Merkel mobile or portable carcinoma

An ultra-low power low noise analog front end (AFE) is presented in this work, aiming for long-term ECG with clear P-waves for clinical diagnose. The proposed AFE achieves a noise-efficient-factor (NEF) of 2.4 and an input-referred noise of 0.39 Vrms in the best case, which shows 3.7X noise improvements among the state-of-the-art designs. The chopper amplifier applies embedded passive high-pass filters to reduce the influence of the offset and noise. With the digital offset cancellation by the pulse-width modulation wave, the AFE achieves a low input-referred dc offset of 0.4 V among 9 tested chips. A 25 dB programmable gain controlled by nano-ampere current bias and the ADC buffer copes with the amplitude variation among patients within small current cost. A closed-loop dynamic scale ADC with low-power comparator logic prevents the instability and signal loss problem, achieving an SFDR of 71.6 dB. The presented AFE consumes 4 W. And the fabricated chip is demonstrated in a miniature prototype for long-term ECG monitoring application and recorded a clear ECG waveform with visible P-wave. The simultaneously ECG recording with a medical grade 12-lead ECG Holter shows the effective acquisition of the prototype, proofing the better noise performance.It is infeasible to test many different chemotherapy drugs on actual patients in large clinical trials, which motivates computational methods with the ability to learn and exploit associations between drug effectiveness and patient characteristics. This work proposes a machine learning approach to infer robust predictors of drug responses from patient genomic information. Rather than predicting the exact drug response on a given cell line, we introduce an elastic-net regression methodology to compare a drug-cell line pair against an alternative pair. Using predicted pairwise comparisons we rank the effectiveness of different drugs on the same cell line. A total of 173 cell lines and 100 drug responses were used in various settings for training and testing the proposed models. LJI308 By comparing our approach against twelve baseline methods, we demonstrate that it outperforms the state-of-the-art methods in the literature. In contrast to most other methods, the algorithm is able to maintain its high performance even when we use a large number of drugs and few cell lines.Identifying interactions between compound and protein is a substantial part of the drug discovery process. Accurate prediction of interaction relationships can greatly reduce the time of drug development. The uniqueness of our method lies in three aspects1) it represents a compound with a distance matrix. A distance matrix can capture the structural information, compared with the SMILES string. On the other hand, a distance matrix does not require complex data preprocessing for the molecular structure as the molecular graph representation, and is easier to obtain; 2) it uses SPP(Spatial pyramid pooling)-net to extract compound features, which has been successfully applied in image classification; and 3) it extracts protein features through the natural language processing method (doc2vec) to obtain sequence semantic information. We evaluated our method on three benchmark datasets-human, C.elegans, and DUDE and the experimental results demonstrate that our proposed model presents competitive performance against state-of-the-art predictors. We also carried out drug-drug interaction (DDI) experiments to verify the strong potential of distance matrix as molecular characteristics. The source code and datasets are available at https//github.com/lxlsu/SPP_CPI.The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were cell type-specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-cell type representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions, and evolutionary conservation. These representations outperform existing methods in a majority of cell types, while yielding smoother representations along the genomic axis due to their sequential nature.Effective 3D shape retrieval and recognition are challenging but important tasks in computer vision research field, which have attracted much attention in recent decades. Although recent progress has shown significant improvement of deep learning methods on 3D shape retrieval and recognition performance, it is still under investigated of how to jointly learn an optimal representation of 3D shapes considering their relationships. To tackle this issue, we propose a multi-scale representation learning method on hypergraph for 3D shape retrieval and recognition, called multi-scale hypergraph neural network (MHGNN). In this method, the correlation among 3D shapes is formulated in a hypergraph and a hypergraph convolution process is conducted to learn the representations. Here, multiple representations can be obtained through different convolution layers, leading to multi-scale representations of 3D shapes. A fusion module is then introduced to combine these representations for 3D shape retrieval and recognition. The main advantages of our method lie in 1) the high-order correlation among 3D shapes can be investigated in the framework and 2) the joint multi-scale representation can be more robust for comparison. Comparisons with state-of-the-art methods on the public ModelNet40 dataset demonstrate remarkable performance improvement of our proposed method on the 3D shape retrieval task. Meanwhile, experiments on recognition tasks also show better results of our proposed method, which indicate the superiority of our method on learning better representation for retrieval and recognition.