Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction.

In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Publicly available data sets of COVID-19 cases for several countries in the world were utilized to test the proposed approach. Simulation results of the COVID-19 data show the potential of the approach to outperform other aggregators in the literature.

Enhancing the one health initiative by using whole genome sequencing to monitor antimicrobial resistance of animal pathogens: Vet-LIRN collaborative project with veterinary diagnostic laboratories in United States and Canada.

BACKGROUND: Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs). RESULTS: We found that most Salmonella isolates (46/69, 67%) had no known resistance genes. Several isolates from both food and companion animals, however, showed genetic relatedness to isolates from humans. For pathogenic E. coli, no resistance genes were identified in 60% (38/63) of the isolates. Diverse resistance patterns were observed, and one of the isolates had predicted resistance to fluoroquinolones and cephalosporins, important antibiotics in human and veterinary medicine. For S. pseudintermedius, we observed a bimodal distribution of resistance genes, with some isolates having a diverse array of resistance mechanisms, including the mecA gene (19/60, 32%). CONCLUSION: The findings from this study highlight the critical importance of veterinary diagnostic laboratory data as part of any national antimicrobial resistance surveillance program. The finding of some highly resistant bacteria from companion animals, and the observation of isolates related to those isolated from humans demonstrates the public health significance of incorporating companion animal data into surveillance systems. Vet-LIRN will continue to build the infrastructure to collect the data necessary to perform surveillance of resistant bacteria as part of fulfilling its mission to advance human and animal health. A One Health approach to AMR surveillance programs is crucial and must include data from humans, animals, and environmental sources to be effective.

[The cultural psychiatry in Latin America]. / La Psiquiatría Cultural en América Latina.

This paper presents only some of the most important contributions in the development of cultural psychiatry in Latin America. The continental efforts to understand the role that culture plays in the manifestation and treatment of mental disorders have been fruitful. The authors included are: Fernando Pagés of Argentina; Mario G. Hollweg of Bolivia; Rubim Alvaro de Pinho and Adalberto Barreto of Brazil; Carlos A. Leon and Carlos A. Uribe of Colombia; Antonio José A. Bustamante and Santa Cruz de Cuba, Carlos Leon Andrade of Ecuador, Guatemala Cristina Chavez; Sergio Villasenor J. Bayardo of Mexico; Carlos A. Seguin, Hermilio Valdizán and Javier Mariátegui in Peru; Y. Bespaldi of Consens of Uruguay; Rojas and Carlos Malpica and Jacqueline Briceño Clarac of Venezuela.

Blood Pressure Classification Using the Method of the Modular Neural Networks.

In this paper, we present a new model based on modular neural networks (MNN) to classify a patient's blood pressure level (systolic and diastolic pressure and pulse). Tests are performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods. The modular neural network architecture is formed by three modules. In the first module we consider the diastolic pressure data; in the second module we use details of the systolic pressure; in the third module, pulse data is used and the response integration is performed with the average method. The goal is to design the best MNN architecture for achieving an accurate classification. The results of the model show that MNN presents an excellent classification for blood pressure. The contribution of this work is related to helping the cardiologist in providing a good diagnosis and patient treatment and allows the analysis of the behavior of blood pressure in relation to the corresponding diagnosis, in order to prevent heart disease.

La Psiquiatría Cultural en América Latina / [The cultural psychiatry in Latin America].

This paper presents only some of the most important contributions in the development of cultural psychiatry in Latin America. The continental efforts to understand the role that culture plays in the manifestation and treatment of mental disorders have been fruitful. The authors included are: Fernando Pagés of Argentina; Mario G. Hollweg of Bolivia; Rubim Alvaro de Pinho and Adalberto Barreto of Brazil; Carlos A. Leon and Carlos A. Uribe of Colombia; Antonio José A. Bustamante and Santa Cruz de Cuba, Carlos Leon Andrade of Ecuador, Guatemala Cristina Chavez; Sergio Villasenor J. Bayardo of Mexico; Carlos A. Seguin, Hermilio Valdizán and Javier Mariátegui in Peru; Y. Bespaldi of Consens of Uruguay; Rojas and Carlos Malpica and Jacqueline Briceño Clarac of Venezuela.