Brantleyrye6273

Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models - Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analca, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University's COVID-19 database.The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%-20%, 70%-30% and 60%-40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic.The European space-economy represents a complex system with a great internal heterogeneity, intensive socioeconomic interactions and differential growth trajectories among countries and regions. The present study aims to investigate the connectivity between spatial competitiveness and resilience in Europe and seeks to design an operational framework for concerted strategies of competitive and resilient regions. To assess the linkage between resilience and competitiveness, we have developed a new measure, viz. the Resilience and Competitiveness Index (RACI) as a function of two constituent sub-indices Resilience and Competitiveness. This approach is tested on the basis of detailed data on European regions. The empirical results from 268 EU NUTS2 regions offer a solid anchor point for the proposed operational framework for concerted development strategies of competitive and resilient regions. Our research distinguishes and proposes several systematic types of concerted regional strategies according to the performance of a region measured by Resilience and Competiveness sub-indices. A key result of the study is the design of an operational constellation for strategic regional policy evaluation, with a major added value for policy- and decision-making purposes. The use of official data from Eurostat and of standard indicators in our research assures continuity and consistency with the official Regional Competitiveness Index (RCI) classification and measurement, so that policy makers are able to compare the performance of their regions over time and to develop proper concerted strategies accordingly. The clear evidence of a connectivity between regional competitiveness and resilience may help to develop a governance approach that balances competitiveness (mainly represented by productive assets) with resilience (mainly represented by sustainability and ecological awareness) and thus to deal with the complexity in socioeconomic systems.This research presents the results of development and validation of the Cyclical Self-Regulated Learning (SRL) Simulation Model, a model of student cognitive and metacognitive experiences learning mathematics within an intelligent tutoring system (ITS). Patterned after Zimmerman and Moylan's (2009) Cyclical SRL Model, the Simulation Model depicts a feedback cycle connecting forethought, performance and self-reflection, with emotion hypothesized as a key determinant of student learning. A mathematical model was developed in steps, using data collected from students during their sessions within the ITS, developing solutions using structural equation modeling, and using these coefficients to calibrate a System Dynamics (SD) Simulation model. Results provide validation of the Cyclical SRL Model, confirming the interplay of grit, emotion, and performance in the ITS. The Simulation Model enables mathematical simulations depicting a variety of student background types and intervention styles and supporting deeper future explorations of dimensions of student learning.We describe the energetic landscape beyond the solid-state dynamic behavior of a cyclic hexapeptoid decorated with four propargyl and two methoxyethyl side chains, namely, cyclo-(Nme-Npa2)2, Nme = N-(methoxyethyl)glycine, Npa = N-(propargyl)glycine. By increasing the temperature above 40 °C, the acetonitrile solvate form 1A starts to release acetonitrile molecules and undergoes a reversible single crystal-to-single crystal transformation into crystal form 1B with a remarkable conformational change in the macrocycle two propargyl side chains move by 113° to form an unprecedented "CH-π zipper". Then, upon acetonitrile adsorption, the "CH-π zipper" opens and the crystal form 1B transforms back to 1A. By conformational energy and lattice energy calculations, we demonstrate that the dramatic side-chain movement is a peculiar feature of the solid-state assembly and is determined by a backbone conformational change that leads to stabilizing CH···OC backbone-to-backbone interactions tightening the framework upon acetonitrile release. Weak interactions as CH···OC and CH-π bonds with the guest molecules are able to reverse the transformation, providing the energy contribution to unzip the framework. We believe that the underlined mechanism could be used as a model system to understand how external stimuli (as temperature, humidity, or volatile compounds) could determine conformational changes in the solid state.Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. read more Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.Even with the ubiquity of Sanger sequencing, automated assembly software are predominantly stand-alone software packages for desktop/laptop use with very few online equivalents, thus geospatially constraining sequence analysis and assembly. With increased data output worldwide, there is also a need for automated quality checks and trimming prior to large assemblies, along with automated detection of mutations. Through web servers with expanded automation and functionalities, even smartphones/phablets can be used to perform complex analysis previously limited to desktops, especially if they can upload files from cloud storage. To facilitate such online accessible sequence assembly and analysis, we created Yet Another Quick Assembly, Analysis and Trimming Tool web server for the automated assembly of multiple .ab1 and .FASTQ sequencing reads de novo with automated trimming and scanning of the assembled sequences for single nucleotide polymorphisms and insertions or deletions without installation of software, allowing it to be accessed from anywhere with Internet access and with minimal dependency on other software and web tools.Force multipliers are attributes of an organization that enable the successful completion of multiple essential missions. Core facilities play a critical role in the research enterprise and can be organized as force multipliers. Conceiving of cores in this way influences their organization, funding, and research impact. To function as a force multiplier for the research enterprise, core facilities need to do more than efficiently provide services for investigators and generate revenue to recover their service costs they must be aligned with the strategic objectives of a research university. When core facilities are organized in this way, they can facilitate recruitment of faculty and trainees; serve to retain talented faculty; drive, acquire, and maintain cutting-edge research platforms; and promote interaction and collaboration across the institution. Most importantly, cores accelerate the discovery and sharing of knowledge that are the foundation of a modern research university. This idea has been systematically implemented through the Emory Integrated Core Facilities (cores.