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Non-human primate epidural ECoG analysis employing explainable deep studying engineering.
97; 95%CI 0.80, 1.17) with high heterogeneity (I2=80.5%), and little indication for publication bias (PEgger's test=0.24). The findings indicate that occupational exposure to ELF-MF, but not electric shocks, might be a risk factor for ALS. However, given the moderate to high heterogeneity and potential publication bias, the results should be interpreted with caution.Objectives Prolonged oxytocin exposure may result in increased blood loss during delivery. #link# Our objective was to determine whether an oxytocin rest period before cesarean delivery had an impact on blood loss. Methods We performed a retrospective cohort study of women who underwent primary cesarean delivery after oxytocin augmentation. The primary outcome was change between pre- and postoperative hematocrit (Hct) in women with less than 60-min oxytocin rest period (60 min group had a higher cumulative dose and longer duration of oxytocin administration. There was no significant difference in change in Hct between the two groups when controlling for these factors. Conclusions We did not find a significant correlation between the duration of the oxytocin rest period and blood loss. Oxytocin washout periods of greater than 60 min may not result in decreased blood loss at cesarean delivery, and thus, women may not benefit from such oxytocin washout periods.Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are Selleck MMRi62 to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.Co-localization analysis is a popular method for quantitative analysis in fluorescence microscopy imaging. The localization of marked proteins in the cell nucleus allows a deep insight into biological processes in the nucleus. Several metrics have been developed for measuring the co-localization of two markers, however, they depend on subjective thresholding of background and the assumption of linearity. link2 We propose a robust method to estimate the bivariate distribution function of two color channels. From this, we can quantify their co- or anti-colocalization. The proposed method is a combination of the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This new method can measure the spatial and nonlinear correlation of signals to determine the marker colocalization in fluorescence microscopy images. The proposed method is compared with MEM for bivariate probability distributions. The new colocalization metric is validated on simulated and real data. The results show that MEC can determine co- and anti-colocalization even in high background settings. MEC can, therefore, be used as a robust tool for colocalization analysis.Objectives Weight control behavior is a strategy for weight loss or weight gains that range from healthy to unhealthy. This study is aimed to determine the prevalence of weight control behaviors and their related factors in adolescent girls in Tehran. Methods Adolescent girls in the last grade of high school (n=491) that were selected by a multi-stage sampling method completed a cross-sectional survey (2018) in Tehran city in Iran. Data were collected using questionnaires (standard and researcher-made) by the self-report method and analyzed using descriptive and inferential statistics, including Chi-square, independent t-test, and logistic regression. Results 17.5% of adolescents had healthy, 60.6% had unhealthy, 15.8% had extreme unhealthy weight control behaviors, and 6.1% had no weight control behaviors. 74.8% of adolescents were in the normal body mass index (BMI) percentile. Unhealthy weight control behaviors were observed more than healthy behaviors at all BMI levels. Weight control behaviors had significant relationships with weight control intention (p=0.005), family (p=0.016) and peers (p=0.011) encouragement to weight control, engagement of relatives in weight control behaviors (p=0.016), anxiety (p less then 0.001), and age (p=0.030). BMI has a positive correlation with body weight satisfaction (p less then 0.001) and body weight perception (p less then 0.001). The results of logistic regression showed that increasing anxiety score can increase the possibility of engaging in unhealthy weight control behaviors (odd ratio=1.086, p=0.006). Conclusions Considering that a significant percentage of adolescents have unhealthy and extreme unhealthy weight control behaviors, and some of these behaviors leave irreversible effects on the health of this age group, design, and implementation of educational programs to prevent such behaviors seem imperative.
COVID-19 was first discovered in December 2019 and has since evolved into a pandemic.

To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19.

We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted.

In total, 11 papers were included for review. AI was applied to COVID-19 in four areas diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19.

In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.
In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.
The COVID-19 pandemic has markedly affected renal transplant care. During this time of social distancing, limited in-person visits, and uncertainty, patients and donors are relying more than ever on telemedicine and web-based information. Several factors can influence patients' understanding of web-based information, such as delivery modes (instruction, interaction, and assessment) and social-epistemological dimensions (choices in interactive knowledge building).

The aim of this study was to systemically evaluate the content, delivery modes, and social-epistemological dimensions of web-based information on COVID-19 and renal transplantation at time of the pandemic.

Multiple keyword combinations were used to retrieve websites on COVID-19 and renal transplantation using the search engines Google.com and Google.nl. From 14 different websites, 30 webpages were examined to determine their organizational sources, topics, delivery modes, and social-epistemological dimensions.

The variety of topics and delivery modes was limited. A total of 13 different delivery modes were encountered, of which 8 (62%) were instructional and 5 (38%) were interactional; no assessment delivery modes were observed. No website offered all available delivery modes. The majority of delivery modes (8/13, 62%) focused on individual and passive learning, whereas group learning and active construction of knowledge were rarely encountered.

By taking interactive knowledge transfer into account, the educational quality of eHealth for transplant care could increase, especially in times of crisis when rapid knowledge transfer is needed.
By taking interactive knowledge transfer into account, the educational quality of eHealth for transplant care could increase, especially in times of crisis when rapid knowledge transfer is needed.Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization functions that change over time in varying environments. The DMOP is challenging because it requires the varying Pareto-optimal sets (POSs) to be tracked quickly and accurately during the optimization process. In recent years, transfer learning has been proven to be one of the effective means to solve dynamic multiobjective optimization. However, the negative transfer will lead the search of finding the POS to a wrong direction, which greatly reduces the efficiency of solving optimization problems. Minimizing the occurrence of negative transfer is thus critical for the use of transfer learning in solving DMOPs. In this article, we propose a new individual-based transfer learning method, called an individual transfer-based dynamic multiobjective evolutionary algorithm (IT-DMOEA), for solving DMOPs. Unlike existing approaches, it uses a presearch strategy to filter out some high-quality individuals with better diversity so that it can avoid negative transfer caused by individual aggregation. On this basis, an individual-based transfer learning technique is applied to accelerate the construction of an initial population. The merit of the IT-DMOEA method is that it combines different strategies in maintaining the advantages of transfer learning methods as well as avoiding the occurrence of negative transfer; thereby greatly improving the quality of solutions and convergence speed. The experimental results show that the proposed IT-DMOEA approach can considerably improve the quality of solutions and convergence speed compared to several state-of-the-art algorithms based on different benchmark problems.Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. link3 After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers.
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