Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16

Gastric carcinosarcoma with rhabdomyosarcomatous differentiation: a case report and literature review

Gastric carcinosarcoma with rhabdomyosarcomatous differentiation is a rare tumor. Herein, we report the case of a 34-year-old man with a history of dysphagia, upper abdominal fullness, and poor appetite. Endoscopic findings showed a large friable mass that originated from the gastric cardia and lesser curvature of the high body. Consequently, radical total gastrectomy with Roux-en-Y esophagojejunostomy was performed. Histopathological analysis of the resected specimen revealed that the mass had invaded the serosa without regional lymph node metastasis; moreover, the tumor was positive for desmin and myogenin. Finally, we conclude this report with literature review and discussion

Fucosyltransferase 1 and 2 play pivotal roles in breast cancer cells.

FUT1 and FUT2 encode alpha 1, 2-fucosyltransferases which catalyze the addition of alpha 1, 2-linked fucose to glycans. Glycan products of FUT1 and FUT2, such as Globo H and Lewis Y, are highly expressed on malignant tissues, including breast cancer. Herein, we investigated the roles of FUT1 and FUT2 in breast cancer. Silencing of FUT1 or FUT2 by shRNAs inhibited cell proliferation in vitro and tumorigenicity in mice. This was associated with diminished properties of cancer stem cell (CSC), including mammosphere formation and CSC marker both in vitro and in xenografts. Silencing of FUT2, but not FUT1, significantly changed the cuboidal morphology to dense clusters of small and round cells with reduced adhesion to polystyrene and extracellular matrix, including laminin, fibronectin and collagen. Silencing of FUT1 or FUT2 suppressed cell migration in wound healing assay, whereas FUT1 and FUT2 overexpression increased cell migration and invasion in vitro and metastasis of breast cancer in vivo. A decrease in mesenchymal like markers such as fibronectin, vimentin, and twist, along with increased epithelial like marker, E-cadherin, was observed upon FUT1/2 knockdown, while the opposite was noted by overexpression of FUT1 or FUT2. As expected, FUT1 or FUT2 knockdown reduced Globo H, whereas FUT1 or FUT2 overexpression showed contrary effects. Exogenous addition of Globo H-ceramide reversed the suppression of cell migration by FUT1 knockdown but not the inhibition of cell adhesion by FUT2 silencing, suggesting that at least part of the effects of FUT1/2 knockdown were mediated by Globo H. Our results imply that FUT1 and FUT2 play important roles in regulating growth, adhesion, migration and CSC properties of breast cancer, and may serve as therapeutic targets for breast cancer

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

Genomic sequencing and analyses of Lymantria xylina multiple nucleopolyhedrovirus

<p>Abstract</p> <p>Background</p> <p>Outbreaks of the casuarina moth, <it>Lymantria xylina </it>Swinehoe (Lepidoptera: Lymantriidae), which is a very important forest pest in Taiwan, have occurred every five to 10 years. This moth has expanded its range of host plants to include more than 65 species of broadleaf trees. LyxyMNPV (<it>L. xylina </it>multiple nucleopolyhedrovirus) is highly virulent to the casuarina moth and has been investigated as a possible biopesticide for controlling this moth. LdMNPV-like virus has also been isolated from <it>Lymantria xylin</it>a larvae but LyxyMNPV was more virulent than LdMNPV-like virus both in NTU-LY and IPLB-LD-652Y cell lines. To better understand LyxyMNPV, the nucleotide sequence of the LyxyMNPV DNA genome was determined and analysed.</p> <p>Results</p> <p>The genome of LyxyMNPV consists of 156,344 bases, has a G+C content of 53.4% and contains 157 putative open reading frames (ORFs). The gene content and gene order of LyxyMNPV were similar to those of LdMNPV, with 151 ORFs identified as homologous to those reported in the LdMNPV genome. Two genes (Lyxy49 and Lyxy123) were homologous to other baculoviruses, and four unique LyxyMNPV ORFs (Lyxy11, Lyxy19, Lyxy130 and Lyxy131) were identified in the LyxyMNPV genome, including a <it>gag-like </it>gene that was not reported in baculoviruses. LdMNPV contains 23 ORFs that are absent in LyxyMNPV. Readily identifiable homologues of the gene <it>host range factor-1 </it>(<it>hrf-1</it>), which appears to be involved in the susceptibility of <it>L. dispar </it>to NPV infection, were not present in LyxyMNPV. Additionally, two putative <it>odv-e27 </it>homologues were identified in LyxyMNPV. The LyxyMNPV genome encoded 14 <it>bro </it>genes compared with 16 in LdMNPV, which occupied more than 8% of the LyxyMNPV genome. Thirteen homologous regions (<it>hr</it>s) were identified containing 48 repeated sequences composed of 30-bp imperfect palindromes. However, they differed in the relative positions, number of repeats and orientation in the genome compared to LdMNPV.</p> <p>Conclusion</p> <p>The gene parity plot analysis, percent identity of the gene homologues and a phylogenetic analysis suggested that LyxyMNPV is a Group II NPV that is most closely related to LdMNPV but with a highly distinct genomic organisation.</p

Associations of Alzheimer's disease risk variants with gene expression, amyloidosis, tauopathy, and neurodegeneration.

BACKGROUND: Genome-wide association studies have identified more than 30 Alzheimer's disease (AD) risk genes, although the detailed mechanism through which all these genes are associated with AD pathogenesis remains unknown. We comprehensively evaluate the roles of the variants in top 30 non-APOE AD risk genes, based on whether these variants were associated with altered mRNA transcript levels, as well as brain amyloidosis, tauopathy, and neurodegeneration. METHODS: Human brain gene expression data were obtained from the UK Brain Expression Consortium (UKBEC), while other data used in our study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. We examined the association of AD risk allele carrier status with the levels of gene expression in blood and brain regions and tested the association with brain amyloidosis, tauopathy, and neurodegeneration at baseline, using a multivariable linear regression model. Next, we analyzed the longitudinal effects of these variants on the change rates of pathology using a mixed effect model. RESULTS: Altogether, 27 variants were detected to be associated with the altered expression of 21 nearby genes in blood and brain regions. Eleven variants (especially novel variants in ADAM10, IGHV1-68, and SLC24A4/RIN3) were associated with brain amyloidosis, 7 variants (especially in INPP5D, PTK2B) with brain tauopathy, and 8 variants (especially in ECHDC3, HS3ST1) with brain neurodegeneration. Variants in ADAMTS1, BZRAP1-AS1, CELF1, CD2AP, and SLC24A4/RIN3 participated in more than one cerebral pathological process. CONCLUSIONS: Genetic variants might play functional roles and suggest potential mechanisms in AD pathogenesis, which opens doors to uncover novel targets for AD treatment