Roles of Apoptosis and Autophagy on the Texture of Red Sea Bream Muscle

One of commercial brands in farmed-products of red sea bream (Pagrus major), named “Date-Madai”, is known to possess hard texture and transparency when served as “Sashimi”. The quality of “Sashimi” is usually evaluated by texture, appearance, and color of dark muscle. These characters easily change worse during post mortem storage. The aim of this study was to reveal relationship between proteolytic degradation and muscle quality of the red sea bream. Sensory analysis was carried out to evaluate the quality of “Sashimi” in terms of texture and appearance of flesh. Western blot analysis was conducted to evaluate protein expressions of red sea bream muscle. Significantly higher score in the sensory analysis for brightness was given to the “Date-Madai”. Similar tendency were also observed in color, texture and general acceptability. Intracellular effector of the apoptotic pathway includes contributin of caspase family. Lower level in caspase-3 protein was observed in the “Date-Madai” muscle. Autophagy is known to be inhibited by the target of rapamycin (TOR) signaling. Phosphorylated ribosomal protein S6 kinase, which is in the downstream of the TOR, increased in the “Date-Madai” muscle. This study concluded that the apoptosis and autophagy could be associated with the quality of the red sea bream.The 1st International Symposium on Aquatic Product Processing 201

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on Autonomous Mental Development (TAMD

An Indictment of Bright Line Tests for Honest Services Mail Fraud

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix

Exceptional values of the Dedekind symbol

AbstractFive new exceptional values of the Dedekind symbol are presented, and a conjecture is proposed on the necessary and sufficient conditions for integers to be exceptional values

Suppression of spin-torque in current perpendicular to the plane spin-valves by addition of Dy cap layers

We demonstrate that the addition of Dy capping layers in current perpendicular to the plane giant magneto-resistive spin-valves can increase the critical current density beyond which spin-torque induced instabilities are observed by about a factor of three. Current densities as high as 5e7 A/cm2 are measured provided that the electron current flows from the free to the reference layer. While Dy capped samples exhibit nonmagnetic 1/f noise, it is sufficiently small to be unimportant for read head operation at practical data rates.Comment: 13 pages (manuscript form), with 5 figures. Submitted for publicatio

CD74-dependent Deregulation of the Tumor Suppressor Scribble in Human Epithelial and Breast Cancer Cells

The γ subunit of the major histocompatibility complex (MHC) class II complex, CD74, is overexpressed in a significant proportion of metastatic breast tumors, but the mechanistic foundation and biologic significance of this phenomenon are not fully understood. Here, we show that when CD74 is overexpressed in human cancer and noncancerous epithelial cells, it interacts and interferes with the function of Scribble, a product of a well-known tumor suppressor gene. Furthermore, using epithelial cell lines expressing CD74 under the control of tetracycline-inducible promoter and quantitative high-resolution mass spectrometry, we demonstrate that, as a result of CD74 overexpression, the phosphorylation pattern of the C-terminal part of Scribble undergoes specific changes. This is accompanied with a translocation of the protein from the sites of cell-to-cell contacts at the plasma membrane to the cytoplasm, which is likely to effectively enhance the motility and invasiveness of the cancer cells. © 2013 Neoplasia Press, Inc. All rights reserved

Graduate Recital:Nozomi Nagasaka, Soprano

Kemp Recital Hall Saturday Afternoon February 27, 1999 2:00p.m