In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

This paper aims to investigate and compare the accuracy of different data mining classification schemes, employing Ensemble Machine Learning Techniques, for the prediction of heart disease. The Cleveland data set for heart diseases, containing 303 instances, has been used as the main database for the training and testing of the developed system. 10-Fold Cross-Validation has been applied in order to increase the amount of data, which would otherwise have been limited. Different classifiers, namely Decision Tree (DT), Naïve Bayes (NB), Multilayer Perceptron (MLP), K-Nearest Neighbor (K-NN), Single Conjunctive Rule Learner (SCRL), Radial Basis Function (RBF) and Support Vector Machine (SVM), have been employed. Moreover, the ensemble prediction of classifiers, bagging, boosting and stacking, has been applied to the dataset. The results of the experiments indicate that the SVM method using the boosting technique outperforms the other aforementioned methods.

Although Colombia has witnessed an important decrease in malaria transmission, the disease remains a public health problem with an estimated ~10 million people currently living in areas with malaria risk and ~61,000 cases reported in 2012. This study aimed to determine and compare the level of knowledge, attitudes and practices (KAP) about malaria in three endemic communities of Colombia to provide the knowledge framework for development of new intervention strategies for malaria elimination.

Malaria transmission in Latin America is generally hypoendemic and unstable, with Plasmodium vivax as the most prevalent species. However, only a few studies have been carried out in areas with low and unstable transmission, whereas the clinical profile of malaria has been broadly described in hyperendemic areas. The pattern of clinical manifestations and laboratory findings in low to moderate endemic areas of Colombia is reported here. A passive surveillance study was conducted between 2011 and 2013 involving 1,328 patients with Plasmodium falciparum, P. vivax or mixed malaria infections attending malaria points-of-care of four malaria endemic-areas with distinct transmission intensities and parasite distribution. Trained physicians recorded clinical symptoms and signs as well as socio-demographic characteristics of study participants. Haematological, biochemical and urine tests were performed at the time of diagnosis. Out of 1,328 cases, 673 (50.7%) were caused by P. vivax; 650 (48.9%) were due to P. falciparum; and five (0.4%) patients had mixed infections (P. falciparum/P. vivax). Most patients (92.5%) presented with uncomplicated malaria characterized by fever, chills, headache, sweating, myalgia/arthralgia and parasitaemia ≤ 20,000 parasites/μL. Fever, tachycardia, pallor and abdominal pain on palpation were more frequent in P. falciparum patients, whereas mild hepatomegaly and splenomegaly were mostly observed with P. vivax. Non-severe anaemia (Hb 7.0-10.9 g/dL) was observed in 20% of the subjects, whereas severe anaemia (Hb < 7.0 g/dL) was present in four patients. Half of the patients presented thrombocytopaenia regardless of parasite species. Leukopaenia, neutrophilia and monocytosis were frequently observed in patients infected with P. falciparum. Mild-to-moderate biochemical alterations were present in ~25% of the patients, particularly abnormal bilirubin in those with P. falciparum and abnormal transaminases in P. vivax malaria patients. Proteinuria was present in ~50% of the patients regardless of parasite species, whereas haemoglobinuria was more common in P. vivax infections. Only 7.5% of the cases were classified as clinically severe malaria, caused by both P. vivax and P. falciparum. The high prevalence of uncomplicated malaria associated with moderate parasitaemia suggests the importance of timely diagnosis and effective treatment and encourages new activities to further decrease complicated malaria cases and mortality.

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the COVID-19 virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this subpopulation, as well as persons with mild disease, are a major contributor in the propagation of the disease. The rapid spread of COVID-19 forced governments around the world to establish and enforce generalized risk mitigation strategies, from lockdowns to guidelines for social distancing, in an effort to minimize community transmission. This created an unprecedented epidemiological situation not properly characterized by existing mathematical models of isolation and quarantine. In this manuscript, we present a mathematical model for community transmission of COVID-19 taking into account asymptomatic carriers and varying degrees of risk mitigation. The main results consist of an exact calculation of the effective reproduction number , and a modeling framework that enables the quantification of the effect of risk mitigation and asymptomatism on community transmission. A computation of is provided using mean parameters. The point estimate of the basic reproduction number is .