(6,6′-Dimeth­oxy­biphenyl-2,2′-di­yl)bis(diphenyl­phosphane) P,P′-dioxide dihydrate

In the title compound, C38H32O4P2·2H2O, the dihedral angle between the meth­oxy­phenol rings is 84.11 (7)°. O—H⋯O hydrogen bonds connect the water mol­ecules of crystallization with the main mol­ecule

5-[2-(4-Acetyl­oxyphen­yl)ethen­yl]benzene-1,3-diyl diacetate

The title compound, C20H18O6, was prepared from resveratrol {systematic name: 5-[(E)-2-(4-hy­droxy­phen­yl)ethen­yl]ben­z­ene-1,3-diol}, which can be isolated from grapes, through triacetyl­ation with using acetic anhydride in pyridine. The two benzene rings are approximately coplanar, making a dihedral angle of 6.64 (14)°, and the three acet­oxy group are located on the same side of the plane. The skeleton of the compound resembles a table with three legs. In the crystal, mol­ecules are linked via C—H⋯O interactions, forming inversion dimers. These dimers are further linked via C—H⋯O interactions, forming a three-dimensional structure

Bis[2-(1H-benzimidazol-2-yl)benzoato]copper(II) dihydrate

In the title compound, [Cu(C14H9N2O2)2]·2H2O, the Cu(II) ion lies on a centre of symmetry and is four-coordinated by two N atoms and two O atoms from two 2-(1H-benzimidazol-2-yl)benzoate ligands in a square-planar environment. The benzimidazol and benzyl rings form a dihedral angle of 42.8 (5)°. The mol­ecule contains two H-bonded carboxyl O acceptors and two H-bonded N—H donors in the benzimidazol groups, which inter­act with two symmetry-related uncoordinated water mol­ecules so that neighboring mol­ecular units are linked by (O—H)water⋯Ocarbox­yl hydrogen bonds with an R 2 4(8) graph-set motif, generating a helical chain in the a-axis direction. These chains are, in turn, inter­connected by (N—H)benzimidazol⋯Owater hydrogen bonds, forming a three-dimensional supra­molecular network

Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs

In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate

The Technology of Mould Steel for Online Pre-hardening

AbstractThis article describes a production method of mould steel pre-hardening, and focus on the advantage of this method, The technical core of method is the variable frequency and variable amplitude pulse uniform high-precision temperature control, which achieved by using strong-medium-weak water cooling, gas-water cooling and gas mist cooling composite cooling control technology. Optimizing the cooling rate path is a good method of optimizing quenched organization and structure

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

ObjectiveThis retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC).MethodsIn total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers—adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared.ResultsThe Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases.ConclusionThe ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions

Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans. In summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.https://doi.org/10.1186/s12864-020-06909-

Improved Siderotic Nodule Detection in Cirrhosis with Susceptibility-Weighted Magnetic Resonance Imaging: A Prospective Study

BACKGROUND: Hepatic cirrhosis is a common pathway of progressive liver destruction from multiple causes. Iron uptake can occur within the hepatic parenchyma or within the various nodules that form in a cirrhotic liver, termed siderotic nodules. Siderotic nodule formation has been shown to correlate with inflammatory activity, and while the relationship between siderotic nodule formation and malignancy remains unclear, iron distribution within hepatic nodules has known implications for the detection of hepatocellular carcinoma. We aimed to evaluate the role of abdominal susceptibility-weighted imaging in the detection of siderotic nodules in cirrhotic patients. METHODOLOGY/PRINCIPAL FINDINGS: Forty-six (46) cirrhotic patients with at least one siderotic nodule detected on previous imaging underwent both computed tomography and magnetic resonance imaging (T1-, T2-, T2*-, and susceptibility-weighted imaging) at 3.0 Tesla. Imaging data was independently analyzed by two radiologists. Siderotic nodule count was determined for each modality and imaging sequence. For each magnetic resonance imaging technique, siderotic nodule conspicuity was assessed on a 3 point scale (1 = weak, 2 = moderate, 3 = strong). More nodules were detected by susceptibility weighted imaging (n = 2935) than any other technique, and significantly more than by T2* weighted imaging (n = 1696, p<0.0001). Lesion conspicuity was also highest with susceptibility-weighted imaging, with all nodules found to be moderate (n = 6) or strong (n = 40); a statistically significant difference (p<0.001). CONCLUSIONS: Susceptibility-weighted imaging had the greatest lesion conspicuity and detected the highest number of siderotic nodules suggesting it is the most sensitive imaging technique to detect siderotic nodules in cirrhotic patients