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Schistosomiasis is a neglected tropical disease that is prevalent in low- and middle-income countries. There are five human pathogenic species, of which Schistosoma haematobium, Schistosoma mansoni and Schistosoma japonicum are the most prevalent worldwide and cause the greatest burden of disease in terms of mortality and morbidity. In addition, hybrid schistosomes have been identified through molecular analysis. Human infection occurs when cercariae, the larval form of the parasite, penetrate the skin of people while bathing in contaminated waters such as lakes and rivers. Schistosomiasis can cause both urogenital and intestinal symptoms. Urogenital symptoms include haematuria, bladder fibrosis, kidney damage, and an increased risk of bladder cancer. Intestinal symptoms may include abdominal pain, sometimes accompanied by diarrhoea and blood in the stool. Schistosomiasis affects more than 250 million people and causes approximately 70 million Disability-Adjusted Life Years (DALYs), mainly in Africa, South America, and Asia. To control infection, it is essential to establish sensitive and specific diagnostic tests for epidemiological surveillance and morbidity reduction. This review provides an overview of schistosomiasis, with a focus on available diagnostic tools for Schistosoma spp. Current molecular detection methods and progress in the development of new diagnostics for schistosomiasis infection are also discussed. [ABSTRACT FROM AUTHOR]

The forecasts of electricity and heating demands are key inputs for the efficient design and operation of energy systems serving urban districts, buildings, and households. Their accuracy may have a considerable effect on the selection of the optimization approach and on the solution quality. In this work, we describe a supervised learning approach based on shallow Artificial Neural Networks to develop an accurate model for predicting the daily hourly energy consumption of an energy district 24 h ahead. Predictive models are generated for each one of the two considered energy types, namely electricity and heating. Single-layer feedforward neural networks are trained with the efficient and robust decomposition algorithm DEC proposed by Grippo et al. on a data set of historical data, including, among others, carefully selected information related to the hourly energy consumption of the energy district and the hourly weather data of the region where the district is located. Three different case studies are analyzed: a medium-size hospital located in the Emilia-Romagna Italian region, the whole Politecnico di Milano University campus, and a single building of a department belonging to the latter. The computational results indicate that the proposed method with enriched data inputs compares favorably with the benchmark forecasting and Machine Learning techniques, namely, ARIMA, Support Vector Regression and long short-term memory networks. [ABSTRACT FROM AUTHOR]

Scutellarein is a component of Scutellaria, recently known as a potent cytotoxic agent on human leukaemia cells. The aim of this study was the synthesis of scutellarein and its methylated derivative. The new features are the innovating method to afford flavones from flavanones and the A-ring regioselective bromination step that lead to the target molecule by a facile and high-yielding pathway. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the learning problem of multilayer perceptrons (MLPs) formulated as the problem of minimizing a smooth error function. As well known, the learning problem of MLPs can be a difficult nonlinear nonconvex optimization problem. Typical difficulties can be the presence of extensive flat regions and steep sided valleys in the error surface, and the possible large number of training data and of free network parameters. We define a wide class of batch learning algorithms for MLP, based on the use of block decomposition techniques in the minimization of the error function. The learning problem is decomposed into a sequence of smaller and structured minimization problems in order to advantageously exploit the structure of the objective function. Theoretical convergence results are established, and a specific algorithm is constructed and evaluated through an extensive numerical experimentation. The comparisons with the state-of-the-art learning algorithms show the effectiveness of the proposed techniques.