Metabolic Profiling Study of Yang Deficiency Syndrome in Hepatocellular Carcinoma by H

This study proposes a 1H NMR-based metabonomic approach to explore the biochemical characteristics of Yang deficiency syndrome in hepatocellular carcinoma (HCC) based on serum metabolic profiling. Serum samples from 21 cases of Yang deficiency syndrome HCC patients (YDS-HCC) and 21 cases of non-Yang deficiency syndrome HCC patients (NYDS-HCC) were analyzed using 1H NMR spectroscopy and partial least squares discriminant analysis (PLS-DA) was applied to visualize the variation patterns in metabolic profiling of sera from different groups. The differential metabolites were identified and the biochemical characteristics were analyzed. We found that the intensities of six metabolites (LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars) in serum of Yang deficiency syndrome patients were lower than those of non-Yang deficiency syndrome patients. It implies that multiple metabolisms, mainly including lipid, amino acid, and energy metabolisms, are unbalanced or weakened in Yang deficiency syndrome patients with HCC. The decreased intensities of metabolites including LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars in serum may be the distinctive metabolic variations of Yang deficiency syndrome patients with HCC. And these metabolites may be potential biomarkers for diagnosis of Yang deficiency syndrome in HCC

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.Comment: This paper has been accepted by IEEE TNNL

Molecular characterization of SARS-CoV-2 nucleocapsid protein

Corona Virus Disease 2019 (COVID-19) is a highly prevalent and potent infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Until now, the world is still endeavoring to develop new ways to diagnose and treat COVID-19. At present, the clinical prevention and treatment of COVID-19 mainly targets the spike protein on the surface of SRAS-CoV-2. However, with the continuous emergence of SARS-CoV-2 Variants of concern (VOC), targeting the spike protein therapy shows a high degree of limitation. The Nucleocapsid Protein (N protein) of SARS-CoV-2 is highly conserved in virus evolution and is involved in the key process of viral infection and assembly. It is the most expressed viral structural protein after SARS-CoV-2 infection in humans and has high immunogenicity. Therefore, N protein as the key factor of virus infection and replication in basic research and clinical application has great potential research value. This article reviews the research progress on the structure and biological function of SARS-CoV-2 N protein, the diagnosis and drug research of targeting N protein, in order to promote researchers’ further understanding of SARS-CoV-2 N protein, and lay a theoretical foundation for the possible outbreak of new and sudden coronavirus infectious diseases in the future

Distribution-free lack-of-fit tests in balanced mixed models

Here we discuss the problem of fitting a parametric model to the regression function of the fixed effects in a class of balanced mixed effects models. The proposed test is based on the supremum of the Khmaladze transformation of a certain partial sum process of calibrated residuals, and the asymptotic null distribution of this transformed process turns out to be the same as that of a time transformed standard Brownian motion. Moreover, we show that this test is consistent against a large class of fixed alternatives and has non-trivial asymptotic power against a class of nonparametric local alternatives. Simulation studies are conducted to assess the finite sample performance of the proposed test.Mixed model Lack-of-fit test Khmaladze transformation Brownian motion

Multi-omics study identifies that PICK1 deficiency causes male infertility by inhibiting vesicle trafficking in Sertoli cells

Abstract Background Infertility affects approximately 10–15% of reproductive-age men worldwide, and genetic causes play a role in one-third of cases. As a Bin-Amphiphysin-Rvs (BAR) domain protein, protein interacting with C-kinase 1 (PICK1) deficiency could lead to impairment of acrosome maturation. However, its effects on auxiliary germ cells such as Sertoli cells are unknown. Purpose The present work was aimed to use multi-omics analysis to research the effects of PICK1 deficiency on Sertoli cells and to identify effective biomarkers to distinguish fertile males from infertile males caused by PICK1 deficiency. Methods Whole-exome sequencing (WES) was performed on 20 infertility patients with oligozoospermia to identify pathogenic PICK1 mutations. Multi-omics analysis of a PICK1 knockout (KO) mouse model was utilized to identify pathogenic mechanism. Animal and cell function experiments of Sertoli cell-specific PICK1 KO mouse were performed to verify the functional impairment of Sertoli cells. Results Two loss-of-function deletion mutations c.358delA and c.364delA in PICK1 resulting in transcription loss of BAR functional domain were identified in infertility patients with a specific decrease in serum inhibin B, indicating functional impairment of Sertoli cells. Multi-omics analysis of PICK1 KO mouse illustrated that targeted genes of differentially expressed microRNAs and mRNAs are significantly enriched in the negative regulatory role in the vesicle trafficking pathway, while metabolomics analysis showed that the metabolism of amino acids, lipids, cofactors, vitamins, and endocrine factors changed. The phenotype of PICK1 KO mouse showed a reduction in testis volume, a decreased number of mature spermatozoa and impaired secretory function of Sertoli cells. In vitro experiments confirmed that the expression of growth factors secreted by Sertoli cells in PICK1 conditional KO mouse such as Bone morphogenetic protein 4 (BMP4) and Fibroblast growth factor 2 (FGF2) were decreased. Conclusions Our study attributed male infertility caused by PICK1 deficiency to impaired vesicle-related secretory function of Sertoli cells and identified a variety of significant candidate biomarkers for male infertility induced by PICK1 deficiency

A Complete Characterization of Bipartite Graphs with Given Diameter in Terms of the Inverse Sum Indeg Index

In 2010, Vukičević introduced an new graph invariant, the inverse sum indeg index of a graph, which has been studied due to its wide range of applications. Let Bnd be the class of bipartite graphs of order n and diameter d. In this paper, we mainly characterize the bipartite graphs in Bnd with the maximal inverse sum indeg index. Bipartite graphs with the largest, second-largest, and smallest inverse sum indeg indexes are also completely characterized

Synthesis of Ferromagnetic Nd2Fe14B Nanocrystalline via Solvothermal Decomposition and Reduction-Diffusion Calcination

Monodispersed and highly crystalline Nd2Fe14B ferromagnetic nanoparticles were chemically synthesized by two steps. Uniform NdFeBO nanoparticles (similar to 100 nm) were obtained via solvothermal decomposition in the presence of oleic acid, oleylamine, and hexadecyl trimethyl ammonium bromide. Prior to thermal treatment, thin CaO layer was covered onto oxide nanoparticles to maintain Nd/ Fe ratio (1/8) at high temperature. After thermal diffusion-reduction annealing at 800 degrees C, Nd2Fe14B nanocrystalline of small particle size (similar to 80 nm) revealed observable coercivity (400 Oe)

Enhanced large magnetic entropy change and adiabatic temperature change of Ni43Mn46Sn11 alloys by a rapid solidification method

The effect of arc melting and melt-spinning on magnetocaloric effect related magnetic properties of Ni43Mn46Sn11 alloys has been contrastively studied. Different measurements based on isothermal magnetization and heat capacity were carried out. For ribbon sample, extremely high magnetic entropy change Delta S-M of 41.4 J kg(-1) K-1 and adiabatic temperature change Delta T-ad of 3.5 K (0-5 T) were achieved, which increases by 40.3% and 16.7% compared with that of bulk sample respectively. The martensitic transition related magnetic properties have been systematically discussed. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved

Removal Modeling and Experimental Verification of Magnetorheological Polishing Fused Silica Glass

Compared to conventional polishing methods, magnetorheological polishing has no subsurface damage and a has good polishing effect, which is suitable for fused silica glass surface processing. However, the existing magnetorheological polishing material removal model has low processing efficiency and uneven removal, which cannot realize the deterministic processing of parts. The material removal (MR) model of fused silica glass is established by convolving the dwell time with the material removal function. The residence time is Fourier transformed. The consequence of process variable such as machining time, workpiece rotational frequency, machining gap and X-direction deflection on the MR of workpiece interface are analyzed. Experiments verify the validity of the material removal model. The surface precision PV value of the workpiece surface under the optimal process parameters was decreased from 7.959 nm to 0.609 nm for machining. The experiment results indicate that the established MR model can be implemented as the deterministic MR of the optical surface and ameliorate the surface accuracy of the workpiece surface