Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study.

Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R2 values were obtained, showing that in the union of the two databases, a good predictive model is obtained.

In silico design of a polypeptide as a vaccine candidate against ascariasis.

Ascariasis is the most prevalent zoonotic helminthic disease worldwide, and is responsible for nutritional deficiencies, particularly hindering the physical and neurological development of children. The appearance of anthelmintic resistance in Ascaris is a risk for the target of eliminating ascariasis as a public health problem by 2030 set by the World Health Organisation. The development of a vaccine could be key to achieving this target. Here we have applied an in silico approach to design a multi-epitope polypeptide that contains T-cell and B-cell epitopes of reported novel potential vaccination targets, alongside epitopes from established vaccination candidates. An artificial toll-like receptor-4 (TLR4) adjuvant (RS09) was added to improve immunogenicity. The constructed peptide was found to be non-allergic, non-toxic, with adequate antigenic and physicochemical characteristics, such as solubility and potential expression in Escherichia coli. A tertiary structure of the polypeptide was used to predict the presence of discontinuous B-cell epitopes and to confirm the molecular binding stability with TLR2 and TLR4 molecules. Immune simulations predicted an increase in B-cell and T-cell immune response after injection. This polypeptide can now be validated experimentally and compared to other vaccine candidates to assess its possible impact in human health.

A reverse vaccinology approach identifies putative vaccination targets in the zoonotic nematode Ascaris.

Ascariasis is the most prevalent helminthic disease affecting both humans and pigs and is caused by the roundworms Ascaris lumbricoides and Ascaris suum. While preventive chemotherapy continues to be the most common control method, recent reports of anthelminthic resistance highlight the need for development of a vaccine against ascariasis. The aim of this study was to use a reverse vaccinology approach to identify potential vaccine candidates for Ascaris. Three Ascaris proteomes predicted from whole-genome sequences were analyzed. Candidate proteins were identified using open-access bioinformatic tools (e.g., Vacceed, VaxiJen, Bepipred 2.0) which test for different characteristics such as sub-cellular location, T-cell and B-cell molecular binding, antigenicity, allergenicity and phylogenetic relationship with other nematode proteins. From over 100,000 protein sequences analyzed, four transmembrane proteins were predicted to be non-allergen antigens and potential vaccine candidates. The four proteins are a Piezo protein, two voltage-dependent calcium channels and a protocadherin-like protein, are all expressed in either the muscle or ovaries of both Ascaris species, and all contained high affinity epitopes for T-cells and B-cells. The use of a reverse vaccinology approach allowed the prediction of four new potential vaccination targets against ascariasis in humans and pigs. These targets can now be further tested in in vitro and in vivo assays to prove efficacy in both pigs and humans.

Contamination of Soil, Water, Fresh Produce, and Bivalve Mollusks with Toxoplasma gondii Oocysts: A Systematic Review.

Toxoplasma gondii is a major foodborne pathogen capable of infecting all warm-blooded animals, including humans. Although oocyst-associated toxoplasmosis outbreaks have been documented, the relevance of the environmental transmission route remains poorly investigated. Thus, we carried out an extensive systematic review on T. gondii oocyst contamination of soil, water, fresh produce, and mollusk bivalves, following the PRISMA guidelines. Studies published up to the end of 2020 were searched for in public databases and screened. The reference sections of the selected articles were examined to identify additional studies. A total of 102 out of 3201 articles were selected: 34 articles focused on soil, 40 focused on water, 23 focused on fresh produce (vegetables/fruits), and 21 focused on bivalve mollusks. Toxoplasma gondii oocysts were found in all matrices worldwide, with detection rates ranging from 0.09% (1/1109) to 100% (8/8) using bioassay or PCR-based detection methods. There was a high heterogeneity (I2 = 98.9%), which was influenced by both the sampling strategy (e.g., sampling site and sample type, sample composition, sample origin, season, number of samples, cat presence) and methodology (recovery and detection methods). Harmonized approaches are needed for the detection of T. gondii in different environmental matrices in order to obtain robust and comparable results.