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09 ± 0.12 and 0.15 ± 0.11 mm, respectively); in subjects with a horizontal sector of PPRD falling inside the pupil, the AL increased less (p = 0.035) than matching or outside the pupil groups by 0.04 ± 0.10 mm, 0.10 ± 0.11 mm, and 0.17 ± 0.12 mm, respectively. This means a 76% lesser AL growth or 0.13 mm/year in absolute reduction. selleck chemical OK corneal parameters can be modified by changing the OK lens designs, which affects myopia progression and AL elongation. Smaller BOZD induces a reduced PPRDs that slows AL elongation better than standard OK lenses. Further investigations should elucidate the effect of pupillary diameter, PPRD, and power change on myopia control.In recent years, the applications of lithium-ion batteries have emerged promptly owing to its widespread use in portable electronics and electric vehicles. Nevertheless, the safety of the battery systems has always been a global concern for the end-users. The separator is an indispensable part of lithium-ion batteries since it functions as a physical barrier for the electrode as well as an electrolyte reservoir for ionic transport. The properties of separators have direct influences on the performance of lithium-ion batteries, therefore the separators play an important role in the battery safety issue. With the rapid developments of applied materials, there have been extensive efforts to utilize these new materials as battery separators with enhanced electrical, fire, and explosion prevention performances. In this review, we aim to deliver an overview of recent advancements in numerical models on battery separators. Moreover, we summarize the physical properties of separators and benchmark selective key performance indicators. A broad picture of recent simulation studies on separators is given and a brief outlook for the future directions is also proposed.Detection of the loading volume of mining trucks is an important task in open pit mining. Aiming at the addressing the current problems of low accuracy and high cost of the detection of the loading volume of mining trucks, this paper proposes a mining truck loading volume detection model based on deep learning and image recognition. The training and test data of the model consists of 6000 sets of images taken in a laboratory environment. After image preprocessing, the VGG16 network model is used to pre classify the ore images. The classification results are displayed and the possibility of each category is determined. Then, the loading volume of mining trucks is calculated by using the classification results and the least squares algorithm. By using the labeled image data of five kinds of mining truck loading volume, the arbitrary loading volume detection of mining trucks is realized, which effectively solves the problem of a lack of labeled data types caused by the difficulty in obtaining mine data. Root mean square error (RMSE) and mean absolute error (MAE) are used to evaluate the fitting effect of the model. The experimental results show that the model has high prediction accuracy. The average absolute error is 17.85 cm3. In addition, this paper uses 400 real mining truck images of open-pit mines to verify the model and the average absolute error is 2.53 m3. The experimental results show that the model has good generality and can be applied well to the actual production of open-pit mines.Considering the complexity of the physical model of wildfire occurrence, this paper develops a method to evaluate the wildfire risk of transmission-line corridors based on Naïve Bayes Network (NBN). First, the data of 14 wildfire-related factors including anthropogenic, physiographic, and meteorologic factors, were collected and analyzed. Then, the relief algorithm is used to rank the importance of factors according to their impacts on wildfire occurrence. After eliminating the least important factors in turn, an optimal wildfire risk assessment model for transmission-line corridors was constructed based on the NBN. Finally, this model was carried out and visualized in Guangxi province in southern China. Then a cost function was proposed to further verify the applicability of the wildfire risk distribution map. The fire events monitored by satellites during the first season in 2020 shows that 81.8% of fires fall in high- and very-high-risk regions.The serine biosynthetic pathway is a key element contributing to tumor proliferation. In recent years, targeting of phosphoglycerate dehydrogenase (PHGDH), the first enzyme of this pathway, intensified and revealed to be a promising strategy to develop new anticancer drugs. Among attractive PHGDH inhibitors are the α-ketothioamides. In previous work, we have demonstrated their efficacy in the inhibition of PHGDH in vitro and in cellulo. However, the precise site of action of this series, which would help the rational design of new inhibitors, remained undefined. In the present study, the detailed mechanism-of-action of a representative α-ketothioamide inhibitor is reported using several complementary experimental techniques. Strikingly, our work led to the identification of an allosteric site on PHGDH that can be targeted for drug development. Using mass spectrometry experiments and an original α-ketothioamide diazirine-based photoaffinity probe, we identified the 523Q-533F sequence on the ACT regulatory domain of PHGDH as the binding site of α-ketothioamides. Mutagenesis experiments further documented the specificity of our compound at this allosteric site. Our results thus pave the way for the development of new anticancer drugs using a completely novel mechanism-of-action.Amyotrophic lateral sclerosis (ALS) is a devastating adult-onset neurodegenerative disease characterized by the progressive degeneration of upper and lower motoneurons. Most ALS cases are sporadic but approximately 10% of ALS cases are due to inherited mutations in identified genes. ALS-causing mutations were identified in over 30 genes with superoxide dismutase-1 (SOD1), chromosome 9 open reading frame 72 (C9orf72), fused in sarcoma (FUS), and TAR DNA-binding protein (TARDBP, encoding TDP-43) being the most frequent. In the last few decades, Drosophila melanogaster emerged as a versatile model for studying neurodegenerative diseases, including ALS. In this review, we describe the different Drosophila ALS models that have been successfully used to decipher the cellular and molecular pathways associated with SOD1, C9orf72, FUS, and TDP-43. The study of the known fruit fly orthologs of these ALS-related genes yielded significant insights into cellular mechanisms and physiological functions. Moreover, genetic screening in tissue-specific gain-of-function mutants that mimic ALS-associated phenotypes identified disease-modifying genes.