Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection.

The autonomous identification of animal births has a significant added value, since it enables for a prompt timely human intervention in the process, protecting the young and the mothers' health, without requiring continuous human surveillance. Wearable inertial sensors have been employed for a variety of animal monitoring applications, thanks to their low cost and the fact that they allow less invasive monitoring process. Alarms triggered by the occurrence of events must be generated close to the events to avoid delays caused by communication latency, which is why this type of mechanism is typically implemented at the network's edge and integrated with existing auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried out commercially for some years, there is no solution for small ruminants, especially goats, where the literature does not even report any attempts. The current work consisted of a first attempt at developing an automatic birth monitor using inertial sensing, as well as detection techniques based on Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus, two concept drift detection techniques and seven kidding detection mechanisms were developed using data classification models. The work also includes the testing and comparison of learning results, both in terms of accuracy and of computational costs of the detection module, for algorithms implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow kidding detection, whereas classification-algorithm-based static learning models do, despite the unbalanced character of the dataset and its reduced size. The learning findings are quite promising in terms of computational cost and its suitability for deployment on edge devices. The algorithm demonstrates behavior changes four hours before kidding and allows for the identification of the kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process with a larger dataset.

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.

Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.

Complex pulmonary aspergilloma treated by cavernostomy / Tratamento do aspergiloma pulmonar complexo por cavernostomia

Objective: To evaluate the effectiveness of cavernostomy in patients with complex fungal balls. Methods: We analyzed the medical records of patients undergoing cavernostomy between January 2005 and May 2013, evaluating: age, gender, preoperative signs and symptoms, predisposing disease, preoperative tests, location of the aspergilloma, etiologic agent, cavernostomy indication, postoperative outcome. Results: Ten patients were male. The mean age was 42.9 years (34-56). The most frequent symptom was repeated pulmonary bleeding. Cavernostomy was proposed for patients at high risk for lung resection. It was performed in 17 patients and all of them had pulmonary tuberculosis sequelae, with cavitations. The indication in all cases was hemoptysis and elimination of phlegm. The cavernostomies were performed in a single surgical procedure. In all 17 patients the cavity was left open after the withdrawal of the mycetoma. In all patients hemoptysis ceased immediately. Operative mortality was 9.5% (1). Conclusion: cavernostomy is an effective treatment alternative in patients at high risk. It may be useful in some patients with complex aspergilloma, irrespective of lung function or bilateral disease. It is technically easy, has low-risk, saves parenchyma, and may be performed in a single operative time. .

Objetivo: avaliar a efetividade da cavernostomia nos pacientes com bola fúngica complexa. Métodos: foram analisados os prontuários de pacientes submetidos à cavernostomia entre janeiro de 2005 e maio de 2013. Foram avaliados: idade, sexo, sinais e sintomas pré-operatórios, doença predisponente, exames pré-operatórios, localização do aspergiloma, agente etiológico, indicação da cavernostomia, evolução pós-operatória. Resultados: dez pacientes eram do sexo masculino. A média de idade foi 42,9 anos (34-56). O sintoma mais frequente foi o sangramento pulmonar repetido. A cavernostomia foi proposta para os pacientes com risco elevado para ressecção pulmonar, foi realizada em 17 pacientes e todos eles apresentavam sequelas de tuberculose pulmonar com lesões cavitárias. A indicação em todos os casos foi hemoptise e eliminação de catarro. As cavernostomias foram realizadas em tempo cirúrgico único. Nos 17 pacientes a caverna foi deixada aberta após a retirada do micetoma. Em todos os pacientes a hemoptise cessou imediatamente. A mortalidade operatória foi 1 (9.5%). Conclusão: a cavernostomia é uma alternativa de tratamento efetivo em pacientes com risco elevado. Pode ser útil em alguns pacientes com aspergiloma complexo, independentemente da função pulmonar ou doença bilateral. É tecnicamente fácil, de baixo risco, poupa parênquima, e realizada em tempo operatório único. .

Complex pulmonary aspergilloma treated by cavernostomy.

OBJECTIVE: To evaluate the effectiveness of cavernostomy in patients with complex fungal balls. METHODS: We analyzed the medical records of patients undergoing cavernostomy between January 2005 and May 2013, evaluating: age, gender, preoperative signs and symptoms, predisposing disease, preoperative tests, location of the aspergilloma, etiologic agent, cavernostomy indication, postoperative outcome. RESULTS: Ten patients were male. The mean age was 42.9 years (34-56). The most frequent symptom was repeated pulmonary bleeding. Cavernostomy was proposed for patients at high risk for lung resection. It was performed in 17 patients and all of them had pulmonary tuberculosis sequelae, with cavitations. The indication in all cases was hemoptysis and elimination of phlegm. The cavernostomies were performed in a single surgical procedure. In all 17 patients the cavity was left open after the withdrawal of the mycetoma. In all patients hemoptysis ceased immediately. Operative mortality was 9.5% (1). CONCLUSION: cavernostomy is an effective treatment alternative in patients at high risk. It may be useful in some patients with complex aspergilloma, irrespective of lung function or bilateral disease. It is technically easy, has low-risk, saves parenchyma, and may be performed in a single operative time.

Probabilistic model checking analysis of palytoxin effects on cell energy reactions of the Na+/K+-ATPase.

Probabilistic model checking (PMC) is a technique used for the specification and analysis of complex systems. It can be applied directly to biological systems which present these characteristics, including cell transport systems. These systems are structures responsible for exchanging ions through the plasma membrane. Their correct behavior is essential for animal cells, since changes on those are responsible for diseases. In this work, PMC is used to model and analyze the effects of the palytoxin toxin (PTX) interactions with one of these systems. Our model suggests that ATP could inhibit PTX action. Therefore, individuals with ATP deficiencies, such as in brain disorders, may be more susceptible to the toxin. We have also used heat maps to enhance the kinetic model, which is used to describe the system reactions. The map reveals unexpected situations, such as a frequent reaction between unlikely pump states, and hot spots such as likely states and reactions. This type of analysis provides a better understanding on how transmembrane ionic transport systems behave and may lead to the discovery and development of new drugs to treat diseases associated to their incorrect behavior.