EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Na2S-Mediated One-Pot Selective Deoxygenation of α-Hydroxyl Carbonyl Compounds including Natural Products

A practical method for the deoxygenation of α-hydroxyl carbonyl compounds under mild reaction conditions is reported here. The use of cheap and easy-to-handle Na₂S·9H₂O as the reductant in the presence of PPh₃ and N-chlorosuccinimide (NCS) enables the selective dehydroxylation of α-hydroxyl carbonyl compounds, including ketones, esters, amides, imides and nitrile groups. The synthetic utility is demonstrated by the late-stage deoxygenation of bioactive molecule and complex natural products.     

A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

Packed-Fiber Solid Phase-Extraction Coupled with HPLC-MS/MS for Rapid Determination of Lipid Oxidative Damage Biomarker 8-Iso-Prostaglandin F2α in Urine

The 8-iso-prostaglandin F_2α (8-iso-PGF_2α) biomarker is used as the gold standard for tracing lipid oxidative stress in vivo. The analysis of urinary 8-iso-PGF_2α is challenging when dealing with trace amounts of 8-iso-PGF_2α and the complexity of urine matrixes. A packed-fiber solid-phase extraction (PFSPE)–coupled with HPLC-MS/MS–method, based on polystyrene (PS)-electrospun nanofibers, was developed for the specific determination of 8-iso-PGF_2α in urine and compared with other newly developed LC-MS/MS methods. The method, which simultaneously processed 12 samples within 5 min on a self-made semi-automatic array solid-phase extraction processor, was the first to introduce PS-electrospun nanofibers as an adsorbent for the extraction of 8-iso-PGF_2α and was successfully applied to real urine samples. After optimizing the PFSPE conditions, good linearity in the range of 0.05–5 ng/mL with R² > 0.9996 and a satisfactory limit of detection of 0.015 ng/mL were obtained, with good intraday and interday precision (RSD < 10%) and recoveries of 95.3–103.8%. This feasible method is expected to be used for the batch quantitative analysis of urinary 8-iso-PGF_2α.     

Precise electro-reduction of alkyl halides for radical defluorinative alkylation

Reported here is a precise electro-reduction strategy for radical defluorinative alkylation towards the synthesis of gem-difluoroalkenes from α-trifluoromethylstyrenes. According to the redox-potential difference of the radical precursors, direct or indirect electrolysis is respectively adopted to realize the precise reduction. An easy-to-handle, catalyst-and metal-free condition is developed for the reduction of alkyl radical precursors that are generally easier to be reduced than α-trifluorome...     

PVC/MBS/纳米 BaSO4 复合材料的制备及其性能

采用熔融共混法制备聚氯乙烯(PVC)/甲基丙烯酸甲酯-丁二烯-苯乙烯共聚物(MBS)/纳米重晶石(nano-BaSO4)三元复合材料,考察其力学性能和热稳定性能,并用扫描电镜观察冲击断面的形态.结果表明:MBS 与 nano-BaSO4 可协同增韧 PVC;当 MBS、nano-BaSO4 的含量分别为 10%、1%时,材料的韧性和刚性可同时得以改善,其冲击断面表现出典型的韧性断裂特征;热重分析显示添加 1%nano-BaSO4 可显著提高 PVC/MBS 的热稳定性能,其第一降解阶段的初始分解温度和最快分解温度分别提高了 10℃和14℃.     

光纤陀螺温控技术的研究

温度变化将严重地影响干涉型光纤陀螺仪(IFOG)系统的零位漂移.本文在初步研究温控技术的基础上,给出一种实用的温控电路,以用于在测试温度变化所引起的零位漂移中进行温度调节与控制.通过在此电路下实测的两者变化关系曲线采取补偿措施,以减小光纤陀螺的零位漂移.     

Promising Chemotherapy for Malignant Pediatric Brain Tumor in Recent Biological Insights

Brain tumors are the most widespread malignancies in children around the world. Chemotherapy plays a critical role in the treatment of these tumors. Although the current chemotherapy process has a remarkable outcome for a certain subtype of brain tumor, improving patient survival is still a major challenge. Further intensive treatment with conventional non-specific chemotherapy could cause additional adverse reactions without significant advancement in survival. Recently, patient derived brain tumor, xenograft, and whole genome analysis using deep sequencing technology has made a significant contribution to our understanding of cancer treatment. This realization has changed the focus to new agents, targeting the molecular pathways that are critical to tumor survival or proliferation. Thus, many novel drugs targeting epigenetic regulators or tyrosine kinase have been developed. These selective drugs may have less toxicity in normal cells and are expected to be more effective than non-specific chemotherapeutics. This review will summarize the latest novel targets and corresponding candidate drugs, which are promising chemotherapy for brain tumors according to the biological insights.     

Rapid Detection of Five Estrogens Added Illegally to Dietary Supplements by Combining TLC with Raman Imaging Microscope

Estrogens added illegally to dietary supplements are hazardous to human health. Traditional detection and analysis methods have many limitations, and we have developed an assay that combines thin-layer chromatography with Raman imaging microscopy (TLC-RIM). The five estrogens (estrone, estradiol, estriol, ethinyl estradiol, and diethylstilbestrol) were initially separated by TLC, then detected by area scanning Raman imaging with a 532 nm laser under a microscope. Raman spectra were obtained for each estrogen, which were used for detecting estrogen illegally added to botanical dietary supplements. The LOD of each estrogen was 0.4, 1.0, 0.8, 0.2, and 0.2 mg/mL, respectively. The matrix in the real sample did not interfere with the detection of estrogens. The method was fast, sensitive, stable, specific, and reliable.     

Impact of the Acetaldehyde-Mediated Condensation on the Phenolic Composition and Antioxidant Activity of Vitis vinifera L. Cv. Merlot Wine

Acetaldehyde is a critical reactant on modifying the phenolic profile during red wine aging, suggesting that the acetaldehyde-mediated condensation can be responsible for the variation of antioxidant activity during the aging of this beverage. The present study employs exogenous acetaldehyde at six levels of treatment (7.86 ± 0.10–259.02 ± 4.95 mg/L) before the bottle aging of Merlot wines to encourage phenolic modification. Acetaldehyde and antioxidant activity of wine were evaluated at 0, 15, 30, 45, 60 and 75 days of storage, while monomeric and polymeric phenolics were analyzed at 0, 30 and 75 days of storage. The loss of acetaldehyde was fitted to a first-order reaction model, the rate constant (k) demonstrated that different chemical reaction happened in wines containing a different initial acetaldehyde. The disappearance of monomeric phenolics and the formation of polymeric phenolics induced by acetaldehyde could be divided into two phases, the antioxidant activity of wine did not alter significantly in the first phase, although most monomeric phenolics vanished, but the second phase would dramatically reduce the antioxidant activity of wine. Furthermore, a higher level of acetaldehyde could shorten the reaction time of the first phase. These results indicate that careful vinification handling aiming at controlling the acetaldehyde allows one to maintain prolonged biological activity during wine aging.