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The Al–10Sr master alloy is used to modify the A359/20 wt% SiC composite, aiming to enhance its tensile properties and wear characteristics concurrently. This modification yields improvements surpassing those observed in pure A359 alloys, a phenomenon attributed to the preferential formation of Al–Si–Sr compounds adjacent to SiC particles. The results elucidate that the integration of SiC, serving as a wear‐resistant phase, substantially augments the wear resistance of the alloy. Abrasion marks incurred from singular friction events are discernibly more superficial on eutectic silicon in contrast to the aluminum substrate. Furthermore, the metamorphosis of eutectic silicon from an elongated to a granular form mitigates stress concentrations. This morphological alteration is pivotal, enhancing wear resistance and diminishing the likelihood of particle detachment. [ABSTRACT FROM AUTHOR]

The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of grey theory in image engineering applications have prompted researchers to continuously explore it. The grey model (GM (1,1)) with the first-order differentiation of one variable is the grey prediction model that is most frequently used. It is a typical trend analysis model and can be used for image edge detection. The traditional integer-order differential image edge detection operator has problems such as blurred and discontinuous edges, incomplete image details, and high influence by noise. We present a novel grey model for detecting image edges based on a fractional-order discrete operator in this paper. To improve the features of the original image, our model first preprocesses it before calculating the prediction of the original image using our fractional-order cumulative greyscale model. We obtain the edge information of the image by first subtracting a preprocessed image from the predicted image and then eliminating isolated noise points using the median filtering method. Based on the discrete wavelet transform, image edges are finally extracted. The comparison experiments with a traditional edge detection operator show that our algorithm can accurately locate the image edges, the image edges are clear and complete, and this model has better anti-noise performance. [ABSTRACT FROM AUTHOR]

Electroconvulsive therapy is an important somatic treatment for severe mental disorders with established efficacy and safety. However, data on the relationship between ECT and the readmission rate of patients with schizophrenia are scarce. This study will explore the association between the administration of ECT and readmission rates using a machine learning method. Inpatient medical records from the year of 2016 in one large psychiatric hospital in Beijing, China, were analyzed using a machine learning algorithm to determine the most important variables affecting readmission of patients with schizophrenia. The medical records of 2131 inpatients with schizophrenia were reviewed. 1099 patients were followed up within 3 months of their index admission (642 ECT cases and 457 non-ECT cases) and 1032 patients were followed up within 6 months (596 ECT cases and 436 non-ECT cases) after discharge. The 3- and 6-month readmission rates in the ECT group (11.37% and 17.94%, respectively) were significantly lower than that of the patients who did not receive ECT (18.79% and 29.36%, respectively, both p < 0.001). The risk of readmission was significantly associated with male sex, older age, being married, having a lower income, a shorter inpatient length of stay, and receiving specific antipsychotic medications including olanzapine, paliperidone, clozapine, and haloperidol during the index admission. In the ECT group, patients who received 9 or more treatments were significantly less likely to be readmitted. Receiving ECT may be associated with a lower risk of readmission in patients with schizophrenia. • Receiving ECT treatment during the index admission was associated with significantly lower readmission rates within 3 and 6 months in patients with schizophrenia. • The risk of readmission was significantly associated with male sex, older age, being married, having a lower income, a shorter inpatient length of stay, and receiving some specific antipsychotic medications including olanzapine, paliperidone, clozapine, and haloperidol during the index admission. • Among patients received ECT treatment, patients who received 9 or more treatments were significantly less likely to be readmitted. [ABSTRACT FROM AUTHOR]

The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments. [ABSTRACT FROM AUTHOR]

Due to the non-locality of fractional derivative, the analytical solution and good approximate solution of fractional partial differential equations are usually difficult to get. Reproducing kernel space is a perfect space in studying this type of equations, however the numerical results of equations by using the traditional reproducing kernel method (RKM) isn't very good. Based on this problem, we present the piecewise technique in the reproducing kernel space to solve this type of equations. The focus of this paper is to verify the stability and high accuracy of the present method by comparing the absolute error with traditional RKM and study the effect on absolute error for different values of α. Furthermore, we can study the distribution of entire space at a particular time period. Three numerical experiments are provided to verify the efficiency and stability of the proposed method. Meanwhile, it is tested by experiments that the change of the value of α has little effect on its accuracy. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the effects of phosphorylatable nucleus localization signal linked nucleic kinase substrate short peptide (NNSp)-conjugated chitosan (NNSp-CS) mediated miR-140 and IGF-1 in both rabbit chondrocytes and cartilage defects model. NNSp-CS was combined with pBudCE4.1-IGF-1, pBudCE4.1-miR-140, and negative control pBudCE4.1 to form pDNA/NNSp-CS complexes. Then these complexes were transfected into chondrocytes or injected intra-articularly into the knee joints. High levels of IGF-1 and miR-140 expression were detected both in vitro and in vivo. Compared with pBudCE4.1 group, in vitro, the transgenic groups significantly promoted chondrocyte proliferation, increased glycosaminoglycan (GAG) synthesis, and ACAN, COL2A1, and TIMP-1 levels, and reduced the levels of nitric oxide (NO), MMP-13, and ADAMTS-5. In vivo, the exogenous genes enhanced COL2A1, ACAN, and TIMP-1 expression in cartilage and reduced cartilage Mankin score and the contents of NO, IL-1β, TNF-α, and GAG contents in synovial fluid of rabbits, MMP-13, ADAMTS-5, COL1A2, and COL10A1 levels in cartilage. Double gene combination showed better results than single gene. This study indicate that NNSp-CS is a better gene delivery vehicle in gene therapy for cartilage defects and that miR-140 combination IGF-1 transfection has better biologic effects on cartilage defects. [ABSTRACT FROM AUTHOR]

A concise synthesis of alkyl-2,3-dihydro-1H-isoindolin-1-one derivatives from 2-formyl benzoic acid, β-keto acid, and ammonia or primary amine was developed via a quaternary ammonium salt-promoted multi-component sequence of decarboxylative alkylation/lactamization reaction in water, in which the target products were obtained in good to excellent yields. [ABSTRACT FROM AUTHOR]

Fiveisostructural three-dimensional (3D) lanthanide (Ln) frameworks[LnCl(bpdc)(DMF)] (Ln = La (1), Ce (2),Pr (3), Nd (4), and Sm (5),H2bpdc = 4,4′-biphenyl-dicarboxylic acid, DMF = N,N-dimethylformamide) have been solvothermally synthesizedand characterized by TGA, IR, and X-ray single crystal diffraction.The frameworks contain unusual Ln-carboxylate/Cl chains, and whichare extended by bpdc2-linkers to form 4-connected sranetworks with 1D rhombic channels. The effect of lanthanide contractioninduces the decreases of average Ln–O and Ln–Cl distancesfrom La to Sm. 4exhibits strong characteristic luminescenceof Nd3+ions in the near-infrared (NIR) region, resultingfrom the sensitization of bpdc2–. 3displays weak NIR emission due to the inefficient sensitizationof Pr3+ions. The depopulation of the Stark levels andpossible antiferromagnetic interactions within the Ln-carboxylate/Clchains lead to the continuous decreases of χMT for 2–4along the decreasing temperatures. [ABSTRACT FROM AUTHOR]

The first organocatalytic asymmetric sequential allylic alkylation–cyclization of Morita–Baylis–Hillman carbonates and 3-hydroxyoxindoles has been developed to afford spirooxindoles bearing α-methylene-γ-butyrolactone motifs in 25–85% yield, 60–94% ee and up to ＞20 : 1 diastereoselectivity. [ABSTRACT FROM AUTHOR]

The title compound, {[Cu(NH3)4][Cu(CN)3]2} n, features a CuI-CuII mixed-valence CuCN framework based on {[Cu2(CN)3]−} n anionic layers and [Cu(NH3)4]2+ cations. The asymmetric unit contains two different CuI ions and one CuII ion which lies on a centre of inversion. Each CuI ion is coordinated to three cyanide ligands with a distorted trigonal-planar geometry, while the CuII ion is ligated by four ammine ligands, with a distorted square-planar coordination geometry. The interlinkage between CuI ions and cyanide bridges produces a honeycomb-like {[Cu2(CN)3]−} n anionic layer containing 18-membered planar [Cu(CN)]6 metallocycles. A [Cu(NH3)4]2+ cation fills each metallocyclic cavity within pairs of exactly superimposed {[Cu2(CN)3]−} n anionic layers, but there are no cations between the layers of adjacent pairs, which are offset. Pairs of N-H...N hydrogen-bonding interactions link the N-H groups of the ammine ligands to the N atoms of cyanide ligands. [ABSTRACT FROM AUTHOR]

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research. [ABSTRACT FROM AUTHOR]

The article presents chemical equations related to the article organocatalytic asymmetric cascade with Michael, Hemiketalization & Retro-Aldol reaction of 3-acetyl-oxindole with unsaturated ketoesters by amino-squaramides by J. Zhou & others, appeared in a 2014 issue of journal "Tetrahedron."

The article presents chemical equations related to the article "Efficient Asymmetric Michael Reaction of 2-Oxindole-3-carboxylate Esters with Maleimides Catalyzed by Cinchonidine" by J. Zhou and others, published in the periodical "Tetrahedron" in 2014.