Morrow Goldberg (fowlcolon4)

The structure-property relationship established from this new bonding-interaction perspective will help in designing improved chalcogenide materials for diverse applications, based on a fundamental chemical-bonding point of view.Background Helicobacter pylori (H pylori) infection is suggested to be a risk factor of metabolic syndrome (MS) and lipid abnormalities. The aim of this study was to investigate the influence of H pylori infection on MS and lipid abnormalities according to sex differences. Materials and methods We analyzed a total of 4551 adults who received health checkups from January 2016 to May 2017. We enrolled participants who did not have a history of hypertension, diabetes, hyperlipidemia, or cancer among those who underwent endoscopy with a rapid urease test. Results We included a total of 1065 participants, and 663 patients (62.3%) were H pylori-positive. The H pylori infection rate was 59.3% (426/719) in males and 68.5% (237/346) in females. The mean level of total cholesterol (P = .003), low-density lipoprotein (LDL) cholesterol (P = .046), and triglycerides (P = .029) were statistically higher in H pylori-infected males. read more The mean level of high-density lipoprotein (HDL) cholesterol was statistically lower in H pylori-infected females (P = .032). Multivariate analysis showed that total cholesterol in males (odds ratio [OR], 1.007; 95% confidence interval [CI], 1.002-1.011) and HDL cholesterol in females (OR, 0.983; 95% CI, 0.968-0.998) were associated with active H pylori infection. The prevalence of MS was higher in both male and female H pylori-infected groups; however, there was no statistical significance. Conclusions H pylori infection is significantly related to increased total cholesterol in males and to decreased HDL cholesterol in females, which suggests that H pylori could affect lipid profiles and may be different by sex.The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text-based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.To investigate the nature and strength of noncovalent interactions at the fullerene surface, molecular torsion balances consisting of C60 and organic moieties connected through a biphenyl linkage were synthesized. NMR and computational studies show that the unimolecular system remains in equilibrium between well-defined folded and unfolded conformers owing to restricted rotation around the biphenyl