India collided with Asia at ~56 Ma in Tibet: Evidence fromstratigraphy and palaeontology

Abstract HKT-ISTP 2013 A

A Wireless Sensor Network-Speech Recognition Scheme Using Deployments of Multiple Kinect Microphone Array-Sensors

Speech recognition has successfully been utilized in lots of applications recently. With the development of the Kinect sensor device from Microsoft, speech recognition could be further promoted to be used in an ubiquitous environment where a wireless sensor network using Kinect sensors is deployed. This study develops a wireless sensor network (WSN)-speech recognition scheme using deployments of multiple Kinect microphone-array sensors. Presented speech recognition by Kinect-WSN could effectively capture the acoustic data made from the talking speaker and then perform the corresponding voice command control on certain target. In this study, different strategies to deploy multiple Kinect microphone-array sensors for constructing an ubiquitous Kinect-WSN speech recognition environment are investigated. Several different acoustic sensing data fusion methods are also explored for achieving superior performance on Kinect-WSN speech recognition. The presented method in this paper is evaluated the efficiency and effectiveness in an 5m×5m laboratory environment in which any of four test speakers is to make the voice command anywhere. Developed Kinect microphone array sensor-deployed WSN speech recognition in this work is finely utilized in various different applications in control

Broadband wavelength conversion in a silicon vertical-dual-slot waveguide

We propose a silicon waveguide structure employing silica-filled vertical-dual slots for broadband wavelength conversion, which can be fabricated using simple silicon-on-insulator technology. We demonstrate group-velocity dispersion tailoring by varying the width of the core, the slots and the side strips, and put forward a method to achieve spectrally-flattened near-zero anomalous group-velocity dispersion at telecom wavelengths. A proposed structure provides a group-velocity dispersion parameter β2 of −60 ps2/km with an e ective mode area Aeff of 0.075 µm2 at 1550 nm. This structure is predicted to significantly broaden the bandwidth of wavelength conversion via four-wave mixing, which is validated with experimentally measured 3 dB bandwidth of 76 nm

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus.

MicroRNAs (miRNAs) are small noncoding RNAs that modulate the cellular transcriptome at the post-transcriptional level. miRNA plays important roles in different disease manifestation, including type 2 diabetes mellitus (T2DM). Many studies have characterized the changes of miRNAs in T2DM, a complex systematic disease; however, few studies have integrated these findings and explored the functional effects of the dysregulated miRNAs identified. To investigate the involvement of miRNAs in T2DM, we obtained and analyzed all relevant studies published prior to 18 October 2016 from various literature databases. From 59 independent studies that met the inclusion criteria, we identified 158 dysregulated miRNAs in seven different major sample types. To understand the functional impact of these deregulated miRNAs, we performed targets prediction and pathway enrichment analysis. Results from our analysis suggested that the altered miRNAs are involved in the core processes associated with T2DM, such as carbohydrate and lipid metabolisms, insulin signaling pathway and the adipocytokine signaling pathway. This systematic survey of dysregulated miRNAs provides molecular insights on the effect of deregulated miRNAs in different tissues during the development of diabetes. Some of these miRNAs and their mRNA targets may have diagnostic and/or therapeutic utilities in T2DM

Long-term nitrogen fertilization decreased the abundance of inorganic phosphate solubilizing bacteria in an alkaline soil

Inorganic phosphate solubilizing bacteria (iPSB) are essential to facilitate phosphorus (P) mobilization in alkaline soil, however, the phylogenetic structure of iPSB communities remains poorly characterized. Thus, we use a reference iPSB database to analyze the distribution of iPSB communities based on 16S rRNA gene illumina sequencing. Additionally, a noval pqqC primer was developed to quantify iPSB abundance. In our study, an alkaline soil with 27-year fertilization treatment was selected. The percentage of iPSB was 1.10 similar to 2.87% per sample, and the dominant iPSB genera were closely related to Arthrobacter, Bacillus, Brevibacterium and Streptomyces. Long-term P fertilization had no significant effect on the abundance of iPSB communities. Rather than P and potassium (K) additions, long-term nitrogen (N) fertilization decreased the iPSB abundance, which was validated by reduced relative abundance of pqqC gene (pqqC/16S). The decreased iPSB abundance was strongly related to pH decline and total N increase, revealing that the long-term N additions may cause pH decline and subsequent P releases relatively decreasing the demands of the iPSB community. The methodology and understanding obtained here provides insights into the ecology of inorganic P solubilizers and how to manipulate for better P use efficiency

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks

GenomeVIP: A cloud platform for genomic variant discovery and interpretation

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.</jats:p