The behavior of potassium ion (K+) establishes the repolarization and hyperpolarization states in the cell membrane of trabecular meshwork cells. One of the main proteins that controls this ion is calcium-dependent potassium maxi-channels BKca, their malfunction evoked by tyrosine kinases, destabilize the control of aqueous humor flow and the increase of intraocular pressure, responsible of glaucoma. In the present work, the ionic behavior of ion K+ of trabecular cells is detailed, when genistein and tyrphostin-51 are applied in culture cells. The flow behavior is described by means of mathematical expressions based on exponential functions and equations of line. Genistein and tyrphostin-51 drugs are compared in their efficiency of inhibiting tyrosine kinases from the mathematical functions provided by the proposed method. The present study describes the ion behavior regarding K+ potassium which is controlled by maxi-channels BKca that come from trabecular meshwork when genistein and tyrphostin-51 are applied in culture cells. The behavior of this flow is described by mathematical expressions. Thus, inhibitor drugs effect and characteristic time behavior are compared using their mathematical function.

In this paper, we propose Long-Short Term Memory (LSTM)-based mosquito’s genus classification, in which the time-frequency features are extracted from the wingbeat sound of mosquitos of three genera, Aedes, Anopheles and Culex. The extracted features are fed into the proposed LSTM-based classifier. We evaluated three time-frequency features, which are: Mel Spectrogram, Log-Mel spectrogram, and Mel-frequency Cepstral Coefficients (MFCC). The proposed scheme is composed by two LSTM layers and one Fully Connected layer connected to a SoftMax activation function. The classification accuracies using the three features are 92.97(±0.2)%, 96.71(±0.2)% and 96.65(±0.2)%, respectively. The Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) for each feature are also obtained, which are 0.9944, 0.9986 and 0.9987, respectively. The proposed classifier requires approximately 62,000 trainable parameters. This number is much smaller than that required for the state-of-arts CNNs, such as AlexNet and Vgg16. This compact configuration of the proposed scheme takes advantage of the mobile and IoT implementation, because the number of trainable parameters is directly proportional to the amount of memory and CPU required.