Consonant-vowel Interactions Inform Paradigm Organization in Egyptian Arabic

This paper presents a quantitative study on vowel alternation in Egyptian Arabic verbs. Specifically, the vowels in perfective verb forms (of the prosodic shape CVCVC) and imperfective verb forms (-CCVC) are hard to predict from each other. This study investigates how probabilistic phonological generalizations involving the root consonants and vowel correspondences help predict the idiosyncratic vowel choice by collecting lexicon statistics and fitting regression models. Following the line of works which has shown that speakers have the ability to internalize statistical patterns into their phonological grammars (e.g., Zuraw 2000, Ernestus & Baayen 2003), the models were used as a means to investigate organization of the perfective-imperfective paradigm. Moreover, by showing that consonant and vowel information play distinct roles in paradigm predictability, this study provides evidence for lexical representations that separate consonants and vowels in Semitic languages (e.g., McCarthy 1979). 

Treatment with Interleukin-22 (IL-22) Protects Against Necrotizing Enterocolitis (NEC) by Enhancing Mucosal Healing

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor(s): Misty Goo

Two Rules on the Protein-Ligand Interaction

So far, we still lack a clear molecular mechanism to explain the protein-ligand interaction on the basis of electronic structure of a protein. By combining the calculation of the full electronic structure of a protein along with its hydrophobic pocket and the perturbation theory, we found out two rules on the protein-ligand interaction. One rule is the interaction only occurs between the lowest unoccupied molecular orbitals (LUMOs) of a protein and the highest occupied molecular orbital (HOMO) of its ligand, not between the HOMOs of a protein and the LUMO of its ligand. The other rule is only those residues or atoms located both on the LUMOs of a protein and in a surface pocket of a protein are activity residues or activity atoms of the protein and the corresponding pocket is the ligand binding site. These two rules are derived from the characteristics of energy levels of a protein and might be an important criterion of drug design

Bridging adaptive management and reinforcement learning for more robust decisions

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional, and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning, a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where reinforcement learning (RL) holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable. For example, model-free deep RL might help identify quantitative decision strategies even when models are nonidentifiable. Finally, we discuss technical and social issues that arise when applying reinforcement learning to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises, and perils of experience-based decision-making.Comment: In press at Philosophical Transactions of the Royal Society

Free Cash Flows and Price Momentum

This study investigates the role of free cash flows and (cross-sectional and time-series) price momentum in predicting future stock returns. Past returns and free cash flows each positively predict future stock returns after controlling for the other, suggesting that cash flows and momentum both contain valuable and distinctive information about future stock returns. A strategy of buying past winners with high free cash flows and shorting past losers with low free cash flows significantly outperforms the traditional momentum trading strategy. The enhanced performance is not sensitive to investor sentiment, time variations, or transaction costs. Further analysis shows that the incremental cash flow effects are largely attributable to net distributions to equity/debt holders. Overall, our findings shed light on the role of corporate fundamentals in technical trading strategies

Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning

We introduce Robust Restless Bandits, a challenging generalization of restless multi-arm bandits (RMAB). RMABs have been widely studied for intervention planning with limited resources. However, most works make the unrealistic assumption that the transition dynamics are known perfectly, restricting the applicability of existing methods to real-world scenarios. To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs. RMABPPO hinges on learning an auxiliary "$\lambda$-network" that allows each arm's learning to decouple, greatly reducing sample complexity required for training; (iv) Under minimax regret, the adversary in the double oracle approach is notoriously difficult to implement due to non-stationarity. To address this, we formulate the adversary oracle as a multi-agent reinforcement learning problem and solve it with a multi-agent extension of RMABPPO, which may be of independent interest as the first known algorithm for this setting. Code is available at https://github.com/killian-34/RobustRMAB.Comment: 18 pages, 3 figure

Endoglin Is Essential for the Maintenance of Self-Renewal and Chemoresistance in Renal Cancer Stem Cells.

Renal cell carcinoma (RCC) is a deadly malignancy due to its tendency to metastasize and resistance to chemotherapy. Stem-like tumor cells often confer these aggressive behaviors. We discovered an endoglin (CD105)-expressing subpopulation in human RCC xenografts and patient samples with a greater capability to form spheres in vitro and tumors in mice at low dilutions than parental cells. Knockdown of CD105 by short hairpin RNA and CRISPR/cas9 reduced stemness markers and sphere-formation ability while accelerating senescence in vitro. Importantly, downregulation of CD105 significantly decreased the tumorigenicity and gemcitabine resistance. This loss of stem-like properties can be rescued by CDA, MYC, or NANOG, and CDA might act as a demethylase maintaining MYC and NANOG. In this study, we showed that Endoglin (CD105) expression not only demarcates a cancer stem cell subpopulation but also confers self-renewal ability and contributes to chemoresistance in RCC