Regulation of Cancer Stem Cells: Lysine Methylation of p53

The highly studied p53 protein regulates multiple transitions through the cell cycle effectively halting the growth of tumorlike masses.[1] This gene was primitively identified an oncogene; however, it was later derived that p53 functions as a tumor suppressor.[1] Named due to its mass in kDa, p53 is a phosphoprotein comprised of 393 amino acids.[1] Normal cells contain controlled, small quantities of p53 in order to facilitate the regulation of normal cell activities such as growth arrest, senescence,DNArepair, and apoptosis.[1,2] These features are pivotal the continuation of healthy cell production. Constructively, the functions of p53 work together to pause the cell growth cycles in order to address and repair certain sequences of DNA if needed before cell division commences. If repair cannot be completed, then p53 signals for the cell to become senescent and/or later to destroy itself via apoptosis.[1] Upon DNA damage and other cellular stressors, the quantity of p53 is upregulated in order to instigate either the repair or apoptotic cellular pathways; however, continued high levels of p53 are detrimental as its increased ability to activate the apoptotic pathway is likened to an accelerated aging process.[1] The C-terminus domain (CTD) of p53 contains several modifiable lysine residues that may be augmented in different patterns resulting in an array of dissimilar protein-protein interactions thus greatly adding to the multiplicity of functions for the protein itself. This study aims to show that the control of these modifications may not only reduce the causation of multiple forms of cancers but may also be used as a preventative mechanism by never allowing malignant masses to have formed in the firstplace

Distributionally Robust Optimization for Sequential Decision Making

The distributionally robust Markov Decision Process (MDP) approach asks for a distributionally robust policy that achieves the maximal expected total reward under the most adversarial distribution of uncertain parameters. In this paper, we study distributionally robust MDPs where ambiguity sets for the uncertain parameters are of a format that can easily incorporate in its description the uncertainty's generalized moment as well as statistical distance information. In this way, we generalize existing works on distributionally robust MDP with generalized-moment-based and statistical-distance-based ambiguity sets to incorporate information from the former class such as moments and dispersions to the latter class that critically depends on empirical observations of the uncertain parameters. We show that, under this format of ambiguity sets, the resulting distributionally robust MDP remains tractable under mild technical conditions. To be more specific, a distributionally robust policy can be constructed by solving a sequence of one-stage convex optimization subproblems

Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10\% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.Comment: Accepted for INTERSPEECH 201

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges---it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework. Specifically, we assume that there exists a source language with dialog belief tracking annotations while the target languages have no annotated dialog data of any form. Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data. We then distill and transfer its own knowledge to the student state tracker in target languages. We specifically discuss two types of common parallel resources: bilingual corpus and bilingual dictionary, and design different transfer learning strategies accordingly. Experimentally, we successfully use English state tracker as the teacher to transfer its knowledge to both Italian and German trackers and achieve promising results.Comment: 13 pages, 5 figures, 3 tables, accepted to EMNLP 2018 conferenc

Silicon micromachined waveguides for millimeter-wave and submillimeter-wave frequencies

The development of micromachining techniques to create silicon-based waveguide circuits, which can operate up to high submillimeter-wave frequencies, is reported. As a first step, a WR-10 waveguide has been fabricated from (110) silicon wafers. Insertion loss measurements on a gold-plated silicon waveguide show performance comparable to that of standard metal waveguides. It is suggested that active devices and planar circuits can be integrated with the waveguides, solving the traditional mounting problems