Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods.

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.

A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images.

Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB.

CML-Cardio: a cascade machine learning model to predict cardiovascular disease risk as a primary prevention strategy.

Cardiovascular diseases are among the leading causes of mortality worldwide, with more than 23 million related deaths per year by 2030, according to the World Heart Federation. Although most of these diseases may be prevented, population awareness strategies are still ineffective. In this context, we propose the CML-Cardio tool, a machine learning application to automate the risk classification process of developing CVDs.

Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.

The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil.

Proteomic analysis reveals differentially abundant proteins probably involved in the virulence of amastigote and promastigote forms of Leishmania infantum.

Owing to the importance and clinical diversity of Leishmania infantum, studying its virulence factors is promising for understanding the relationship between parasites and hosts. In the present study, differentially abundant proteins from strains with different degrees of virulence in promastigote and amastigote forms were compared using two quantitative proteomics techniques, differential gel electrophoresis and isobaric mass tag labeling, followed by identification by mass spectrometry. A total of 142 proteins were identified: 96 upregulated and 46 downregulated proteins in the most virulent strain compared to less virulent.

Comparative transcriptomic analysis of antimony resistant and susceptible Leishmania infantum lines.

Background: One of the major challenges to leishmaniasis treatment is the emergence of parasites resistant to antimony. To study differentially expressed genes associated with drug resistance, we performed a comparative transcriptomic analysis between wild-type and potassium antimonyl tartrate (Sb)-resistant Leishmania infantum lines using high-throughput RNA sequencing.

Methods: All the cDNA libraries were constructed from promastigote forms of each line, sequenced and analyzed using STAR for mapping the reads against the reference genome (L.

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks.

Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors.

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions.

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government.

Diversity and genome mapping assessment of disordered and functional domains in trypanosomatids.

The proteins that have structural disorder exemplify a class of proteins which is part of a new frontier in structural biology that demands a new understanding of the paradigm of structure/function correlations. In order to address the location, relative distances and the functional/structural correlation between disordered and conserved domains, consensus disordered predictions were mapped together with CDD domains in Leishmania braziliensis M2904, Leishmania infantum JPCM5, Trypanosoma cruzi CL-Brener Esmeraldo-like, Trypanosoma cruzi Dm28c, Trypanosoma cruzi Sylvio X10, Blechomonas ayalai B08-376 and Paratrypanosoma confusum CUL13 predicted proteomes. Our results depicts the role of protein disorder in key aspects of parasites biology highlighting: a) statistical significant association between genome structural location of protein disordered consensus stretches and functional domains; b) that disordered protein stretches appear in greater percentage at upstream or downstream position of the predicted domain; c) a possible role of structural disorder in several gene expression, control points that includes but are not limited to: i) protein folding; ii) protein transport and degradation; and iii) protein modification.

Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

Leishmaniasis is a wide-spectrum disease caused by parasites from genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies.

Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria.

This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver.