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Mechanism involving upkeep in the intestinal tract mucosa structures as well as NF-κB/PGE2 lowering simply by hydrogen sulfide on cholera toxin-induced diarrhea in rodents.
We investigated the longitudinal relations between cognitive skills, specifically language-related skills, and word-problem solving in 340 children (6.10 to 9.02 years). We used structural equation modeling to examine whether word-problem solving, computation skill, working memory, nonverbal reasoning, oral language, and word reading fluency measured at second grade were associated with performance on measures of word-problem solving in fourth grade. Results indicated that prior word-problem solving, computation skill, nonverbal reasoning, and oral language were significantly associated with children's later word-problem solving. Multi-group modeling suggested that these relations were not significantly different for boys versus girls. Implications of these findings are discussed.Many college students in the United States take longer than four years to complete their bachelor's degrees. Long time-to-degree can increase higher education costs by billions. Time-to-degree can be reduced if students take more credits each term. While academic momentum theory suggests that additional credits may also improve student performance, and there is a strong positive correlation between course load and student performance, high course load may reduce time investment in each course, giving high course load a negative causal effect on performance. Concern about the negative impact of course load on performance, especially for struggling students, may lead to pushback against policies to reduce time-to-degree by increasing course load. Using longitudinal data from a regional four-year university with a high average time-to-degree, we find no evidence that high course loads have a negative impact on student grades, even for students at the low end of the performance distribution. This result is consistent with a model where students substitute time away from non-education activities when their course loads increase.The metaphorical adoption of the concepts of information, program and signal introduced into biology the logic and implicit causal structure of the mathematical theories of information; this is inimical to biology. In turn, those metaphors have hindered the development of a theory of organisms by transferring the agency of organisms to natural selection and to DNA. Moreover, those metaphors introduced into biology the dualism software-hardware and a Laplacian causal structure. Instead, we propose to uphold the agency of the living by adopting three foundational principles for a theory of organisms namely, 1) the principle of biological inertia (i.e., the default state of cells is proliferation and motility), 2) the principle of variation, and 3) the principle of organization.Pneumonia is a global disease that causes high children mortality. The situation has even been worsening by the outbreak of the new coronavirus named COVID-19, which has killed more than 983,907 so far. People infected by the virus would show symptoms like fever and coughing as well as pneumonia as the infection progresses. Timely detection is a public consensus achieved that would benefit possible treatments and therefore contain the spread of COVID-19. X-ray, an expedient imaging technique, has been widely used for the detection of pneumonia caused by COVID-19 and some other virus. Salubrinal To facilitate the process of diagnosis of pneumonia, we developed a deep learning framework for a binary classification task that classifies chest X-ray images into normal and pneumonia based on our proposed CGNet. In our CGNet, there are three components including feature extraction, graph-based feature reconstruction and classification. We first use the transfer learning technique to train the state-of-the-art convolutional neural networks (CNNs) for binary classification while the trained CNNs are used to produce features for the following two components. Then, by deploying graph-based feature reconstruction, we, therefore, combine features through the graph to reconstruct features. Finally, a shallow neural network named GNet, a one layer graph neural network, which takes the combined features as the input, classifies chest X-ray images into normal and pneumonia. Our model achieved the best accuracy at 0.9872, sensitivity at 1 and specificity at 0.9795 on a public pneumonia dataset that includes 5,856 chest X-ray images. To evaluate the performance of our proposed method on detection of pneumonia caused by COVID-19, we also tested the proposed method on a public COVID-19 CT dataset, where we achieved the highest performance at the accuracy of 0.99, specificity at 1 and sensitivity at 0.98, respectively.The COVID-19 outbreak is exacerbating uncertainty in energy demand. This paper aims to investigate the impact of the confined measures due to COVID-19 outbreak on energy demand of a building mix in a district. Three levels of confinement for occupant schedules are proposed based on a new district design in Sweden. The Urban Modeling Interface tool is applied to simulate the energy performance of the building mix. The boundary conditions and input parameters are set up according to the Swedish building standards and statistics. The district is at early-design stage, which includes a mix of building functions, i.e. residential buildings, offices, schools and retail shops. By comparing with the base case (normal life without confinement measures), the average delivered electricity demand of the entire district increases in a range of 14.3% to 18.7% under the three confinement scenarios. However, the mean system energy demands (sum of heating, cooling, and domestic hot water) decreases in a range of 7.1% to 12.0%. These two variation nearly cancel each other out, leaving the total energy demand almost unaffected. The result also shows that the delivered electricity demands in all cases have a relatively smooth variation across a year, while the system energy demands follow the principle trends for all the cases, which have peak demands in winter and much lower demands in transit seasons and summer. This study represents a first step in the understanding of the energy performance for community buildings when they confront with this kind of shock.
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