Orthogonal analysis of variants in APOE gene using in-silico approaches reveals novel disrupting variants

Introduction: Alzheimer’s disease (AD) is one of the most prominent medical conditions in the world. Understanding the genetic component of the disease can greatly advance our knowledge regarding its progression, treatment and prognosis. Single amino-acid variants (SAVs) in the APOE gene have been widely investigated as a risk factor for AD Studies, including genome-wide association studies, meta-analysis based studies, and in-vivo animal studies, were carried out to investigate the functional importance and pathogenesis potential of APOE SAVs. However, given the high cost of such large-scale or experimental studies, there are only a handful of variants being reported that have definite explanations. The recent development of in-silico analytical approaches, especially large-scale deep learning models, has opened new opportunities for us to probe the structural and functional importance of APOE variants extensively.Method: In this study, we are taking an ensemble approach that simultaneously uses large-scale protein sequence-based models, including Evolutionary Scale Model and AlphaFold, together with a few in-silico functional prediction web services to investigate the known and possibly disease-causing SAVs in APOE and evaluate their likelihood of being functional and structurally disruptive.Results: As a result, using an ensemble approach with little to no prior field-specific knowledge, we reported 5 SAVs in APOE gene to be potentially disruptive, one of which (C112R) was classificed by previous studies as a key risk factor for AD.Discussion: Our study provided a novel framework to analyze and prioritize the functional and structural importance of SAVs for future experimental and functional validation

Controllable Synthesis of Sheet-Flower ZnO for Low Temperature NO₂ Sensor

ZnO is a wide band gap semiconductor metal oxide that not only has excellent electrical properties but also shows excellent gas-sensitive properties and is a promising material for the development of NO2 sensors. However, the current ZnO-based gas sensors usually operate at high temperatures, which greatly increases the energy consumption of the sensors and is not conducive to practical applications. Therefore, there is a need to improve the gas sensitivity and practicality of ZnO-based gas sensors. In this study, three-dimensional sheet-flower ZnO was successfully synthesized at 60 °C by a simple water bath method and modulated by different malic acid concentrations. The phase formation, surface morphology, and elemental composition of the prepared samples were studied by various characterization techniques. The gas sensor based on sheet-flower ZnO has a high response value to NO2 without any modification. The optimal operating temperature is 125 °C, and the response value to 1 ppm NO2 is 125. At the same time, the sensor also has a lower detection limit (100 ppb), good selectivity, and good stability, showing excellent sensing performance. In the future, water bath-based methods are expected to prepare other metal oxide materials with unique structures

Boundary optimization of inclined coal seam open-pit mine based on the ISSA–LSSVR coal price prediction method

Abstract As an important link in the complex system engineering project of open pit mining, the quality of the boundary determines the performance of the project to a large extent. However, changes in economic indicators may raise doubts about the optimality of mining boundaries. In this article, a coal price time series forecasting model that considers some amount of redundancy is proposed, which combines an improved sparrow search algorithm (ISSA) and a least squares support vector regression machine regression (LSSVR) algorithm. The optimal values of the penalty factor and kernel function parameter of the LSSVR model are selected by ISSA, which improves the prediction accuracy and generalization performance of the forecasting model. A multistep decision optimization method under fluctuating coal price conditions is proposed, and the model prediction results are applied to the boundary optimization design process. Using the widely applied block model as the basis, a set of optimal production nested pits is obtained, allowing the realm design results to fit the coal price fluctuation trend and further enhance enterprise efficiency. The applicability and effectiveness of this method were verified by taking an ideal two-dimensional model and an inclined coal seam open-pit coal mine in Xinjiang as an example