Meta-Analysis of Methamphetamine Modulation on Amyloid Precursor Protein through HMGB1 in Alzheimer’s Disease

The deposition of amyloid-beta (Aβ) through the cleavage of amyloid-beta precursor protein (APP) is a biomarker of Alzheimer’s disease (AD). This study used QIAGEN Ingenuity Pathway Analysis (IPA) to conduct meta-analysis on the molecular mechanisms by which methamphetamine (METH) impacts AD through modulating the expression of APP. All the molecules affected by METH and APP were collected from the QIAGEN Knowledge Base (QKB); 78 overlapping molecules were identified. Upon simulation of METH exposure using the “Molecule Activity Predictor” feature, eight molecules were found to be affected by METH and exhibited activation relationships on APP expression at a confidence of p = 0.000453 (Z-score = 3.51, two-tailed). Core Analysis of these eight molecules identified High Mobility Group Box protein 1 (HMGB1) signaling pathway among the top 5 canonical pathways with most overlap with the 8-molecule dataset. Simulated METH exposure increased APP expression through HMGB1 at a confidence of p < 0.00001 (Z-score = 7.64, two-tailed). HMGB1 is a pathogenic hallmark in AD progression. It not only increases the production of inflammatory mediators, but also mediates the disruption of the blood-brain barrier. Our analyses suggest the involvement of HMGB1 signaling pathway in METH-induced modulation of APP as a potential casual factor of AD.     

Transgenerational Effects of Di(2-Ethylhexyl) Phthalate on Anogenital Distance,Sperm Functions and DNA Methylation in Rat Offspring

Di(2-ethylhexyl) phthalate (DEHP) is widely used as a plasticizer in the manufacture of polyvinylchloride plastics and has been associated with concerns regarding male reproductive toxicity. In this study, we hypothesized that maternal exposure to DEHP induces transgenerational inheritance of adult-onset adverse reproductive outcomes through the male germline in the F1, F2, and F3 generations of male offspring. Pregnant rats were treated with 5 or 500 mg of DEHP/kg/day through gavage from gestation day 0 to birth. The offspring body weight, anogenital distance (AGD), anogenital index (AGI), sperm count, motility, and DNA fragmentation index (DFI) were measured for all generations. Methyl-CpG binding domain sequencing was performed to analyze sperm DNA methylation status in the F3. DEHP exposure at 500 mg/kg affected AGD, AGI, sperm count, mean DFI, and %DFI in the F1; AGD, sperm count, and mean DFI in the F2; and AGD, AGI, mean DFI, and %DFI in the F3. DEHP exposure at 5 mg/kg affected AGD, AGI, sperm count, and %DFI in the F1; sperm count in the F2; and AGD and AGI in F3. Compared with the control group, 15 and 45 differentially hypermethylated genes were identified in the groups administered 5 mg/kg and 500 mg/kg DEHP, respectively. Moreover, 130 and 6 differentially hypomethylated genes were observed in the groups administered 5 mg/kg and 500 mg/kg DEHP. Overall, these results demonstrated that prenatal exposure to DEHP caused transgenerational epigenetic effects, which may explain the observed phenotypic changes in the male reproductive system.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Synthesis of Hollow Three-Dimensional Channels LiNi_(0.5)Mn_(1.5)O_4 Microsphere by PEO Soft Template Assisted with Solvothermal Method

A appropriate size with three-dimension(3 D) channels for lithium diffusion plays an important role in constructing highperforming LiNi_(0.5)Mn_(1.5)O_4(LNMO) cathode materials, as it can not only reduce the transport path of lithium ions and electrons, but also reduce the side effects and withstand the structural strain in the process of repetitive Li~+ intercalation/deintercalation. In this work, an e fficient method for designing the hollow LNMO microsphere with 3 D channels structure by using polyethylene oxide(PEO) as soft template agent assisted solvothermal method is proposed. Experimental results indicate that PEO can make the reagents mingle evenly and nucleate slowly in the solvothermal process, thus obtaining a homogeneous distribution of carbonate precursors. In the final LNMO products, the hollow 3 D channels structure obtained by the decomposition of PEO and carbonate precursor in the calcination can provide abundant electroactive zones and electron/ion transport paths during the charge/discharge process, which benefits to improve the cycling performance and rate capability. The LNMO prepared by adding 1 g PEO possesses the most outstanding electrochemical performance, which presented an excellent discharge capacity of 143.1 mAh g~(-1) at 0.1 C and with a capacity retention of 92.2% after 100 cycles at 1 C. The superior performance attributed to the 3 D channels structure of hollow microspheres, which provide uninterrupted conductive systems and therefore achieve the stable transfer for electron/ion.     

Transient liquid phase sintering of high-purity mullite for high-temperature structural ceramics

High-purity mullite ceramics, promising engineering ceramics for high-temperature applications, were fabricated using transient liquid phase sintering to improve their high-temperature mechanical properties. Small amounts of ultrafine alumina or silica powders were uniformly mixed with the mullite precursor depending on the silica-alumina ratio of the resulting ceramics to allow for the formation of a transient liquid phase during sintering, thus, enhancing densification at the early stage of sintering and mullite formation by the reaction between additional alumina and the residual glassy phase (mullitization) at the final stage of sintering. The addition of alumina powder to the silica-rich mullite precursor resulted in a reaction between the glassy silica and alumina phases during sintering, thereby forming a mullite phase without inhibiting densification. The addition of fine silica powder to the mullite single-phase precursor led to densification with an abnormal grain growth of mullite, whereas some of the added silica remained as a glassy phase after sintering. The resulting mullite ceramics prepared using different powder compositions showed different sintering behaviors, depending on the amount of alumina added. Upon selecting an optimum process and the amount of alumina to be added, the pure mullite ceramics obtained via transient liquid phase sintering exhibited high density (approximately 99%) and excellent high-temperature flexural strength (approximately 320 MPa) at 1500 °C in air. These results clearly demonstrate that pure mullite ceramics fabricated via transient liquid phase sintering with compositions close to those of stoichiometric mullite could be a promising process for the fabrication of high-temperature structural ceramics used in an ambient atmosphere. The transient liquid phase sintering process proposed in this study could be a powerful processing tool that allows for the preparation of superior high-temperature structural ceramics used in the ambient processing atmosphere.     

High-temperature oxidation behavior of monolithic Zr3[Al(Si)]4C6 and its composite incorporating 30vol% ZrB2-SiC: Providing a basis for broadening the service temperature

To provide a basis for the high-temperature oxidation of ultra-high temperature ceramics (UHTCs), the oxidation behavior of Zr₃[Al(Si)]₄C₆ and a novel Zr₃[Al(Si)]₄C₆-ZrB₂-SiC composite at 1500 °C were investigated for the first time. From the calculation results, the oxidation kinetics of the two specimens follow the oxidation dynamic parabolic law. Zr₃[Al(Si)]₄C₆ exhibited a thinner oxide scale and lower oxidation rate than those of the composite under the same conditions. The oxide scale of Zr₃[Al(Si)]₄C₆ exhibited a two-layer structure, while that of the composite exhibited a three-layer structure. Owing to the volatilization of B₂O₃ and the active oxidation of SiC, a porous oxide layer formed in the oxide scale of the composite, resulting in the degradation of its oxidation performance. Furthermore, the cracks and defects in the oxide scale of the composite indicate that the reliability of the oxide scale was poor. The results support the service temperature of the obtained ceramics.     

Effects of the Anti-Tumorigenic Agent AT101 on Human Glioblastoma Cells in the Microenvironmental Glioma Stem Cell Niche

Glioblastoma (GBM) is a barely treatable disease due to its profound chemoresistance. A distinct inter- and intratumoral heterogeneity reflected by specialized microenvironmental niches and different tumor cell subpopulations allows GBMs to evade therapy regimens. Thus, there is an urgent need to develop alternative treatment strategies. A promising candidate for the treatment of GBMs is AT101, the R(-) enantiomer of gossypol. The present study evaluates the effects of AT101, alone or in combination with temozolomide (TMZ), in a microenvironmental glioma stem cell niche model of two GBM cell lines (U251MG and U87MG). AT101 was found to induce strong cytotoxic effects on U251MG and U87MG stem-like cells in comparison to the respective native cells. Moreover, a higher sensitivity against treatment with AT101 was observed upon incubation of native cells with a stem-like cell-conditioned medium. This higher sensitivity was reflected by a specific inhibitory influence on the p-p42/44 signaling pathway. Further, the expression of CXCR7 and the interleukin-6 receptor was significantly regulated upon these stimulatory conditions. Since tumor stem-like cells are known to mediate the development of tumor recurrences and were observed to strongly respond to the AT101 treatment, this might represent a promising approach to prevent the development of GBM recurrences.     

自适应融合层级特征的混合退化图像复原算法

多种退化类型混合的图像比单一类型的退化图像降质更严重,很难建立精确模型对其复原,研究端到端的神经网络算法是复原的关键.现有的基于操作选择注意力网络的算法(operation-wiseattentionnetwork,OWAN)虽然有一定的性能提升,但是其网络过于复杂,运行较慢,复原图像缺乏高频细节,整体效果也有提升的空间.针对这些问题,提出一种基于层级特征融合的自适应复原算法.该算法直接融合不同感受野分支的特征,增强复原图像的结构;用注意力机制对不同层级的特征进行动态融合,增加模型的自适应性,降低了模型冗余;另外,结合L1损失和感知损失,增强了复原图像的视觉感知效果.在DIV2K,BSD500等数据集上的实验结果表明,该算法无论是在峰值信噪比和结构相似性上的定量分析,还是在主观视觉质量方面,均优于OWAN算法,充分证明了该算法的有效性.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.