far the time elapsed of any other method for all experiments. We also show a way to preprocess sequences and prepare train and test sets. Therefore, TERL can learn how to predict any hierarchical level of the TEs classification system, is on average 162 times and four orders of magnitude faster than TEclass and PASTEC respectively and on a real-world scenario obtained better accuracy, recall, and specificity than the other methods.
Distinct functions of tissue-resident and circulating memory Th2 cells in allergic airway disease
Authors: Rahimi Rod A. , Nepal Keshav , Cetinbas Murat , Sadreyev Ruslan I. , Luster Andrew D. 
Published: March 2020

ABSTRACTMemory CD4+T helper type 2 (Th2) cells are critical in driving allergic asthma pathogenesis, yet the mechanisms whereby tissue-resident memory Th2 cells (Th2 Trm) and circulating memory Th2 cells collaboratein vivoremain unclear. Here, using a house dust mite (HDM) model of allergic asthma and parabiosis, we demonstrate that Th2 Trm and circulating memory Th2 cells perform non-redundant functionsin vivo. Upon HDM re-challenge, circulating memory Th2 cells trafficked into the lung parenchyma and ignited perivascular inflammation to promote eosinophil and CD4+T cell recruitment. In contrast, Th2 Trm proliferated near airways and promoted mucus metaplasia, airway hyper-responsiveness, and airway eosinophil activation. Transcriptional analysis revealed that Th2 Trm and circulating memory Th2 cells share a core Th2 gene signature, but also exhibit distinct transcriptional profiles. Specifically, Th2 Trm express a tissue adaptation signature, including genes involved in regulating and interacting with extracellular matrix. Our findings demonstrate that Th2 Trm and circulating memory Th2 cells are functionally and transcriptionally distinct subsets with unique roles in promoting allergic airway disease.SUMMARYHow memory Th2 cell subsets orchestrate allergic airway inflammation remains unclear. Rahimiet al. use a murine model of allergic asthma and parabiosis to demonstrate that tissue-resident and circulating memory Th2 cells are functionally distinct subsets with unique roles in promoting allergic airway disease.
Dityrosine cross-link trapping of amyloid-β intermediates reveals that self-assembly is required for Aβ-induced cytotoxicity
Authors: Maina Mahmoud B. , Mengham Kurtis , Burra Gunasekhar K. , Al-Hilaly Youssra A. , Serpell Louise C. 
Published: March 2020

AbstractMultiple chemical reactions, such as the production of reactive oxygen species (ROS) can lead to dityrosine (DiY) formation via the cross-linking of closely spaced tyrosine residues and this can serve as a marker for aging. Amyloid-β (Aβ) has been found to be DiY cross-linked in the brains of AD patients.In vitro,Aβ forms DiY cross-links via metal-catalysed oxidation (Cu2+and H202) (MCO) leading to the formation of fibrils that are resistant to formic acid denaturation. However, copper is well known to influence and enhance self-assembly. Here, to investigate the interplay between self-assembly and DiY cross-linking we have utilised a non-assembly competent variant of Aβ (vAβ). MCO and UV oxidation experiments using vAβ and wild-type Aβ, revealed that DiY cross-linking stabilises, but does not induce or promote Aβ assembly. Cu2+alone, without H202, facilitates the formation and DiY cross-linking of wild-type Aβ into long-lived oligomers. Our work reveals DiY formation halts further Aβ self-assembly. DiY cross-linked Aβ is non-toxic to neuroblastoma cells at all stages of self-assembly in contrast to oligomeric non-cross-linked Aβ. These findings point to a mechanism of toxicity that necessitates continuing self-assembly of the Aβ peptide, whereby trapped DiY Aβ assemblies and assembly incompetent variant Aβ are unable to result in cell death.
PprA interacts with replication proteins and affects their physicochemical properties required for replication initiation inDeinococcus radiodurans
Authors: Maurya Ganesh K. , Misra Hari S. 
Published: March 2020

AbstractThe deletion mutant ofpprA, a gene encoding pleiotropic functions in radioresistant bacteriumDeinococcus radiodurans, showed an increased genomic content and ploidy in chromosome I and chromosome II. We identifiedoriCin chromosome I(oriCI) and demonstrated the sequence specific interaction of deinococcal DnaA (drDnaA) withoriCI. drDnaA and drDnaB showed ATPase activity while drDnaB catalyzed 5′→3′ dsDNA helicase activity. These proteins showed both homotypic and heterotypic interactions. The roles of C-terminal domain of drDnaA inoriCIbinding and its stimulation of ATPase activity were demonstrated. Notably, PprA showed ~2 times higher affinity to drDnaA as compared to drDnaB and attenuated both homotypic and heterotypic interactions of these proteins. Interestingly, the ATPase activity of drDnaA but not drDnaB was inhibited in presence of PprA. These results suggested that PprA influences the physicochemical properties of drDnaA and drDnaB that are required for initiation of DNA replication atoriCIsite in this bacterium.
NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data
Authors: Lu Zitong , Ku Yixuan 
Published: March 2020

AbstractIn studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities, and even bridge data from different species. However, previous toolboxes have been made to fit for specific datasets. Here, we develop a novel and easy-to-use toolbox based on Python named NeuroRA for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g. EEG, MEG, fNIRS, ECoG, sEEG, neuroelectrophysiology, fMRI), behavioral data, and computer simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. By using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity between different conditions, but also conduct a representational analysis among different RDMs to achieve a cross-modal comparison. In addition, users can calculate neural pattern similarity, spatiotemporal pattern similarity (STPS) and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.
A Novel Method for Calculating Beta Band Burst Durations in Parkinson’s Disease Using a Physiological Baseline
Authors: Anderson RW , Neuville RS , Kehnemouyi YM , Anidi CM , Petrucci MN , Parker JE , Velisar A , Bronte-Stewart H 
Published: March 2020

ABSTRACTBackgroundPathological bursts of neural activity in Parkinson’s disease present as exaggerated subthalamic neuronal oscillations in the 8-30 Hz frequency range and are related to motor impairment.New MethodThis study introduces a novel method for determining burst dynamics using a baseline that matches physiological 1/f spectrum activity. We used resting state local field potentials from people with Parkinson’s disease and a simulated 1/f signal to measure beta burst durations, to demonstrate how tuning parameters (i.e., bandwidth and center frequency) affect burst durations, to compare this with high power threshold methods, and to study the effect of increasing neurostimulation intensities on burst duration.ResultsBurst durations calculated using the Anderson method captured the longest and broadest distribution of burst durations in a pathological beta band compared to previous methods. Mean beta band burst durations were significantly shorter on compared to off neurostimulation (p = 0.011), and their distribution was shifted towards that of the physiological 1/f spectrum during increasing intensities of stimulation.Comparison with Existing MethodExisting methods of measuring local field potential power either lack temporal specificity to detect bursts (power spectral density diagrams and spectrograms) or include only higher power bursts and portions of the neural signal.ConclusionsWe suggest that this novel method is well suited to quantify the full range of fluctuations in beta band neural activity in the Parkinsonian brain. This method may reveal more relevant feedback biomarkers than averaged beta band power for future closed loop algorithms.HighlightsA novel method for measuring variability in subthalamic local field potential oscillations in Parkinson’s disease using a physiological baseline of power.Modeling normal brain activity using a physiological 1/f spectrum.Burst durations depend on choice of bandwidth and center frequency.Defining an inert frequency band whose mean burst duration overlap the physiological 1/f spectrum, from which the baseline was determined.Burst durations progressively shortened during increasing intensities of deep brain stimulation.