MicroRNA-23a promotes myelination in the central nervous system.

Demyelinating disorders including leukodystrophies are devastating conditions that are still in need of better understanding, and both oligodendrocyte differentiation and myelin synthesis pathways are potential avenues for developing treatment. Overexpression of lamin B1 leads to leukodystrophy characterized by demyelination of the central nervous system, and microRNA-23 (miR-23) was found to suppress lamin B1 and enhance oligodendrocyte differentiation in vitro. Here, we demonstrated that miR-23a-overexpressing mice have increased myelin thickness, providing in vivo evidence that miR-23a enhances both oligodendrocyte differentiation and myelin synthesis. Using this mouse model, we explored possible miR-23a targets and revealed that the phosphatase and tensin homologue/phosphatidylinositol trisphosphate kinase/Akt/mammalian target of rapamycin pathway is modulated by miR-23a. Additionally, a long noncoding RNA, 2700046G09Rik, was identified as a miR-23a target and modulates phosphatase and tensin homologue itself in a miR-23a-dependent manner. The data presented here imply a unique role for miR-23a in the coordination of proteins and noncoding RNAs in generating and maintaining healthy myelin

Extremum Seeking Based Fault-Tolerant Cooperative Control for Multiagent Systems

We propose a novel fault-tolerant cooperative control strategy for multiagent systems. A set of unknown input observers for each agent are constructed for fault detection. Then a real-time adaptive extremum seeking algorithm is utilized for adaptive approximation of fault parameter. We prove that the consensus can be still reached by regulating the interconnection weights and changing the connection topology of the fault agent. A numerical simulation example is given to illustrate the feasibility and effectiveness of the proposed method

New Supernova Candidates from SDSS-DR7 of Spectral Survey

The letter presents 25 discovered supernova candidates from SDSS-DR7 with our dedicated method, called Sample Decrease, and 10 of them were confirmed by other research groups, and listed in this letter. Another 15 are first discovered including 14 type Ia and one type II based on Supernova Identification (SNID) analysis. The results proved that our method is reliable, and the description of the method and some detailed spectra analysis procedures were also presented in this letter.Comment: 6 pages, 3 figure

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

Bioactive compounds from Rumex plants

Two new naphthalene acylglucosides, rumexneposides A (1) and B (2), together with 12 known compounds (3-14), were isolated from the roots of Rumex nepalensis. Their structures were established by chemical and spectroscopic methods. The biological activities of compounds 1-14 as well as an additional 11 compounds previously isolated from R. nepalensis and Rumex hastatus (15–25) were evaluated against Mycobacterium tuberculosis, para-aminobenzoic acid (pAba) pathway, and a panel of human cancer cell lines. The results showed that compound 15 was the most active against M. tuberculosis with an MIC value of 2.85 mM similar to that of isoniazid. Compound 5 could inhibit pAba synthetic pathway with an MIC value of 12.6 mM, comparable to that of positive control abyssomicin C, representing a new example of the rare pAba pathway inhibitors

Is carrot consumption associated with a decreased risk of lung cancer? A meta-analysis of observational studies

Findings of epidemiological studies regarding the association between carrot consumption and lung cancer risk remain inconsistent. The present study aimed to summarise the current epidemiological evidence concerning carrot intake and lung cancer risk with a meta-analysis. We conducted a meta-analysis of case–control and prospective cohort studies, and searched PubMed and Embase databases from their inception to April 2018 without restriction by language. We also reviewed reference lists from included articles. Prospective cohort or case–control studies reporting OR or relative risk with the corresponding 95 % CI of the risk lung cancer for the highest compared with the lowest category of carrot intake. A total of eighteen eligible studies (seventeen case–control studies and one prospective cohort study) were included, involving 202 969 individuals and 5517 patients with lung cancer. The pooled OR of eighteen studies for lung cancer was 0·58 (95%CI 0·45, 0·74) by comparing the highest category with the lowest category of carrot consumption. Based on subgroup analyses for the types of lung cancer, we pooled that squamous cell carcinoma (OR 0·52, 95 % CI 0·19, 1·45), small-cell carcinoma (OR 0·43, 95 % CI 0·12, 1·59), adenocarcinoma (OR 0·34, 95 % CI 0·15, 0·79), large-cell carcinoma (OR 0·40, 95 % CI 0·10, 1·57), squamous and small-cell carcinoma (OR 0·85, 95 % CI 0·45, 1·62), adenocarcinoma and large-cell carcinoma (OR 0·20, 95 % CI 0·02, 1·70) and mixed types (OR 0·61, 95 % CI 0·46, 0·81). Exclusion of any single study did not materially alter the pooled OR. Integrated epidemiological evidence from observational studies supported the hypothesis that carrot consumption may decrease the risk of lung cancer, especially for adenocarcinoma

A Search for Double-peaked narrow emission line Galaxies and AGNs in the LAMOST DR1

LAMOST has released more than two million spectra, which provide the opportunity to search for double-peaked narrow emission line (NEL) galaxies and AGNs. The double-peaked narrow-line profiles can be well modeled by two velocity components, respectively blueshifted and redshifted with respect to the systemic recession velocity. This paper presents 20 double-peaked NEL galaxies and AGNs found from LAMOST DR1 using a search method based on multi-gaussian fit of the narrow emission lines. Among them, 10 have already been published by other authors, either listed as genuine double-peaked NEL objects or as asymmetric NEL objects, the remaining 10 being first discoveries. We discuss some possible origins for double-peaked narrow-line features, as interaction between jet and narrow line regions, interaction with companion galaxies and black hole binaries. Spatially resolved optical imaging and/or follow-up observations in other spectral bands are needed to further discuss the physical mechanisms at work.Comment: 17 pages, 5figures, 4 tables, accepted by RA

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure