The impact of calcitriol and estradiol on the SARS-CoV-2 biological activity: a molecular modeling approach

The novel coronavirus disease (COVID-19) is currently a big concern around the world. Recent reports show that the disease severity and mortality of COVID-19 infected patients may vary from gender to gender with a very high risk of death for seniors. In addition, some steroid structures have been reported to affect coronavirus, SARS-CoV-2, function and activity. The entry of SARS-CoV-2 into host cells depends on the binding of coronavirus spike protein to angiotensin converting enzyme-2 (ACE2). Viral main protease is essential for the replication of SARS-CoV-2. It was hypothesized that steroid molecules (e.g., estradiol, progesterone, testosterone, dexamethasone, hydrocortisone, prednisone and calcitriol) could occupy the active site of the protease and could alter the interaction of spike protein with ACE2. Computational data showed that estradiol interacted more strongly with the main protease active site. In the presence of calcitriol, the binding energy of the spike protein to ACE2 was increased, and transferring Apo to Locked S conformer of spike trimer was facilitated. Together, the interaction between spike protein and ACE2 can be disrupted by calcitriol. Potential use of estradiol and calcitriol to reduce virus invasion and replication needs clinical investigation.

Detection of Road Traffic Anomalies Based on Computational Data Science

The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach

Background The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. Results We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. Conclusion We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

THEORETICAL AND COMPUTER MODELS OF 3-DIMENSIONAL GRAIN BOUNDARY MIGRATION WITH MOBILE PARTICLES

In this work, the migration of a three-dimensional (3D) spherical crystal in the presence of mobile particles using a Monte Carlo algorithm was studied. Different concentrations of particles (<i>f</i>) and different particle mobility (<i>M_p</i>) were used. It was found that the grain size reaches a critical radius (<i>R_c</i>) which depends exclusively on <i>f</i>. This dependence can be written as: <i>R_c</i>∝<i>f</i>^1/3. The dynamic equation of grain size evolution and its analytical solution were also found. The analytical solution proposed fits successfully the simulation results. The particle fraction in the grain boundary was also found analytically and it fits the computational data.

The Impact of the Surface Modification on Tin-Doped Indium Oxide Nanocomposite Properties

This review provides an analysis of the theoretical methods to study the effects of surface modification on structural properties of nanostructured indium tin oxide (ITO), mainly by organic compounds. The computational data are compared with experimental data such as X-ray diffraction (XRD), atomic force microscopy (AFM) and energy-dispersive X-ray spectroscopy (EDS) data with the focus on optoelectronic and electrocatalytic properties of the surface to investigate potential relations of these properties and applications of ITO in fields such as biosensing and electronic device fabrication. Our analysis shows that the change in optoelectronic properties of the surface is mainly due to functionalizing the surface with organic molecules and that the electrocatalytic properties vary as a function of size.

Statistical and Computational Needs for Big Data Challenges

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

A System for Measurement and Analysis of Aircraft Noise Impacts

The Metroplex Overflight Noise Analysis (MONA) project seeks to measure, analyze, and archive the ground noise generated by aircraft overflights and to provide accurate and actionable data for a variety of different purposes. On the one hand, experimental datasets collected and processed by the MONA system can serve as an openly-available database for validation and verification (V&V) of improved noise prediction methods. On the other, study conclusions derived from both the experimental and computational data can serve to inform technical discussions and options involving aircraft noise, aircraft routes, and the potential impacts of the FAA’s NextGen procedure changes on overflown communities at varying distances from the airport. Given the complex interdependencies between the noise levels perceived on the ground and the air-traffic patterns that generate the aircraft noise, a secondary goal of the MONA project is to share, through compelling visualizations, key results with broad communities of stakeholders to help generate a common understanding and reach better decisions more quickly. In this paper, we focus on the description of the MONA system architecture, its design, and its current set of capabilities. Subsequent publications will focus on the results we are obtaining though the use of the MONA system.

A comprehensive BIAPSS platform for the physicochemical featurization of phase separating proteins

The liquid-liquid phase separation (LLPS) of biological macromolecules has emerged as a foundational mechanism underlying the formation of a myriad of membraneless organelles (MLOs), such as stress granules, transcription factor condensates, and chromatin compartments. A molecular grammar of sequences, which would enable a quantitative prediction and understanding of protein phase separation from first principles is currently missing. A major challenge in the field is the sparsity of bioinformatics data and the lack of computational, data-driven tools for biophysical and statistical analysis of proteins capable of phase separation. Here we present the utility of web applications framed within a novel open-source platform for BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences, https://biapss.chem.iastate.edu/. BIAPSS combines high-throughput interactive data analytics of physicochemical and evolutionary features with a comprehensive repository of bioinformatic data for on-the-fly research of the sequence-dependent properties of proteins with known LLPS behavior. To facilitate exploration of the services and provide the interpretation guideline, we present two attention-getting case studies of FUS and hnRNPDL. This should help the LLPS community uncover the nature of interactions driving the formation of membraneless organelles.

A discussion on the new critical convergences towards the centrality of knowledge in school: the theories of the relation to knowledge of Bernard Charlot and the return of Michael Young's powerful knowledge

In the late 1960s, driven by the increasing capacity of computational data processing, statistics that linked school success with students' social backgrounds became the main argument in favor of the idea that schools -even the public ones- did little more than reproduce class inequalities and legitimize them by attributing school failure to the poor intellectual abilities of subordinate class students. Both in England and France, critical theories about education questioned the curriculum, which they saw as arbitrary and related to the interests and tastes of the privileged classes, as well as the authority of the teacher, transmitter of these contents and legitimizer of educative but especially social failure, of children from low strata. This apparent consensus is explicitly broken with the turn of the century, and authors such as Bernard Charlot in France and Michael FD Young in England, converge on pointing to knowledge as the central factor in educational work. The objective of this article is to examine the approaches of the two authors on this point, to compare both perspectives, and to propose overcoming visions of some distances that separate them. It concludes with a theoretical critique of both perspectives, an attempt of an overcoming synthesis, underlining the value of knowledge as a central factor in educational activities.