Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series.

The prediction of the number of infected and dead due to COVID-19 has challenged scientists and government bodies, prompting them to formulate public policies to control the virus' spread and public health emergency worldwide. In this sense, we propose a hybrid method that combines the SIRD mathematical model, whose parameters are estimated via Bayesian inference with a seasonal ARIMA model. Our approach considers that notifications of both, infections and deaths are realizations of a time series process, so that components such as non-stationarity, trend, autocorrelation and/or stochastic seasonal patterns, among others, must be taken into account in the fitting of any mathematical model.

Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

Protein-protein interactions govern all molecular processes for living organisms, even those involved in pathogen infection. Pathogens such as virus, bacteria, and parasites contain proteins that help the pathogen to attach, penetrate, and settle inside the target cell. Thus, it is necessary to know the regions in pathogenic proteins that interact with host cell receptors.

R Script Approach to Infer Infection Mechanisms From Microarrays and Domain-Domain Protein Interactions.

Pathogen-host protein-protein interaction systems examine the interactions between the protein repertoires of 2 distinct organisms. Some of these pathogen proteins interact with the host protein system and may manipulate it for their own advantages. In this work, we designed an R script by concatenating 2 functions called rowDM and rowCVmed to infer pathogen-host interaction using previously reported microarray data, including host gene enrichment analysis and the crossing of interspecific domain-domain interactions.

MSCA: a spectral comparison algorithm between time series to identify protein-protein interactions.

Background: The interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive.

Cluster analysis identifies aminoacid compositional features that indicate Toxoplasma gondii adhesin proteins.

Toxoplasma gondii invade host cells using a multi-step process that depends on the regulated secretion of adhesions. To identify key primary sequence features of adhesins in this parasite, we analyze the relative frequency of individual amino acids, their dipeptide frequencies, and the polarity, polarizability and Van der Waals volume of the individual amino acids by using cluster analysis. This method identified cysteine as a key amino acid in the Toxoplasma adhesin group.

Analysis of sandflies (Diptera: Psychodidae) in Barra do Garças, State of Mato Grosso, Brazil, and the influence of environmental variables on the vector density of Lutzomyia longipalpis (Lutz & Neiva, 1912).

Introduction: Leishmaniasis is an infectious and parasitic zoonotic, non-contagious, vector-borne disease caused by protozoa of the genus Leishmania. In Brazil, the major vector of Leishmania (Leishmania) infantum chagasi (Cunha & Chagas, 1934) is Lutzomyia longipalpis. Barra do Garças, State of Mato Grosso, was designated as a priority area by the Brazilian Ministry of Health for american visceral leishmaniasis, and it is important to identify the vector species present in this municipality.