bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tumor-specific EphA2 receptor tyrosine kinase inhibits anti-tumor immunity by recruiting suppressive myeloid populations in NSCLC
Authors: Shiuan Eileen , Song Wenqiang , Wang Shan , Brantley-Sieders Dana M. , Chen Jin 
Published: May 2020

AbstractGiven the success of both targeted and immunotherapies against cancer, there is increasing utility for identifying targeted agents that also promote anti-tumor immunity. EphA2 is a receptor tyrosine kinase that contributes to tumor growth and metastasis and has been identified as a viable target for many solid cancers. Investigating EphA2’s impact on the host immune system may advance our understanding of tumor immune evasion and the consequences of targeting EphA2 on the tumor microenvironment. Here, we examine how tumor-specific EphA2 affects the activation and infiltration of immune cell populations and the cytokine and chemokine milieu in non-small cell lung cancer (NSCLC). Effects of EphA2 overexpression in murine NSCLC cells were evaluated in bothin vitrocell viability assays andin vivotumor models. Tumor immune infiltrate was assessed by flow cytometry. Cytokine and chemokine expression was evaluated using NanoString technology and qRT-PCR. Although EphA2 overexpression in NSCLC cells did not display proliferative advantagein vitro, it conferred a growth advantagein vivo. Analysis of lung tumor infiltrate revealed decreased natural killer and T cells in the EphA2-overexpressing tumors, as well as increased myeloid populations, including tumor-associated macrophages (TAMs). T cell activation, particularly in CD8 T cells, was decreased, while PD-1 expression was increased. These changes were accompanied by increased monocyte-attracting chemokines, specifically CCL2, CCL7, CCL8, and CCL12, and immunosuppressive proteins TGF-β and arginase 1. Our studies suggest EphA2 on tumor cells recruits monocytes and promotes their differentiation into TAMs that likely inhibit activation and infiltration of cytotoxic lymphocytes, promoting tumor immune escape. Further studies are needed to determine the molecular mechanisms by which EphA2 affects the recruitment of these cell types and to test the function of these myeloid cells.
Using EEG Microstates to Examine Post-Encoding Quiet Rest and Subsequent Word-Pair Memory
Authors: Poskanzer Craig , Denis Dan , Herrick Ashley , Stickgold Robert 
Published: May 2020

AbstractEvidence suggests that the brain preferentially consolidates memories during “offline” periods, in which an individual is not performing a task and their attention is otherwise undirected, including spans of quiet, resting wakefulness. Moreover, research has demonstrated that factors such as the initial encoding strength of information influence which memories receive the greatest benefit. Recent studies have begun to investigate these periods of post-learning quiet rest using EEG microstate analysis to observe the electrical dynamics of the brain during these stretches of memory consolidation, specifically finding an increase in the amount of the canonical microstate D during a post-encoding rest period. Here, we implement an exploratory analysis to probe the activity of EEG microstates during a post-encoding session of quiet rest in order to scrutinize the impact of learning on microstate dynamics and to further understand the role these microstates play in the consolidation of memories. We examined 54 subjects (41 female) as they completed a word-pair memory task designed to use repetition to vary the initial encoding strength of the word-pair memories. In this study, we were able to replicate previous research in which there was a significant increase (p < .05) in the amount of microstate D (often associated with the dorsal attention network) during post-encoding rest. This change was accompanied by a significant decrease (p < .05) in the amount of microstate C (often associated with the default mode network). We also found preliminary evidence indicating a positive relationship between the amount of microstate D and improved memory for weakly encoded memories, which merits further exploration.
Structure of the unique tetrameric STENOFOLIA homeodomain bound with DNA
Authors: Pathak Prabhat Kumar , Zhang Fei , Peng Shuxia , Niu Lifang , Chaturvedi Juhi , Tadege Million , Deng Junpeng 
Published: May 2020

AbstractHomeobox transcription factors are key regulators of morphogenesis and development in both animals and plants1. In plants, the WUSCHEL-related homeobox (WOX) family transcription factors function as central organizers of several developmental programs from embryo patterning to meristematic stem cell maintenance through transcriptional activation and repression2-4. The structure of WOX Homeodomain (HD) and the molecular mechanism of its interaction with DNA are unknown. Here, we report the 2.1 Å crystal structure of the STENOFOLIA (STF) HD fromMedicago truncatulain complex with DNA. STF binds DNA as a novel cooperative tetramer, enclosing nearly entire bound DNA surface. The STF tetramer is partially stabilized by docking of the C-terminal tail from one protomer onto a conserved hydrophobic surface on the head of another in a head-to-tail manner. Helix α3 not only serves a canonical role as a base reader in the major groove, but also provides extensive binding to DNA in the minor groove. Our structural and functional data reveal that STF specifically targets ‘TGA’ sequence and the cooperative tetrameric binding with DNA is key to transcriptional repression in plants. Our data reveal an unprecedented HD:DNA recognition mechanism, representing the first plant HD structure from WOX family of transcription factors.
The shapes of wine and table grape leaves: an ampelometric study inspired by the methods of Pierre Galet
Authors: Chitwood Daniel H. 
Published: May 2020

ABSTRACTThe shapes of grapevine leaves have been critical to correctly identify economically important varieties throughout history. The correspondence of homologous features in nearly all grapevine species and varieties has enabled advanced morphometric approaches to mathematically classify leaf shape. These approaches either model leaves through the measurement of numerous vein lengths and angles or measure a finite number of corresponding landmarks and use Procrustean approaches to superimpose points and perform statistical analyses. Hand illustrations, too, play an important role in grapevine identification, as details omitted using the above methods can be visualized. Here, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations. Using these points, averaged leaf shapes for 60 varieties of wine and table grapes are calculated that preserve features. A pairwise Procrustes distance matrix of the overall morphological similarity of each variety to the other classifies leaves into two main groups—deeply lobed and more entire—that correspond to the measurements of sinus depth by Pierre Galet. Using the system of Galet, pseudo-landmarks are converted into relative distance and angle measurements. Both Galet-inspired and Procrustean methods allow increased accuracy in predicting variety compared to a finite number of landmarks. Using Procrustean pseudo-landmarks captures grapevine leaf shape at the same level of detail as drawings and provides a quantitative method to arrive at mean leaf shapes representing varieties that can be used within a predictive statistical framework.
Causal inference explains the stimulus-level relationship between the McGurk Effect and auditory speech perception
Authors: Magnotti John F. , Dzeda Kristen B. , Wegner-Clemens Kira , Beauchamp Michael S. 
Published: May 2020

AbstractThe McGurk effect is widely used as a measure of multisensory integration during speech perception. Two observations have raised questions about the relationship between the effect and everyday speech perception. First, there is high variability in the strength of the McGurk effect across different stimuli and observers. Second, there is low correlation across observers between perception of the McGurk effect and measures of everyday speech perception, such as the ability to understand noisy audiovisual speech. Using the framework of the causal inference of multisensory speech (CIMS) model, we explored the relationship between the McGurk effect, syllable perception, and sentence perception in seven experiments with a total of 296 different participants. Perceptual reports revealed a relationship between the efficacy of different McGurk stimuli created from the same talker and perception of the auditory component of the McGurk stimuli presented in isolation, either with or without added noise. The CIMS model explained this high stimulus-level correlation using the principles of noisy sensory encoding followed by optimal cue combination within a representational space that was identical for McGurk and everyday speech. In other experiments, CIMS successfully modeled low observer-level correlation between McGurk and everyday speech. Variability in noisy speech perception was modeled using individual differences in noisy sensory encoding, while variability in McGurk perception involved additional differences in causal inference. Participants with all combinations of high and low sensory encoding noise and high and low causal inference disparity thresholds were identified. Perception of the McGurk effect and everyday speech can be explained by a common theoretical framework that includes causal inference.
Developmentally Delayed Epigenetic Reprogramming Underlying the Pathogenesis of Preeclampsia
Authors: He Wei , Wei Yuan , Gong Xiaoli , Chang Luyuan , Jin Wan , Liu Ke , Wang Xinghuan , Xiao Yu , Zhang Wenjing , Chen Qiong , Wu Kai , Liang Lili , Liu Jia , Chen Yawen , Guo Huanhuan , Chen Wenhao , Yang Jiexia , Qi Yiming , Dong Wei , Fu Meng , Li Xiaojuan , Liu Jiusi , Zhang Yi , Yin Aihua 
Published: May 2020

SummaryPreeclampsia, a life-threatening pregnancy complication characterized by hypertension and multiorgan damage, affects 2-5% of pregnancies and causes 76,000 deaths per year. Most preeclampsia associated syndromes immediately dispel after removal of placenta, indicating a casual role of placenta in the pathogenesis. Failed transformation of spiral artery due to insufficient invasion and excessive apoptosis of trophoblast suggested developmental defects in preeclampsia placenta. However, the underlying molecular mechanisms that affected placenta development in preeclampsia remained elusive. Here we show that, in preeclampsia placenta, the epigenetic landscape formed during extraembryonic tissue differentiation was disrupted: dramatic chromatin accessibility shift affected known and novel genes implicated in preeclampsia. DNA methylation defects in preeclampsia affected lineage-defining PcG-controlled loci in trophectoderm. LTR12 retrotransposons associated with VCT/SCT-specific genes were hypermethylated. Meanwhile, hundreds of PcG-regulated EVT-specific gene promoters, which otherwise undergone post-ZGA extraembryonic-tissue-specificde novomethylation, were hypomethylated and hyper-activated. Together, these epigenetic defects resulted in placenta developmental delay in preeclampsia. The defective methylation pattern could be detected in serum cfDNA, and could be used to accurately predict preeclampsia at early pregnancy weeks in independent validation cohorts. Our data suggests that the preeclampsia placenta represents a stalled state of epigenetic reprogrammingen routeof development from trophectoderm to normal placenta.
Deep learning models for COVID-19 infected area segmentation in CT images
Authors: Voulodimos Athanasios , Protopapadakis Eftychios , Katsamenis Iason , Doulamis Anastasios , Doulamis Nikolaos 
Published: May 2020

AbstractRecent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.Impact StatementFully Convolutional Neural Networks appear to be an accurate segmentation method in CT scans for COVID-19 pneumonia and could assist in the detection as a fast and cost-effective option.
Age-stratified model of the COVID-19 epidemic to analyze the impact of relaxing lockdown measures: nowcasting and forecasting for Switzerland
Authors: Balabdaoui F. , Mohr D. 
Published: May 2020

AbstractCompartmental models enable the analysis and prediction of an epidemic including the number of infected, hospitalized and deceased individuals in a population. They allow for computational case studies on non-pharmaceutical interventions thereby providing an important basis for policy makers. While research is ongoing on the transmission dynamics of the SARS-CoV-2 coronavirus, it is important to come up with epidemic models that can describe the main stages of the progression of the associated COVID-19 respiratory disease. We propose an age-stratified discrete compartment model as an alternative to differential equation based S-I-R type of models. The model captures the highly age-dependent progression of COVID-19 and is able to describe the day-by-day advancement of an infected individual in a modern health care system. The fully-identified model for Switzerland not only predicts the overall histories of the number of infected, hospitalized and deceased, but also the corresponding age-distributions. The model-based analysis of the outbreak reveals an average infection fatality ratio of 0.4% with a pronounced maximum of 9.5% for those aged ≥80 years. The predictions for different scenarios of relaxing the soft lockdown indicate a low risk of overloading the hospitals through a second wave of infections. However, there is a hidden risk of a significant increase in the total fatalities (by up to 200%) in case schools reopen with insufficient containment measures in place.
US Primary Care in 2029: A Delphi Survey on the Impact of Machine Learning
Authors: Blease Charlotte , Kharko Anna , Locher Cosima , DesRoches Catherine M. , Mandl Kenneth D. 
Published: May 2020

AbstractObjectiveTo solicit leading health informaticians’ predictions about the impact of AI/ML on primary care in the US in 2029.DesignA three-round online modified Delphi poll.ParticipantsTwenty-nine leading health informaticians.MethodsIn September 2019, health informatics experts were selected by the research team, and invited to participate the Delphi poll. Participation in each round was anonymous, and panelists were given between 4-8 weeks to respond to each round. In Round 1 open-ended questions solicited forecasts on the impact of AI/ML on: (1) patient care, (2) access to care, (3) the primary care workforce, (4) technological breakthroughs, and (5) the long-future for primary care physicians. Responses were coded to produce itemized statements. In Round 2, participants were invited to rate their agreement with each item along 7-point Likert scales. Responses were analyzed for consensus which was set at a predetermined interquartile range of ≤ 1. In Round 3 items that did not reach consensus were redistributed.ResultsA total of 16 experts participated in Round 1 (16/29, 55%). Of these experts 13/16 (response rate, 81%), and 13/13 (response rate, 100%), responded to Rounds 2 and 3, respectively. As a result of developments in AI/ML by 2029 experts anticipated workplace changes including incursions into the disintermediation of physician expertise, and increased AI/ML training requirements for medical students. Informaticians also forecast that by 2029 AI/ML will increase diagnostic accuracy especially among those with limited access to experts, minorities and those with rare diseases. Expert panelists also predicted that AI/ML-tools would improve access to expert doctor knowledge.ConclusionsThis study presents timely information on informaticians’ consensus views about the impact of AI/ML on US primary care in 2029. Preparation for the near-future of primary care will require improved levels of digital health literacy among patients and physicians.
Negative nasopharyngeal SARS-CoV-2 PCR conversion in Response to different therapeutic interventions
Authors: Shabrawishi Mohammed Hassan , Naser Abdallah Y , Alwafi Hassan , Aldobyany Ahmad Mansoor , Touman Abdelfattah Ahmed 
Published: May 2020

ABSTRACTBACKGROUNDDespite lack of convincing evidence of the efficacy of hydroxychloroquine, it has been suggested to be used for the treatment of SARS-CoV-2 to accelerate the negative virus conversion. We aimed to explore the association between negative nasopharyngeal SARS-CoV-2 PCR clearance and different therapeutic interventions.METHODOLOGYThis was a retrospective cohort study of 93 patients who were admitted to medical ward with a PCR confirmed diagnosis of COVID-19 and met the inclusion criteria in a tertiary hospital in Mecca, Saudi Arabia. There were three interventional subgroups (group A (n=45): who received antimalarial drug only classified as (A1), combined with azithromycin (A2) or combined with antiviral drugs (A3)), and one supportive care group (group B) (n=48). The primary and secondary endpoints of the study were achieving negative SARS-CoV-2 nasopharyngeal PCR sample within five days or less from the start of the intervention and 12 days or less from the diagnose, respectively.RESULTSThe mean age of the patients was 43.9 years (SD:15.9). A median time of 3.00 days (IQR:2.00-6.50) needed from the time of starting the intervention/supportive care to the first negative PCR sample. There was no statistically significant difference neither between the percentage of patients in the interventional group and the supportive care group who achieved the primary or the secondary endpoint, nor in the median time needed to achieve the first negative PCR sample (p>0.05).CONCLUSIONPrescribing antimalarial medications was not shown to shorten the disease course nor to accelerate the negative PCR conversion rate.
Prediction of 2019-nCov in Italy based on PSO and inversion analysis
Authors: Siyi Sun , Yangping Zheng 
Published: May 2020

AbstractNovel coronavirus (2019-nCov) has swept the world, and all of the world have been harmful. This article makes prediction and suggestions for the Italy. Up to March 11, 2020, 2019-nCov thoroughly broke out in Italy with over 10,000 confirmed cases notwithstanding the gradually block of the country since March 9, 2020. Estimation of possible infection population and prospective suggestion of handling spread based on exist data are of crucial importance. Considering of the biology parameters obtained based on Chinese clinical data in Wuhan, other scholars’ work and real spread feature of 2019-nCov in Italy, we built a more applicable model called SEIJR with log-normal distributed time delay to forecast the trend of spreading. Adopting Particle Swarm Optimization (PSO), we estimated the early period average spreading velocity (α0) and conducted inversion analysis of time point (T0) when the virus first hit the Italy. Based on fixed α0 and T0, we then obtained the average spreading velocity α1 after the lock by PSO. For the aim of offering expeditious advice, we generated the prediction trends with different α which we considered would be helpful in addressing the infection. Not only solved the complex, nondifferentiable equation of epidemic model, our research also performs well in inversion analysis based on PSO which conveys informative outcomes for further discussion on precatious action. To conclude, the first day of spread is around February 1, 2020 with the early period average spreading velocity α0=0.330 which is higher than most cities in China except Wuhan. After locking the country and attaching great attention to public precaution, the α1 sharply descended to 0.278, indicting the effectiveness of these measures. Furthermore, in order to cope the disease before mid-April, take actions to control the under 0.25 is necessary. Code can be freely downloaded from https://github.com/Summerwork/2019-nCov-Prediction.
Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity
Authors: Rajagopal Veera M. , Ganna Andrea , Coleman Jonathan R. I. , Allegrini Andrea G. , Voloudakis Georgios , Grove Jakob , Als Thomas D. , Horsdal Henriette T. , Petersen Liselotte , Appadurai Vivek , Schork Andrew , Buil Alfonso , Bulik Cynthia M. , Bybjerg-Grauholm Jonas , Bækvad-Hansen Marie , Hougaard David M. , Mors Ole , Nordentoft Merete , Werge Thomas ,   iPSYCH-Broad Consortium, Mortensen Preben Bo , Breen Gerome , Roussos Panos , Plomin Robert , Agerbo Esben , Børglum Anders D. , Demontis Ditte 
Published: May 2020

AbstractIndividuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language. Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of six major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls. We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls. The results overall suggest that risk for psychiatric disorders, language ability and creativity might have overlapping genetic roots.