{4-[5-(4-tert-Butyl­phen­yl)-1,3,4-oxa­diazol-2-yl]phen­yl}methanol

In the title compound, C19H20N2O2, the 1,3,4-oxadiazole ring is almost coplanar with the two neighboring benzene rings [dihedral angles = 3.76 (4) and 5.49 (4)°]. In the crystal, mol­ecules are connected by strong inter­molecular O—H⋯N hydrogen bonds, forming chains parallel to the c axis

Impact of Enteromorpha Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China

Between 2008 and 2016, there were mass summer blooms of Enteromorpha in the Yellow Sea, China. They covered an area of thousands of square kilometers annually, lasting an average of 90 days. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the Enteromorpha blooms were used in this study of the Qianliyan Island area. Underwater robots survey trepang, wrinkles abalone, and submarine ecological status. We found that the time taken by Enteromorpha to cover the Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The Enteromorpha made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. Enteromorpha sedimentation during outbreaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of trepang and wrinkles abalone were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms

Structural basis for the identification of the N-terminal domain of coronavirus nucleocapsid protein as an antiviral target

Coronaviruses (CoVs) cause numerous diseases, including Middle East respiratory syndrome and severe acute respiratory syndrome, generating significant health-related and economic consequences. CoVs encode the nucleocapsid (N) protein, a major structural protein that plays multiple roles in the virus replication cycle and forms a ribonucleoprotein complex with the viral RNA through the N protein's N-terminal domain (N-NTD). Using human CoV-OC43 (HCoV-OC43) as a model for CoV, we present the 3D structure of HCoV-OC43 N-NTD complexed with ribonucleoside 5'-monophosphates to identify a distinct ribonucleotide-binding pocket. By targeting this pocket, we identified and developed a new coronavirus N protein inhibitor, N-(6-oxo-5,6-dihydrophenanthridin-2-yl)(N,N-dimethylamino)acetamide hydrochloride (PJ34), using virtual screening; this inhibitor reduced the N protein's RNA-binding affinity and hindered viral replication. We also determined the crystal structure of the N-NTD-PJ34 complex. On the basis of these findings, we propose guidelines for developing new N protein-based antiviral agents that target CoVs

STAR: A Structure and Texture Aware Retinex Model

© 2020 IEEE. Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent γ ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with γ \u3e 1, while the texture map is generated by been shrank with γ \u3c 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR

The prevalence of autism spectrum disorders in China: A comprehensive meta-analysis

There are conflicting prevalence estimates of autism spectrum disorders (ASDs) in mainland China (China thereafter). This study is a comprehensive meta-analysis of the pooled prevalence of ASDs in the general population in China. Study investigators independently conducted a systematic literature search of the following databases: PubMed, EMBASE, PsycINFO, China National Knowledge Infrastructure, Chinese biomedical literature service system, and Wan Fang. Studies reporting prevalence of ASDs and autism in Chinese population were identified and analysed using the Comprehensive Meta-Analysis program with the random effects model. Forty-four studies were included in the meta-analysis comprising 2,337,321 subjects of whom 46.66 % were females. The mean age of subjects ranged from 1.6 to 8 years. Based on diagnostic criteria the pooled prevalence of ASDs was 39.23 per 10,000 (95% CI: 28.44-50.03 per 10,000, I2=89.2%); specifically, the prevalence of autism was 10.18 per 10,000 (95% CI: 8.46-11.89 per 10,000, I2=92.5%). Subgroup analyses revealed significant difference in the prevalence of ASDs between genders (72.77 per 10,000 in males vs. 16.45 per 10,000 in females). In conclusion, the prevalence of ASDs and autism in China was found generally lower than those reported in other countries. Further studies are needed to clarify the variation in prevalence

Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, KGs contain rich side information (e.g., entity description), and how to effectively leverage those information has not been adequately investigated in self-supervised EA. In this paper, we propose an interactive contrastive learning model for self-supervised EA. The model encodes not only structures and semantics of entities (including entity name, entity description, and entity neighborhood), but also conducts cross-KG contrastive learning by building pseudo-aligned entity pairs. Experimental results show that our approach outperforms previous best self-supervised results by a large margin (over 9% average improvement) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.Comment: Accepted by CIKM 202

7-Nitro­quinazolin-4(3H)-one

In the crystal structure of the title compound, C8H5N3O3, inter­molecular N—H⋯O hydrogen bonds link mol­ecules into centrosymmetric dimers. These dimers are, in turn, linked though weak inter­molecular C—H⋯O and C—H⋯N hydrogen bonds and π–π stacking inter­actions, with centroid–centroid distances of 3.678 (3) Å, into a three-dimensional network

Bostrycin inhibits proliferation of human lung carcinoma A549 cells via downregulation of the PI3K/Akt pathway

<p>Abstract</p> <p>Background</p> <p>Bostrycin is a novel compound isolated from marine fungi that inhibits proliferation of many cancer cells. However, the inhibitory effect of bostrycin on lung cancers has not been reported. This study is to investigate the inhibitory effects and mechanism of bostrycin on human lung cancer cells in vitro.</p> <p>Methods</p> <p>We used MTT assay, flow cytometry, microarray, real time PCR, and Western blotting to detect the effect of bostrycin on A549 human pulmonary adenocarcinoma cells.</p> <p>Results</p> <p>We showed a significant inhibition of cell proliferation and induction of apoptosis in bostrycin-treated lung adenocarcinoma cells. Bostrycin treatment caused cell cycle arrest in the G0/G1 phase. We also found the upregulation of microRNA-638 and microRNA-923 in bostrycin-treated cells. further, we found the downregulation of p110α and p-Akt/PKB proteins and increased activity of p27 protein after bostrycin treatment in A549 cells.</p> <p>Conclusions</p> <p>Our study indicated that bostrycin had a significant inhibitory effect on proliferation of A549 cells. It is possible that upregulation of microRNA-638 and microRNA-923 and downregulaton of the PI3K/AKT pathway proteins played a role in induction of cell cycle arrest and apoptosis in bostrycin-treated cells.</p