bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Kamin Blocking Effect in Sign and Goal Trackers
Authors: Aggarwal Mayank , Wickens Jeffery R. 
Published: August 2020

AbstractThe discovery of the Kamin blocking effect suggested that surprise or prediction errors are necessary for associative learning. This suggestion led to the development of a new theoretical framework for associative learning relying on prediction error rather than just temporal contiguity between events. However, many recent studies have failed to replicate the blocking effect, questioning the central role of blocking in associative learning theory. Here, we test the expression of Kamin blocking in rats that either approach and interact with the conditioned cue (sign trackers) or approach and interact with the reward location (goal trackers) during appetitive classical conditioning. The behavioral task involved three phases: classical conditioning of a lever cue, conditioning of a compound of the lever cue plus an auditory cue, and testing response to presentation of the auditory cue in extinction. The results show that only sign trackers express the blocking effect. Thus, groups that include goal trackers are less likely to be able to replicate the blocking effect. Our findings support the idea that sign and goal tracking responses arise as a result of distinct parallel learning processes. Psychological theories of learning that incorporate these parallel learning processes and their interactions will provide a better framework for understanding the blocking effect and related associative learning phenomena.
Epidemiological characteristics of SARS-COV-2 in Myanmar
Authors: Thway Aung Minn , Teza Htun , Win Tun Tun , Htun Ye Min , Aung Moe Myint , Win Yan Naung , Tun Kyaw Myo 
Published: August 2020

AbstractCoronavirus disease (COVID-19) is an infectious disease caused by a newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In Myanmar, first COVID-19 reported cases were identified on 23rd March 2020. There were 336 reported confirmed cases, 261 recovered and 6 deaths through 13th July 2020. The study was a retrospective case series and all COVID-19 confirmed cases from 23rd March to 13th July 2020 were included. The data series of COVID-19 cases were extracted from the daily official reports of the Ministry of Health and Sports (MOHS), Myanmar and Centers for Disease Control and Prevention (CDC), Myanmar. Among 336 confirmed cases, there were 169 cases with reported transmission events. The median serial interval was 4 days (IQR 3, 2-5) with the range of 0 - 26 days. The mean of the reproduction number was 1.44 with (95% CI = 1.30-1.60) by exponential growth method and 1.32 with (95% CI = 0.98-1.73) confident interval by maximum likelihood method. This study outlined the epidemiological characteristics and epidemic parameters of COVID-19 in Myanmar. The estimation parameters in this study can be comparable with other studies and variability of these parameters can be considered when implementing disease control strategy in Myanmar.
Dynamics of task-related electrophysiological networks: a benchmarking study
Authors: Tabbal Judie , Kabbara Aya , Khalil Mohamad , Benquet Pascal , Hassan Mahmoud 
Published: August 2020

AbstractMotor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the random selection of methods and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We show that the independent component analysis (ICA)-based methods and especially those exploring high order statistics are the most efficient, in terms of spatiotemporal accuracy and subject-level analysis. We seek to help researchers in choosing objectively the appropriate methodology when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.
Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery
Authors: Cuccarese Michael F. , Earnshaw Berton A. , Heiser Katie , Fogelson Ben , Davis Chadwick T. , McLean Peter F. , Gordon Hannah B. , Skelly Kathleen-Rose , Weathersby Fiona L. , Rodic Vlad , Quigley Ian K. , Pastuzyn Elissa D. , Mendivil Brandon M. , Lazar Nathan H. , Brooks Carl A. , Carpenter Joseph , Probst Brandon L. , Jacobson Pamela , Glazier Seth W. , Ford Jes , Jensen James D. , Campbell Nicholas D. , Statnick Michael A. , Low Adeline S. , Thomas Kirk R. , Carpenter Anne E. , Hegde Sharath S. , Alfa Ronald W. , Victors Mason L. , Haque Imran S. , Chong Yolanda T. , Gibson Christopher C. 
Published: August 2020

ABSTRACTDevelopment of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.
Nonlinear delay differential equations and their application to modeling biological network motifs
Authors: Glass David S. , Jin Xiaofan , Riedel-Kruse Ingmar H. 
Published: August 2020

AbstractBiological regulatory systems, such as transcription factor or kinase networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. “Network motif” models focus on small sub-networks to provide quantitative insight into overall behavior. However, conventional network motif models often ignore time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equations (DDEs), both analytically and numerically. We find many broadly applicable results, such as the reduction in number of parameters compared to canonical descriptions via ordinary differential equations (ODE), criteria for when delays may be ignored, a complete phase space for autoregulation, explicit dependence of feedforward loops on a difference of delays, a unified framework for Hill-function logic, and conditions for oscillations and chaos. We emphasize relevance to biological function throughout our analysis, summarize key points in non-mathematical form, and conclude that explicit-delay modeling simplifies the phenomenological understanding of many biological networks and may aid in discovering new functional motifs.
Application of Social Vulnerability Index to Identify High- risk Population of Contracting COVID-19 Infection: a state-level study
Authors: Lara-Garcia Odalys Estefania , Retamales Violeta Alvarez , Suarez Oswaldo Madrid , Parajuli Priyanka , Hingle Susan , Robinson Robert 
Published: August 2020

AbstractSocial factors that determine a population’s health are known as the social determinants of health. During the past few weeks, as COVID-19 cases grew exponentially, the discrepancy among the number of cases distribution was evident.By applying the social vulnerability index and analyzing data from a total of 102 counties across the state of Illinois, we investigated which factors enhanced the risk of contracting the infection and which were related to a lower risk of infection. Our results showed that social factors such as belonging to a minority group, speaking English less than well, living in a multi-unit structure, and households with individuals of age group of 17 or younger were associated with a higher risk of infection. On the other hand, we found that factors such as living in a mobile home, individuals of age group 65 or older, low income per capita and, older than age 5 with disability were protective. We propose that communities with disproportionate health burdens can be identified by the application of these factors. Future efforts need to focus on decreasing the gap of to decrease the gap of disparity by modifying these social factors.
Tertiary Lymphoid Structure and CD8 T Cell Exclusion in Minimally Invasive Adenocarcinoma
Authors: Wang Jin , Jiang Dongbo , Zheng Xiaoqi , Li Wang , Zhao Tian , Wang Di , Yu Huansha , Sun Dongqing , Li Ziyi , Zhang Jian , Zhang Zhe , Hou Likun , Jiang Gening , Zhang Fan , Yang Kun , Zhang Peng 
Published: August 2020

AbstractLung adenocarcinoma is the leading cause of cancer death. To characterize the tumor microenvironment (TME) of early-stage lung adenocarcinoma, we performed RNA-seq profiling on 59 pairs of minimally invasive adenocarcinoma (MIA) tumors and matched adjacent normal lung tissues from Chinese patients. We observed mucin over-expression and glycosylation, and altered cytokine-cytokine interactions in MIA tumors, which also had distinct adaptive immune TME of higher CD4+ T cell infiltration, higher plasma B cell activation, and lower CD8+ T cell infiltration. The high expression of markers for B cells, activated CD4 T cells, and follicular helper T (Tfh) cells in MIA implicated the formation of tertiary lymphoid structures (TLS), which were supported by two independent single-cell RNA-seq data. Multiplex immunohistochemistry (mIHC) staining of 22 MIA tumors validated TLS formation and revealed an enrichment of follicular regulatory T cells (Tfr) in TLS follicles, which may explain the lower CD8+ T cell infiltration and attenuated anti-tumor immunity in MIA.SignificanceWe discovered a potential immunosuppressive phenotype in MIA mediated by follicular regulatory T cells (Tfr) in TLS formation, which could be a potential mechanism of CD8+ T cell exclusion. Our study demonstrated how integrating tumor transcriptome and pathology can characterize the TME and elucidate potential mechanisms of tumor immune evasion.
Waves of COVID-19 pandemic. Detection and SIR simulations
Authors: Nesteruk Igor 
Published: August 2020

AbstractBackgroundUnfortunately, the COVID-19 pandemic is still far from stabilizing. Of particular concern is the sharp increase in the number of diseases in June-July 2020. The causes and consequences of this sharp increase in the number of cases are still waiting for their researchers, but there is already an urgent need to assess the possible duration of the pandemic, the expected number of patients and deaths. The resumption of international passenger traffic needs the information for deciding which countries’ citizens are welcome guests. Correct simulation of the infectious disease dynamics needs complicated mathematical models and many efforts for unknown parameters identification. Constant changes in the pandemic conditions (in particular, the peculiarities of quarantine and its violation, situations with testing and isolation of patients) cause various epidemic waves, lead to changes in the parameter values of the mathematical models.ObjectiveIn this article, pandemic waves in Ukraine and in the world will be detected, calculated and discussed. The estimations for hidden periods, epidemic durations and final numbers of cases will be presented. The probabilities of meeting a person spreading the infection and reproduction numbers will be calculated for different countries and regions.MethodsWe propose a simple method for the epidemic waves detection based on the differentiation of the smoothed number of cases. We use the known SIR (susceptible-infected-removed) model for the dynamics of the epidemic waves. The known exact solution of the SIR differential equations and statistical approach were modified and used.ResultsThe optimal values of the SIR model parameters were identified for four waves of pandemic dynamics in Ukraine and five waves in the world. The number of cases and the number of patients spreading the infection versus time were calculated. In particular, the pandemic probably began in August 2019. If current trends continue, the end of the pandemic should be expected no earlier than in March 2021 both in Ukraine and in the world, the global number of cases will exceed 20 million. The probabilities of meeting a person spreading the infection and reproduction numbers were calculated for different countries and regions.ConclusionsThe SIR model and statistical approach to the parameter identification are helpful to make some reliable estimations of the epidemic waves. The number of persons spreading the infection versus time was calculated during all the epidemic waves. The obtained information will be useful to regulate the quarantine activities, to predict the medical and economic consequences of the pandemic and to decide which countries’ citizens are welcome guests.
Spreading of COVID-19 in Italy as the spreading of a wave packet
Authors: Feoli A. , Iannella A. L. , Benedetto E. 
Published: August 2020

AbstractWe find that the spreading of the COVID-19 pandemic in Italy can be described as the propagation of a wave packet in a dispersive medium where the effect of Lockdown is simulated by the dispersion relation of the medium. We start expanding a previous statistical analysis based on the official data provided by the Italian civil protection during 100 days, from March 2nd to June 9th. As the total number of people infected with the virus is uncertain, we have considered the trend of ICU patients and the sum of hospitalized patients and the deceased. Both the corresponding curves are well approximated by the same function depending on four free parameters. The model allows to predict the short term behavior of the pandemic and to estimate the benefits due to lockdown measures.
Correlation between daily infections and fatality rate due to Covid-19 in Germany
Authors: Mergel Dieter 
Published: August 2020

AbstractThe officially reported daily Covid-19 fatality rate is modelled with a trend line based on a nominal day-to-day reproduction rate and a cosine to take account of weekly fluctuations. Although the time trajectories of officially reported infections and fatalities are pronouncedly different, the reproduction rates obtained therefrom are similar. The long-term effective reproduction rate is around 0.835 and the administrative measures to contain the pandemic seem not to have an immediate reducing effect but well the ease of restrictions an increasing one. The fatality trajectory represented by its trend line can be projected from the number of daily infections by assuming a time lapse between symptom onset and death between 17 and 19 days and a time-dependent nominal lethality. The time trajectory of this lethality increases from 2.5% at March 16 when public life was restricted to 6% within 20 days indicating relatively more infections of vulnerable people. After stipulating face mask wearing at April 27, the nominal lethality decreases down to 1% later in summer. A detailed analysis shows that mask wearing really reduces the number of fatal infections and the officially reported daily infections in May and June are less lethal than before.
Multisystem inflammatory syndrome in children (MIS-C) temporally associated with SARS-CoV-2 infection: a scoping review of the literature
Authors: Sabbour Mohamed , El-Swaify Seif Tarek , Farrag Nourhan , Kamel Menna , Ali Sara H. , Amir Abdelrahman , Refaat Mazen A. , Dyab Menatalla A. , Nabhan Ashraf 
Published: August 2020

ABSTRACTBackgroundWith the rise of the COVID-19 pandemic, a new severe life-threatening inflammatory syndrome has been reported in some pediatric populations. Global attention was shifted towards the syndrome termed multisystem inflammatory syndrome in children (MIS-C), with new case reports flooding in.ObjectivesThe aim of this scoping review is to summarize the existing reports on MIS-C and focus on the demographics, diagnosis, clinical presentation, laboratory investigations, imaging studies, treatment, and patient outcomes.MethodsWe conducted a systemic search using LitCovid and MEDLINE electronic databases. We screened citations, titles and abstracts, then reviewed potentially relevant articles in full. After data extraction, we reported our final data under subheadings of demographics, diagnosis, clinical presentation, laboratory investigations, imaging studies, treatment, and patient outcomes.ResultsOur search strategy yielded 42 original studies reporting 674 pediatric patients fitting the case definition of MIS-C. The studies included 21 case reports, 16 case series and 5 cohort studies. The most common reported symptom of MIS-C was fever (98%). Gastrointestinal symptoms were common (N=557, 83%). Interleukin-6 (IL-6) levels were measured in 125 patients and was elevated in 94 % (N=117). Echocardiography detected coronary artery lesions in 100 patients. Prophylactic and/or therapeutic heparin was required in 34% (N=227) of patients. The most commonly administered treatment modality targeting MIS-C was intravenous immunoglobulin (IVIG) (N=490). Corticosteroids (N=347) and aspirin (N=112) were also integral parts of the treatment regimens. Biologic therapy was integrated into the treatment regimen for 116 patients. Intensive care unit (ICU) admission was alarming (N=478, 71%). 9 fatalities were recorded due to MIS-CConclusionsWe believe MIS-C bears pathophysiological resemblance to the well-known Kawasaki disease but with some key differences highlighted. Understanding those differences will aid our management plan for such patients.
Integrated intra- and intercellular signaling knowledge for multicellular omics analysis
Authors: Türei Dénes , Valdeolivas Alberto , Gul Lejla , Palacio-Escat Nicolàs , Ivanova Olga , Gábor Attila , Módos Dezső , Korcsmáros Tamás , Saez-Rodriguez Julio 
Published: August 2020

AbstractMolecular knowledge of biological processes is a cornerstone in the analysis of omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across different resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources in a single database. It covers the interactions and roles of proteins in inter- and intracellular signal transduction, as well as transcriptional and post-transcriptional regulation. We also provide a comprehensive collection of protein complexes and rich annotations on the properties of proteins, including function, localization, and role in diseases. The resource is available for human, and via homology translation for mouse and rat. The data is accessible viaOmniPath’s web service, a Cytoscape plugin, and packages in R/Bioconductor and Python, providing convenient access options for both computational and experimental scientists. Our resource provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications to study SARS-CoV-2 infection and ulcerative colitis.
ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices
Authors: de Cesare Irene , Zamora-Chimal Criseida G. , Postiglione Lorena , Khazim Mahmoud , Pedone Elisa , Shannon Barbara , Fiore Gianfranco , Perrino Giansimone , Napolitano Sara , di Bernardo Diego , Savery Nigel , Grierson Claire , di Bernardo Mario , Marucci Lucia 
Published: August 2020

ABSTRACTExtracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. While such image segmentation applications have been previously reported, there is in the literature a lack of open-source and documented code for the community. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.
Eye-specific voluntary attention can induce a shift of perceptual ocular dominance
Authors: Lyu Lili , Han Qiu , He Xin , Bao Min 
Published: August 2020

AbstractOcular dominance plasticity in adults has been extensively studied in the recent decade. An interocular imbalance of visual input, e.g. monocular deprivation, has been proved to markedly reshape ocular dominance. As visual attention can be eye-specific, dissimilar visual inputs from the two eyes during monocular deprivation inevitably lead attention to be more allocated to the monocular input that conveys relatively intact information. Does the imbalanced attention across the eyes also contribute to reshaping ocular dominance? Here, using a novel “backwards-movie” adaptation paradigm, we showed that prolonged attention to the input in one eye was sufficient to shift perceptual ocular dominance in favor of the unattended eye. Furthermore, the effect was stronger when eye-specific attention was directed to the dominant eye, possibly due to fewer disturbances from the other eye during the adaptation. Taken together, these findings suggest that top-down attention plays an important role in short-term ocular dominance plasticity.Statement of RelevanceAn important goal in neuroscience is to understand and take advantage of adaptive neural plasticity. Ocular dominance plasticity resulting from imbalanced visual input signals across the two eyes has been intensively investigated. By developing a novel “backwards-movie” paradigm in which movie images played normally were presented to one eye while movie images of the same episode but played backwards were presented to the other eye, the current study for the first time demonstrates the non-negligible contributions of selective attention in reshaping ocular dominance. These findings expand the homeostatic compensation theory of monocular deprivation by highlighting the contributions of feedback signals. Furthermore, our method could be applied in future work to explore new possibilities in treating adults with amblyopia.