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A clear case of Guillain-Barre syndrome right after Pfizer COVID-19 vaccine.
Access to the labor market by graduates of the National University of Moquegua is limited by a wide range of socioeconomic and cultural factors. The study aimed to develop a multivariate model to identify the socioeconomic and cultural factors that influence labor insertion of graduates of the National University of Moquegua, 2019. The type of research according to its purpose was basic and the non-experimental cross-sectional design, with a stratified random sample with proportional allocation with a significance level of 5% and a sampling error of 7%. The data collection technique was the survey and two validated and reliable instruments were applied. The population consisted of 537 graduates, with a sample of 121 graduates from six Professional Schools. The results of the application of logistic regression models indicate that the employment status (Wald = 21.179 and p-value = 0.000), basic electricity services (Wald = 4.567 and p-value = 0.033), the preference for movies (Wald = 6,136 and p-value = 0.013), and the communications media TV and radio (Wald = 4.962 and p-value = 0.026) significantly influence the labor insertion of graduates of UNAM. It is concluded that both the working condition, electricity services, the preference for movies and communication media like TV and radio significantly influence the labor insertion of graduates of the National University of Moquegua.Plant breeding experiments require the use of appropriate experimental designs that will efficiently block variation due to wide heterogeneity nature of tropical soils. The primary objective of this study was to assess the effectiveness of eight different alpha-lattice designs relative to randomized complete block design for evaluating 108 genotypes of maize under rainforest agro-ecology. The maize genotypes were field-tested using three replications at two locations. Data were collected on grain yield and other agronomic traits. Data collected were subjected to analysis of variance (ANOVA) assuming randomized complete block design (RCBD) and eight alpha-lattice designs. Pearson's correlation and stepwise multiple regression analyses were used to analyze relationship among different designs and efficiency of the lattice designs over RCBD was computed. selleck Result showed that all the alpha lattice designs except 27 × 4 were effective in evaluating the genotypes for plant height. There was significant difference (p less then 0.001) among genotypes for grain yield only when data were analyzed based on 9 × 12 alpha lattice design. In addition, results showed that the proportion of variation due to blocking and R-square values of the model increased with increase in the number of blocks for grain yield. In contrast, coefficient of variation decreased with increase in the number of blocks. The result showed an increase in efficiency of the alpha lattice designs as the number of blocks increased. It could then be concluded that the more the number of blocks within replicate, the proportion of total variation due to blocking increased, the coefficient of variation (CV) reduced, coefficient of determination (R-square) increased and thus, effectiveness increased. Appropriateness of designs was trait dependent. The 9 × 12 alpha lattice design was identified to be the best in the evaluation of grain yield for the maize genotypes.Hypoxic Ischemic Encephalopathy (HIE) remains a major cause of neurological disability. Early intervention with therapeutic hypothermia improves outcome, but prediction of HIE is difficult and no single clinical marker is reliable. Machine learning algorithms may allow identification of patterns in clinical data to improve prognostic power. Here we examine the use of a Random Forest machine learning algorithm and five-fold cross-validation to predict the occurrence of HIE in a prospective cohort of infants with perinatal asphyxia. Infants with perinatal asphyxia were recruited at birth and neonatal course was followed for the development of HIE. Clinical variables were recorded for each infant including maternal demographics, delivery details and infant's condition at birth. We found that the strongest predictors of HIE were the infant's condition at birth (as expressed by Apgar score), need for resuscitation, and the first postnatal measures of pH, lactate, and base deficit. Random Forest models combining features including Apgar score, most intensive resuscitation, maternal age and infant birth weight both with and without biochemical markers of pH, lactate, and base deficit resulted in a sensitivity of 56-100% and a specificity of 78-99%. This study presents a dynamic method of rapid classification that has the potential to be easily adapted and implemented in a clinical setting, with and without the availability of blood gas analysis. Our results demonstrate that applying machine learning algorithms to readily available clinical data may support clinicians in the early and accurate identification of infants who will develop HIE. We anticipate our models to be a starting point for the development of a more sophisticated clinical decision support system to help identify which infants will benefit from early therapeutic hypothermia.Based on self-determination theory the study seeks to examine influence of teacher autonomy support, structure and relatedness support on amotivation of middle school students. This correlational study based in Indian sub-continent establishes that all three dimensions of teacher support (i.e., teacher autonomy, teacher structure and teacher relatedness support) reduces amotivation however teacher structure have the strongest influence. No gender and age differences were reported for the study. Study highlights the importance of reverse side of motivation (amotivation) and predicates that teacher support is essential not only in increasing motivation but also in reducing amotivation. Training teachers is necessary to increase their ability of providing autonomy support, structure and relatedness support.Global warming is adversely affecting the earth's climate system due to rapid emissions of greenhouse gases (GHGs). Consequently, the world's coastal ecosystems are rapidly approaching a dangerous situation. In this study, we formulate a mathematical model to assess the impact of rapid emissions of GHGs on climate change and coastal ecosystems. Furthermore, we develop a mitigation method involving two control strategies coastal greenbelt and desulfurization. Here, greenbelt is considered in coastal areas to reduce the concentrations of GHGs by absorbing the environmental carbon dioxide (CO2), whereas desulfurization is considered in factories and industries to reduce GHG emissions by controlling the release of harmful sulfur compounds. The model and how it can control the situation are analytically verified. Numerical results of this study are confirmed by comparison with other studies that examine different scenarios. Results show that both control strategies can mitigate GHG concentrations, curtail global warming and to some extent manage climate change.
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