Notes

Nanoparticle Shipping and delivery within Prostate gland Malignancies Incorporated within Rats Triggerred by simply Both Nearby or perhaps Whole-Body Home heating.
The sensitivity of CogStack for identifying patients screened was 90% (95% CI 85%, 93%). Of the 203 patients identified by both manual screening and CogStack, the index date matched in 95 (47%) and CogStack was earlier in 94 (47%). In conclusion, analysis of EHR data using NLP could effectively replicate recruitment in a critical care trial, and identify some eligible patients at an earlier stage, potentially improving trial recruitment if implemented in real time.This article proposes a novel iterative weighted group thresholding method for group sparse recovery of signals from underdetermined linear systems. Based on an equivalent weighted group minimization problem with ℓpp-norm (0 less then p ≤ 1), we derive closed-form solutions for a subproblem with respect to some specific values of p when using the proximal gradient method. Then, we design the corresponding algorithmic framework, including stopping criterion and the method of nonmonotone line search, and prove that the solution sequence generated by the proposed algorithm converges under some mild conditions. Moreover, based on the proposed algorithm, we develop a homotopy algorithm with an adaptively updated group threshold. Extensive computational experiments on the simulated and real data show that our approach is competitive with state-of-the-art methods in terms of exact group selection, estimation accuracy, and computation time.Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinforcement learning for personalized dialogue generation (MRPDG). read more Specifically, MRPDG consists of two subtasks 1) an author profiling module that recognizes user characteristics from the input sentence (auxiliary task) and 2) a personalized dialog generation system that generates informative, grammatical, and coherent responses with reinforcement learning algorithms (primary task). Three kinds of rewards are proposed to generate high-quality conversations. We investigate the effectiveness of three widely used reinforcement learning methods [i.e., Q-learning, policy gradient, and actor-critic (AC) algorithm] in a personalized dialog generation system and demonstrate that the AC algorithm achieves the best results on the underlying framework. Comprehensive experiments are conducted to evaluate the performance of the proposed model on two real-life data sets. Experimental results illustrate that MRPDG is able to produce high-quality personalized dialogs for users with different characteristics. Quantitatively, the proposed model can achieve better performance than the compared methods across different evaluation metrics, such as the human evaluation, BiLingual Evaluation Understudy (BLEU), and perplexity.With the commercialization of haptic devices, understanding behavior under various environmental conditions is crucial for product optimization and cost reduction. Specifically, for surface haptic devices, the dependence of the friction force and the electroadhesion effect on the environmental relative humidity and the finger hydration level can directly impact their design and performance. This paper presents the influence of relative humidity on the finger-surface friction force and the electroadhesion performance. Mechanisms including changes to Young's modulus of skin, contact angle change and capillary force were analyzed separately with experimental and numerical methods. Through comparison of the calculated capillary force in this paper and the electroadhesion force calculated in published papers, it was found that electrowetting at high voltage could contribute up to 60% of the total friction force increase in electroadhesion. Therefore, in future design of surface haptic devices, the effect of electrowetting should be considered carefully.Conventional glucose monitoring methods for the growing number of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar level in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This paper presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20μA with an input-referred current noise of 7.3pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal extract six distinct features and finally predicts the blood glucose level using (Support Vector Regression with Fine Gaussian kernel) (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using 180nm CMOS process with a chip area of 4.0mm2 while consuming 1.62mW. The glucose measurement is performed for 200 subjects with R2 of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.This paper considers implementation of artificial neural networks (ANNs) using molecular computing and DNA based on fractional coding. link2 Prior work had addressed molecular two-layer ANNs with binary inputs and arbitrary weights. In prior work using fractional coding, a simple molecular perceptron that computes sigmoid of scaled weighted sum of the inputs was presented where the inputs and the weights lie between [-1,1]. Even for computing the perceptron, the prior approach suffers from two major limitations. First, it cannot compute the sigmoid of the weighted sum, but only the sigmoid of the scaled weighted sum. Second, many machine learning applications require the coefficients to be arbitrarily positive and negative numbers that are not bounded between [-1,1]; such numbers cannot be handled by the prior perceptron using fractional coding. This paper makes four contributions. First molecular perceptrons that can handle arbitrary weights and can compute sigmoid of the weighted sums are presented. Thus, these molecular perceptrons are ideal for regression applications and multi-layer ANNs. A new molecular divider is introduced and is used to compute sigmoid(ax) where a>1. Second, based on fractional coding, a molecular artificial neural network (ANN) with one hidden layer is presented. Third, a trained ANN classifier with one hidden layer from seizure prediction application from electroencephalogram is mapped to molecular reactions and DNA and their performances are presented. Fourth, molecular activation functions for rectified linear unit (ReLU) and softmax are also presented.Protein Secondary Structural Class (PSSC) information is important in investigating further challenges of protein sequences like fold recognition, tertiary structure prediction, and analysis of protein functions for drug discovery. Identification of PSSC using biological methods is time-consuming and cost-intensive. Existing computational models lack in generalization. Hence, predicting PSSC based on protein sequences is still proving to be an uphill task. We proposed an effective, novel and generalized prediction model consisting of a feature modeling and ensemble classifier. The proposed feature modeling extracts discriminating features by leveraging three techniques (i) Embedding (ii) SkipXGram Bi-gram, and (iii) General Statistical (GS) based features. The combined sets of features are trained and classified using an ensemble of three classifiers Support Vector Machine, Random Forest, and Gradient Boosting Machines. The proposed model when assessed on five benchmark datasets, viz. z277, z498, 25PDB, 1189, and FC699, reported an overall accuracy of 93.55%, 97.58%, 81.82%, 81.11%, and 93.93% respectively. The proposed model is further validated on a large-scale updated low similarity dataset, where it achieved an overall accuracy of 81.11%. The proposed generalized model is robust and consistently outperformed several state-of-the-art models on all the five benchmark datasets.The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors.The SVM- and CNNbased methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. link3 These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p less then 0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Sequence alignment enables systematic genome screening against organisms whose glycolytic abilities have been manually curated by experts. Clustering of homology scores helps identify organisms that share common abilities as well as the most promising organisms regarding specific glycolytic abilities. The method was applied to members of the bacterial families Ruminococcaceae, Eubacteriaceae, and Lachnospiraceae, which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera, i.e. 320 unique glycoside hydrolases in 221 metabolic pathways. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus.
Read More: https://www.selleckchem.com/products/c188-9.html