Optofluidic Microring Dye Laser

We demonstrated, for the first time, an optofluidic microring dye laser on a monolithic poly(dimethylsiloxane) (PDMS) chip. Laser threshold of 9.2nJ was obtained with a single mode liquid-core waveguide based microring cavity

Naive Bayes algorithm for Twitter sentiment analysis and its implementation in MapReduce

Data has been growing exponentially in recent years. With the development of information highway, data can be generated and collected very fast, and the data is so large that it has exceeded the limit of our conventional processing methods and applications. The social network is one of many data explosion areas. Among all social network medias, Twitter has become one of the most important platforms to share and communicate with friends. Sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document, and the sentiment analysis on Twitter has also been used as a valid indicator of stock prices in the past. Naive Bayes is an algorithm to perform sentiment analysis. MapReduce programming model provides a simple and powerful model to implement distributed applications without having deeper knowledge of parallel programming. When a new hypothetical MapReduce sentiment analysis system is built to provide certain performance goal, we are lack of the benchmark and the traditional trial-and-error solution is extremely time-consuming and costly. In this thesis we implemented a prototype system using Naive Bayes to find the correlation between the geographical sentiment on Twitter and the stock price behavior of companies. Also we implemented the Naive Bayes sentiment analysis algorithm in MapReduce model based on Hadoop, and evaluated the algorithm on large amount of Twitter data with different metrics. Based on the evaluation results, we provided a comprehensive MapReduce performance prediction model for Naive Bayes based sentiment analysis algorithm. The prediction model can predict task execution performance within a window, and can also be used by other MapReduce systems as a benchmark in order to improve the performance

Computational protein structure prediction using deep learning

Protein structure prediction is of great importance in bioinformatics and computational biology. Over the past 30 years, many machine learning methods have been developed for this problem in homology-based and ab-initio approaches. Recently, deep learning has been successfully applied and has outperformed previous methods. Deep learning methods could effectively handle high dimensional feature inputs in modeling the complex mapping from protein primary amino acid sequences to protein 2-D or 3-D structures. In this dissertation, new deep learning methods and deep learning networks have been proposed for three problems in protein structure prediction: loop modeling, contact map prediction, and contact map refinement. They have been implemented in the state-of-the-art MUFOLD software and obtained significant performance improvement. The goal of loop modeling is to predict the conformation of a relatively short stretch of protein backbone. A new method based on Generative Adversarial Network (GAN), called MUFOLD-LM, is proposed. The protein 3-D structure can be represented using the 2-D distance map of C [subscript alpha] atoms. The missing region in the structure will be a missing region in the distance map correspondingly. Our network uses the Generator Network to fill in the missing regions in the distance map based on the context, and the Discriminator Network will take both the predicted complete distance map and the ground truth as input to distinguish between them. The method utilizes both the features and context of the missing loop region to make better prediction of the 3-D structure of the loop region. In experiments using commonly used benchmark datasets 8-Res and 12-Res, MUFOLD-LM outperformed previous methods significantly, up to 43.9 [percent] and 4.13 [percent] in RMSD, respectively. To the best of our knowledge, it is the first successful GAN application in protein structure prediction. The goal of contact map prediction is to predict whether the distance between two C [subscript beta] atoms (C [subscript alpha] for Glycine) in a protein falls within a certain threshold. It can help to determine the global s"tructure of a protein in order to assist the 3D modeling process. In this work, a new two-stage multi-branch neural network based on Fully Convolutional Network and Dilated Residual Network, called MUFOLD_Contact, is proposed. It formulates the problem as a pixel-wise regression and classification problem. The first stage predicts distance maps for short-, medium-, and long-range residue pairs. The second stage takes the predicted distances from stage 1 along with other features as input to predict a binary contact map. The method utilizes the distance distribution information in the feature set to improve the binary prediction results. In experiments using CASP13 targets, the new method outperformed single stage networks and is comparable with the best existing tools. In addition to predicting contact directly using deep neural networks, a new method, called TPCref (Template Prediction Correction refinement), is proposed to refine and improve the prediction results of a contact predictor using protein templates. Based on the idea of collaborative filtering from recommendation system, TPCref first finds multiple template sequences based on the target sequence and uses the templates' structures and the templates' predicted contact map generated by a contact predictor to form a target contact map filter using the idea of collaborative filtering. Then the contact-map filter is used to refine the predicted contact map. In experimental results using recently released PDB proteins, TPCref significantly improved the contact prediction results of existing predictors, improving MUFOLD_Contact, MetaPSICOV, and CCMPred by 5.0 [percent], 12.8 [percent], and 37.2 [percent], respectively. The proposed new methods have been implemented in MUFOLD, a comprehensive platform for protein structure prediction. It provides a rich set of functions, including database generation, secondary and supersecondary structure prediction, beta-turn and gamma-turn prediction, contact map prediction and refinement, protein 3D structure prediction, loop modeling, model quality assessment, and model refinement. In this work, a new modularized MUFOLD pipeline has been designed and developed. Each module is decoupled from each other and provides standard communication protocol interfaces for other programs to call. The modularization provides the capability to easily integrate new algorithms and tools to have a fast iteration during research. In addition, a new web portal for MUFOLD has been designed and implemented to provide online services or APIs of our tools to the community

Mechanically tunable optofluidic distributed feedback dye laser

A continuously tunable optofluidic distributed feedback (DFB) dye laser was demonstrated on a monolithic replica molded poly(dimethylsiloxane) (PDMS) chip. The optical feedback was provided by a phase-shifted higher order Bragg grating embedded in the liquid core of a single mode buried channel waveguide. Due to the soft elastomeric nature of PDMS, the laser frequency could be tuned by mechanically stretching the grating period. In principle, the mechanical tuning range is only limited by the gain bandwidth. A tuning range of nearly 60nm was demonstrated from a single dye laser chip by combining two common dye molecules Rhodamine 6G and Rhodamine 101. Single-mode operation was maintained with less than 0.1nm linewidth. Because of the higher order grating, a single laser, when operated with different dye solutions, can provide tunable light output covering the entire spectrum from near UV to near IR in which efficient laser dyes are available. An array of five DFB dye lasers with different grating periods was also demonstrated on a chip. Such tunable integrated laser arrays are expected to become key components in inexpensive advanced spectroscopy chips

NSNet: A General Neural Probabilistic Framework for Satisfiability Problems

We present the Neural Satisfiability Network (NSNet), a general neural framework that models satisfiability problems as probabilistic inference and meanwhile exhibits proper explainability. Inspired by the Belief Propagation (BP), NSNet uses a novel graph neural network (GNN) to parameterize BP in the latent space, where its hidden representations maintain the same probabilistic interpretation as BP. NSNet can be flexibly configured to solve both SAT and #SAT problems by applying different learning objectives. For SAT, instead of directly predicting a satisfying assignment, NSNet performs marginal inference among all satisfying solutions, which we empirically find is more feasible for neural networks to learn. With the estimated marginals, a satisfying assignment can be efficiently generated by rounding and executing a stochastic local search. For #SAT, NSNet performs approximate model counting by learning the Bethe approximation of the partition function. Our evaluations show that NSNet achieves competitive results in terms of inference accuracy and time efficiency on multiple SAT and #SAT datasets

Efficient inference in Bayes networks as a combinatorial optimization problem

AbstractA number of exact algorithms have been developed in recent years to perform probabilistic inference in Bayesian belief networks. The techniques used in these algorithms are closely related to network structures, and some of them are not easy to understand and implement. We consider the problem from the combinatorial optimization point of view and state that efficient probabilistic inference in a belief network is a problem of finding an optimal factoring given a set of probability distributions. From this viewpoint, previously developed algorithms can be seen as alternative factoring strategies. In this paper, we define a combinatorial optimization problem, the optimal factoring problem, and discuss application of this problem in belief networks. We show that optimal factoring provides insight into the key elements of efficient probabilistic inference, and demonstrate simple, easily implemented algorithms with excellent performance

The efficacy and safety of 10 mg/day vortioxetine compared to placebo for adult major depressive disorder: a meta-analysis

Background: There is a growing interest in vortioxetine in major depressive disorder (MDD). Objectives: This meta-analysis aimed to assess the efficacy and safety of 10 mg/day (mg/d) vortioxetine compared to placebo for MDD in adult.Methods: Eight randomly controlled trials (RCTs) about the treatment of 10 mg/d vortioxetine in adult patients with MDD were identified and 2354 patients were included in meta-analysis.Results: According to the results, 10 mg/d vortioxetine showed significant differences in response rates (OR=1.88, 95% CI=1.40-2.53, P<0.0001), remission rates (OR=1.54, 95% CI=1.27-1.86, P<0.00001), change from baseline in Montgomery-As- berg Depression Rating Scale (MADRS) total score (SMD=-3.50, 95%CI=-4.83 to -2.17, P<0.00001), clinical global Impres- sion-Global Improvement (CGI-I) total score (SMD=-3.40, 95% CI=-4.69 to -2.11, P<0.00001), and change from baseline in Sheehan Disability Scale (SDS) total score (SMD=-2.09, 95% CI=-2.64 to -1.55, P<0.00001). But 10 mg/d vortioxetine was easier induced nausea (OR=4.18, 95% CI=3.21-5.44, P<0.00001) and constipation (OR=1.88, 95% CI=1.14 to 3.09, P=0.01).Conclusion: 10 mg/d vortioxetine was more effective, but easily induced nausea and constipation when compared to placebo for MDD in adult.Keywords: Vortioxetine, major depressive disorder, meta-analysis.