Decoding Brain Activation from Ipsilateral Cortex using ECoG Signals in Humans

Today, learning from the brain is the most challenging issue in many areas. Neural scientists, computer scientists, and engineers are collaborating in this broad research area. With better techniques, we can extract the brain signals by either non-invasive approach such as EEG: electroencephalography), fMRI, or invasive method such as ECoG: electrocorticography), FP: field potential) and signals from single unit. The challenge is, given the brain signals, how can we possibly decipher them? Brain Computer Interfaces, or BCIs, aim at utilizing the brain signals to control prothetic arms or operate devices. Previously almost all the research on BCIs focuses on decoding signals from the contralateral hemisphere to implement BCI systems. However, the loss of functionality in the contralateral cortex often occurs due to strokes, resulting in total failure to motor function of fingers, hands, and limbs contralateral to the damaged hemisphere. Recent studies indicate that the signals from ipsilateral cortex is relevant to the planning phase of motor movements. Therefore, it is critical to find out if human motor movements can be decoded using signals from the ipsilateral cortex. In the thesis, we propose using ECoG signals from the ipsilateral cortex to decode finger movements. To our knowledge, this is the first work that successfully detects finger movements using signals from the ipsilateral cortex. We also investigate the experiment design and decoding directional movements. Our results show high decoding performance. We also show the anatomical feature analysis for ipsilateral cortex in performing motor-associated tasks, and the features are consistent with previous findings. The result reveals promising implications for a stroke relevant BCI

Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition

We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we reconstruct a temporal slice of filterbank features from past and future context frames. The resulting deep contextualized acoustic representations (DeCoAR) are then used to train a CTC-based end-to-end ASR system using a smaller amount of labeled audio data. In our experiments, we show that systems trained on DeCoAR consistently outperform ones trained on conventional filterbank features, giving 42% and 19% relative improvement over the baseline on WSJ eval92 and LibriSpeech test-clean, respectively. Our approach can drastically reduce the amount of labeled data required; unsupervised training on LibriSpeech then supervision with 100 hours of labeled data achieves performance on par with training on all 960 hours directly. Pre-trained models and code will be released online.Comment: Accepted to ICASSP 2020 (oral

Linear Thermodynamics of Rodlike DNA Filtration

Linear thermodynamics transportation theory is employed to study filtration of rodlike DNA molecules. Using the repeated nanoarray consisting of alternate deep and shallow regions, it is demonstrated that the complex partitioning of rodlike DNA molecules of different lengths can be described by traditional transport theory with the configurational entropy properly quantified. Unlike most studies at mesoscopic level, this theory focuses on the macroscopic group behavior of DNA transportation. It is therefore easier to conduct validation analysis through comparison with experimental results. It is also promising in design and optimization of DNA filtration devices through computer simulation.Singapore-MIT Alliance (SMA

Update of TTD: Therapeutic Target Database

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp

A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands

10.1371/journal.pone.0039076PLoS ONE76

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06Acoustic models require a large amount of training data. However, lots of labor is required to annotate the training data for automatic speech recognition. More importantly, the performance of the acoustic model could degenerate during test time, where the conditions of test data differ from the training data in speaker characteristics, channel and recording environment. To compensate for the deviation between training and test conditions, we investigate a graph-based semi-supervised learning approach to acoustic modeling in automatic speech recognition. Graph-based semi-supervised learning (SSL) is a widely used semi-supervised learning method in which the labeled data and unlabeled data are jointly represented as a weighted graph, and the information is propagated from the labeled data to the unlabeled data. The key assumption that graph-based SSL makes is that data samples lie on a low dimensional manifold, where samples that are close to each other are expected to have the same class label. More importantly, by exploiting the relationship between training and test samples, graph-based SSL implicitly adapts to the test data. In this thesis, we address several key challenges in applying graph-based SSL to acoustic modeling. We first investigate and compare several state-of-the-art graph-based SSL algorithms on a benchmark dataset. In addition, we propose novel graph construction methods that allow graph-based SSL to handle variable-length input features. We next investigate the efficacy of graph-based SSL in context of a fully-fledged DNN-based ASR system. We compare two different integration frameworks for graph-based learning. First, we propose a lattice-based late integration framework that combines graph-based SSL with the DNN-based acoustic modeling and evaluate the framework on continuous word recognition tasks. Second, we propose an early integration framework using neural graph embeddings and compare two different neural graph embedding features that capture the information of the manifold at different levels. The embedding features are used as input to a DNN system and are shown to outperform the conventional acoustic feature inputs on several medium-to-large vocabulary conversational speech recognition tasks

Unsupervised submodular subset selection for speech data

We conduct a comparative study on selecting subsets of acous-tic data for training phone recognizers. The data selection problem is approached as a constrained submodular optimiza-tion problem. Previous applications of this approach required transcriptions or acoustic models trained in a supervised way. In this paper we develop and evaluate a novel and entirely unsupervised approach, and apply it to TIMIT data. Results show that our method consistently outperforms a number of baseline methods while being computationally very efficient and requiring no labeling. Index Terms — speech processing, automatic speech recognition, machine learning 1

Determination of the Volatile Composition in Brown Millet, Milled Millet and Millet Bran by Gas Chromatography/Mass Spectrometry

The volatile compounds from brown millet (BM), milled millet (MM) and millet bran (MB) were extracted using simultaneous distillation/extraction with a Likens-Nickerson apparatus. The extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). A total of 65 volatile compounds were identified in all of the samples. Among these compounds, 51, 51 and 49 belonged to BM, MM and MB, respectively. Aldehydes and benzene derivatives were the most numerous among all of the compounds. Three compounds (hexanal, hexadecanoic acid and 2-methylnaphthalene) were dominant in the BM and MM materials. Eight compounds (hexanal, nonanal, (E)-2-nonenal, naphthalene, 2-methylnaphthalene, 1-methylnaphthalene, hexadecanoic acid and 2-pentylfuran) were dominant in the MB materials. Apart from the aromatic molecules, which were present in all fractions, compounds present only in BM, MM or MB were also identified

Characterization of the Key Aroma Compounds in Proso Millet Wine Using Headspace Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry

The volatile compounds in proso millet wine were extracted by headspace solid-phase microextraction (85 μm polyacrylate (PA), 100 μm polydimethylsiloxane (PDMS), 75 μm Carboxen (CAR)/PDMS, and 50/30 μm divinylbenzene (DVB)/CAR/PDMS fibers), and analyzed using gas chromatography-mass spectrometry; the odor characteristics and intensities were analyzed by the odor activity value (OAV). Different sample preparation factors were used to optimize this method: sample amount, extraction time, extraction temperature, and content of NaCl. A total of 64 volatile compounds were identified from the wine sample, including 14 esters, seven alcohols, five aldehydes, five ketones, 12 benzene derivatives, 12 hydrocarbons, two terpenes, three phenols, two acids, and two heterocycles. Ethyl benzeneacetate, phenylethyl alcohol, and benzaldehyde were the main volatile compounds found in the samples. According to their OAVs, 14 volatile compounds were determined to be odor-active compounds (OAV > 1), and benzaldehyde, benzeneacetaldehyde, 1-methyl-naphthalene, 2-methyl-naphthalene, and biphenyl were the prominent odor-active compounds (OAV > 50), having a high OAV. Principal component analysis (PCA) showed the difference of distribution of the 64 volatile compounds and 14 odor-active compounds with four solid-phase microextraction (SPME) fibers