The best result, using the RF classifier, we obtained classification rates higher than 99% of accuracy with 0.843% of standard deviation, 0.999 of the area under the Receiver Operating Characteristics (ROC) curve, 0.995 of Kappa and 0.996 of F-Measure. The experimental results demonstrate that the proposed method is promising and can potentially be used by experts to accurately diagnose dry eye syndrome in tear film images.In this study, we develop a two-layer predictor called iPro2L-PSTKNC, in order to identify various types of promoters in the E. coli genome. It is a big challenge for biological researchers in improving the classification of promoters. The keys to resolving this problem are to effectively formulate the sequence samples concerned. On the first layer, it is predicted whether a sequence is promoter or not. And the second layer identifies which types of promoter it is. Specifically, we propose a novel feature extraction model, named the position specific tendencies of k-mer nucleotide composition (PSTKNC). The ensemble classification SVM performs optimal performance comparing with other classifiers, which gets a promising accuracy and the Matthews correlation coefficient (MCC). Comparing with the performance of state-of-the-art methods, our predictor achieves an evident improvement in almost all of the evaluation index.Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertexwise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise 1) how to design a differentiable exchange protocol (e.g., a one-hop Laplacian smoothing in the original GCN) and 2) how to characterize the tradeoff in complexity with respect to the local updates. In this brief, we show that the state-of-the-art results can be achieved by adapting the number of communication steps independently at every node. In particular, we endow each node with a halting unit (inspired by Graves' adaptive computation time [1]) that after every exchange decides whether to continue communicating or not. We show that the proposed adaptive propagation GCN (AP-GCN) achieves superior or similar results to the best proposed models so far on a number of benchmarks while requiring a small overhead in terms of additional parameters. We also investigate a regularization term to enforce an explicit tradeoff between communication and accuracy. The code for the AP-GCN experiments is released as an open-source library.This article presents the design of an unobtrusive and wireless-enabled blood pressure (BP) monitoring system that is suitable for ambulatory use. By adopting low-profile electromechanical actuators and a compact printed circuit board design, this lightweight device can be worn directly on the occlusive cuff, therefore eliminating the need of a long and obtrusive tubing interconnect between the device and the cuff, as seen in traditional ambulatory BP monitors (ABPM). Instead of executing the BP estimation algorithm directly on the device, the proposed design rather sends the raw oscillometric signal through a Bluetooth Low Energy link, thus granting any Bluetooth-enabled device to gather and process the signal using a dedicated application. This in turn allows to assess several BP estimation algorithms found in the literature without being limited by the device resources. Three of them were tested with the designed prototype and validated with a reference equipment on 11 subjects. Overall, two of the algorithms revealed a mean absolute difference with the reference equipment of less than 5 mmHg and almost zero bias along with a standard deviation of less than 6 mmHg. Reproducibility results shown a mean difference between successive measurements of less than 3.1 mmHg and a standard deviation of less than 2.4 mmHg. The assembled prototype dimensions are 63.8 × 134.8 × 24.8 mm and features an autonomy of 63.1 hours. Comparison with commercial ABPM devices shown that the proposed design is 18% to 33% smaller volume-wise, 5% to 27% weight-wise and height is reduced by 17% to 25%.This paper presents an energy-efficient mm-scale self-contained bidirectional optogenetic neuro-stimulator, which employs a novel highly-linear μLED driving circuit architecture as well as inkjet-printed custom-designed optical μlenses for light directivity enhancement. The proposed current-mode μLED driver performs linear control of optical stimulation for the entire target range ( 10 mA) while requiring the smallest reported headroom, yielding a significant boost in the energy conversion efficiency. A 30.46× improvement in the power delivery efficiency to the target tissue is achieved by employing a pair of printed optical μlenses. The fabricated SoC also integrates two recording channels for LFP recording and digitization, as well as power management blocks. A micro-coil is also embedded on the chip to receive inductive power and our experimental results show a PTE of 2.24 % for the wireless link. The self-contained system including the μLEDs, μlenses and the capacitors required by the power management blocks is sized 6 mm 3 and weighs 12.5 mg. Full experimental measurement results for electrical and optical circuitry as well as in vitro measurement results are reported.Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs) are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq) to predict TF binding specificities. https://www.selleckchem.com/products/BEZ235.html We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell) to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor.