When the Affordable Care Act (ACA) became law in 2010, public opinion of it was narrowly divided and deeply partisan. Our review of 102 nationally representative public opinion polls in the period 2010-19 reveals that opinion remains divided and has shifted in a sustained way at only two points in time in a negative direction following technical problems in the first enrollment period, and in a positive direction after President Donald Trump's election and subsequent Republican repeal efforts. In late 2019 the ACA was more popular than ever, yet partisan divisions have gotten larger rather than smaller. Many core elements of the law remain popular across partisan groups, even as fewer people recognize the ACA as the source of some of these provisions. While Republicans may never embrace the law that is seen as President Barack Obama's legacy, the public's reluctance to see certain benefits taken away will continue to be a roadblock for people who would seek to repeal or dismantle it.PURPOSE The objective of this systematic review was to describe nutrition-related publications on children and adolescents diagnosed with cancer in Brazil. METHODS The methodology followed that of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Medline, LILACS (the Latin American & Caribbean Health Sciences Literature), and Embase were searched in April 2019, and data extraction and rating of methodologic study quality (according to the National Institutes of Health quality score assessment) were performed independently by reviewers. RESULTS Twenty-seven studies met the inclusion criteria, reporting on 3,509 patients from 1994 to 2018. Most of the studies (74%) were of poor quality in methodology and reporting. Different cancer diagnoses were included in 52% of studies, whereas acute leukemia was the exclusive focus in 41%. The majority of the articles (70%) were from institutions in the Southeast Region of Brazil, mainly the state of São Paulo (74%); no publications were from the North Region of the country. Twelve studies addressed nutritional status and body composition, reporting an abundance of malnourished patients in the Brazilian population of children and adolescents with cancer. Six studies on micronutrients pointed to possible deficiencies in this population, with a yet unclear but promising role for supplementation during treatment. CONCLUSION Evidence indicates that there is great interest in the impact of nutrition on childhood cancer treatment and clinical outcomes in Brazil. However, there is a need to focus on high-quality research, particularly with multicentric/national studies. This will help establish research priorities and better planned clinical interventions, adapted to each region of the country.Postpartum weight retention (PPWR) is an important risk factor for long-term obesity. Appetite may be a key factor regulating PPWR. The objectives of this study were to determine the associations between 1) PPWR and appetite, and 2) appetite, lactation, and metabolic characteristics. Data from 49 women at nine months postpartum contributed to this cross-sectional analysis. Energy expenditure was assessed in a whole body calorimetry unit for 24 hours. Appetite sensations were rated using visual analogue scales. Lactation (min/day) was measured using a 3-day breastfeeding diary. PPWR was negatively associated with fullness (β±SE; R2= -2.97 ± 0.72; 0.661; P less then 0.001), and satiety (-2.75 ± 0.81; 0.617; P=0.002), and positively associated with hunger (2.19 ± 1.02; 0.548; P=0.039), prospective food consumption (PFC; 2.19 ± 0.91; 0.562; P=0.021), and composite appetite score (CAS; 0.34 ± 0.09; 0.632; P=0.001). Lactation was associated with higher CAS (39.68 ± 15.56; 0.365; P=0.015), hunger (3.56 ± 1.61; 0.308; P=0.033), and PFC (4.22 ± 1.78; 0.314; P=0.023), and with reduced sensations of fullness (-4.18 ± 1.94; 0.358; P=0.038), and satiety (-3.83 ± 1.87; 0.295; P=0.048). Lactation was associated with appetite, which in turn was related to PPWR. Appetite control should be explored to support postpartum weight management strategies. Novelty bullets • Postpartum weight retention was associated with appetite sensations which were assessed throughout the day under conditions in which energy intake and expenditure were precisely matched. • Lactation and other maternal metabolic factors, including carbohydrate oxidation and physical activity level may play a role in controlling appetite during the postpartum period.The value of training for a data sciences professional is in the eye of the beholder. And dependent on the scope and breadth of that training and the cost and time frame of that training. Value for the employee may differ from value for the employer. The lens is different and value may depend on what lens you look through. Training can be online or on-site, short term with specific focus or longer term with greater breadth and less depth. Career goals should also be considered when determining value. Certification in Spark is not valuable if you do not want to work with Spark. A PhD in management psychology is not as valuable if you do not want to manage people. The fact that training (both certification and degree programs) is valuable is not debatable.  https://www.selleckchem.com/products/cdk2-inhibitor-73.html  Maximizing that value for both employee and employer is always a preferable option. But is it realistic?Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed Rider-based monarch butterfly optimization (Rider-MBO) algorithm. The proposed Rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data.