Staphylococcus spp. have been associated with cases of healthcare associated infections due to their high incidence in isolates from the hospital environment and their ability to cause infections in immunocompromised patients; synthesize biofilms on medical instruments, in the case of negative coagulase species; and change in genetic material, thus making it possible to disseminate genes that code for the acquisition of resistance mechanisms against the action of antibiotics. This study evaluated the presence of blaZ, femA, and mecA chromosomal and plasmid genes of Staphylococcus spp. using the qPCR technique. The results were associated with the phenotypic expression of resistance to oxacillin and penicillin G. We found that the chromosomal femA gene was present in a greater proportion in S. intermedius when compared with the other species analyzed, while the plasmid-borne mecA gene was prevalent in the S. aureus samples. The binary logistic regression performed to verify the association among the expression of the genes analyzed and the acquisition of resistance to oxacillin and penicillin G were not significant in any of the analyses, p > 0.05.

Water flow forecasts are an essential information for energy production, management and hydropower control. Advanced actions to optimize electricity production can be taken based on predicted information. This work proposes an ensemble strategy using recurrent neural networks to generate a forecast of water flow at Jirau Hydroelectric Power Plant (HPP), installed on the Madeira River in Brazil. The ensemble strategy consists of combining three long short-term memory (LSTM) networks that model the Madeira River and two of its tributaries: Mamoré and Abunã rivers. The historical data from streamflow of the Madeira river and its tributaries are used to validate the ensemble LSTM model, where each time series of river tributaries are modeled separated by LSTM models and the result used as input for another LSTM model in order to forecast the streamflow of the main river. The experimental results present low errors for training and test sets for individual LSTM networks and ensemble model. In addition, these results were compared with the operational forecasts performed by Jirau HPP. The proposed model showed better accuracy in four of the five scenarios tested, which indicates a promising approach to be explored in water flow forecasting based on river tributaries.

The water flow is an important information for the study of the energy production as well as management and hydropower control. The forecast of future information scenarios allows to take advanced actions in order to optimize electricity generation. This work proposes a model to generate a forecast of water flow based on a Recurrent Neural Network, more specifically the long short-term memory (LSTM) type. The dataset used to validate the LSTM model is obtained from flow history of Jirau Hydroelectric Power Plant, installed on the Madeira River in the state of Rondônia, Brazil. The model was trained and tested on one-day time-step resolution. Experimental results shows a lower error according its predictive capacity. The resulted forecast was evaluated by operating staff ex-perts, from Jirau Hydroelectric Power Plant, which attested the results can be used in real operation scenario. It is concluded that LSTM model is a good strategy for the forecast of water flow for the study of hydroelectric turbine efficiency.

Monitoring and management of water levels has become an essential task in obtaining hydroelectric power. Activities such as water resources planning, supply basin management and flood forecasting are mediated and defined through its monitoring. Measurements, performed by sensors installed on the river facilities, are used for precisely information about water level estimations. Since weather conditions influence the results obtained by these sensors, it is necessary to have redundant approaches in order to maintain the high accuracy of the measured values. Staff gauge monitored by conventional cameras is a common redundancy method to keep track of the measurements. However, this method has low accuracy and is not reliable once it is monitored by human eyes. This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. To that end, three models of neural networks were compared: the residual networks (ResNet50), a MobileNetV2 and a proposed model of convolutional neural network (CNN). The results showed that ResNet50 and MobileNetV2 present inferior results compared to the proposed CNN.

Background: Recurrent tonsillitis is one of the most common diseases in childhood, caused many times by ß-lactam-resistant S. aureus. The objective of this study was to investigate an alternative method to identify resistance to oxacillin/cefoxitin in S. aureus from hospitalized children with recurrent tonsillitis. Methods: The samples of S. aureus came from patients with recurrent tonsillitis and were used in 16S rRNA sequencing and an antibiogram test for identification and verifying resistance, after which HSI methodology were applied for separation of S. aureus resistances. Results: The S. aureus isolated showed sensitivity to oxacillin/cefoxitin and the diagnostic images show a visual description of the resistance different groups formed, that may be related to sensitivity and resistance to oxacillin/cefoxitin, characterizing the MRSA S. aureus. Conclusions: Samples that showed phenotypic resistance to oxacillin/cefoxitin were clearly separated from samples that did not show this resistance. A PLS-DA model predicted the presence of resistance to oxacillin/cefoxitin in S. aureus samples and it was possible to observe the pixels classified as MRSA. The HSI was able to successfully discriminate samples in replicas that were sensitive and resistant, based on the calibration model it received.