Case Report: Novel SLC9A6 Splicing Variant in a Chinese Boy With Christianson Syndrome With Electrical Status Epilepticus During Sleep

We investigated the existence and potential pathogenicity of a SLC9A6 splicing variant in a Chinese boy with Christianson Syndrome (CS), which was reported for the first time in China. Trio whole-exome sequencing (WES) was performed in the proband and his parents. Multiple computer prediction tools were used to evaluate the pathogenicity of the variant, and reverse transcription-polymerase chain reaction (RT-PCR) analysis and cDNA sequencing were performed to verify the RNA splicing results. The patient presented with characteristic features of CS: global developmental delay, seizures, absent speech, truncal ataxia, microcephaly, ophthalmoplegia, smiling face and hyperkinesis with electrical status epilepticus during sleep (ESES) detected in an electroencephalogram (EEG). A SLC9A6 splicing variant was identified by WES and complete skipping of exon 10 was confirmed by RT-PCR. This resulted in altered gene function and was predicted to be pathogenic. ESES observed early in the disease course is considered to be a significant feature of CS with the SLC9A6 variant. Combined genetic analysis at both the DNA and RNA levels is necessary to confirm the pathogenicity of this variant and its role in the clinical diagnosis of CS.

Computer prediction of technological modes of rapid cone-shaped adsorption filters with automated discharge of part of heat from separation surfaces in filtering mode

In the paper a mathematical models of technological modes of filtration with automated removal of part of heat from interface surfaces (water purification from multicomponent impurities), backwashing, chemical regeneration and direct washing of rapid cone-shaped adsorption filters with chemical regeneration of piecewise homogeneous porous loads while maintaining constant velocities of the respective modes is formulated. The proposed models in the complex allow to conduct computer experiments to investigate the change in the concentrations of components of a multicomponent impurity in the filtration stream and on the surface of the loading adsorbent, retained by both physical and chemical adsorption, filtration flow temperature, filtration coefficient, active porosity and pressure along the filter height and on their basis to predict more optimal options for the use of adsorbents of each loading layer and increase the protective time of rapid cone-shaped adsorption filters with automated heat removal from the interface surfaces in filter mode.

Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing

A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.

Anti-Oxidation and Anti-Inflammatory Potency Evaluation of Ferulic Acid Derivatives Obtained through Virtual Screening

Various factors such as ultraviolet rays can cause a continuous threat to our skin, resulting in inflammation or oxidation problems. Ferulic acid (FA), with certain antioxidant and anti-inflammatory properties, is widely used in many cosmetics, even used to treat various diseases in the clinic. In this study, the FA structural skeleton was used to search for FA derivatives. Then, molecular docking, the rule of five, and Veber rules were performed to virtually screen compounds that can bind to proteins with a good drug likeness. DPPH and ABTS were used to evaluate their antioxidant potency and an MTT assay was employed to investigate the toxicities of the compounds, while Griess Reaction System and ELISA were used to judge the concentration variations of NO and different inflammatory factors (TNF-α, IL-1β, and IL-6). Western blotting featured nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein expression levels. The trend of the intracellular changes of reactive oxygen species (ROS) was detected by the DCFH-DA method and fluorescence staining. As a result, we found that the ferulic acid derivative S-52372 not only had certain scavenging effects on free radicals in biochemical experiments, but also prevented inflammation and oxidative stress in LPS-stimulated RAW264.7 cells in the cellular environment; intracellular ROS and inflammatory mediators, including iNOS, COX-2, TNF-α, and IL-6, were also suppressed. In a computer prediction, S-52372 owned better water solubility and lower toxicity than FA. This compound deserves further research to find an ideal FA derivative.

Integrating Computer Prediction Methods in Social Science: A Comment on Hofman et al. (2021)

Machine learning and other computer-driven prediction models are one of the fastest growing trends in computational social science. These methods and approaches were developed in computer science and with different goals and epistemologies than those in social science. The most obvious difference being a focus on prediction versus explanation. Predictive modeling offers great potential for improving research and theory development, but its adoption poses some challenges and creates new problems. For this reason, Hofman et al. (2021) published recommendations for more effective integration of predictive modeling into social science. In this communication I review their recommendations and expand on some additional concerns related to current practices and whether prediction can effectively serve the goals of most social scientists. Overall, I argue they provide a sound set of guidelines and a classification scheme that will serve those of us working in computational social science.