On the LURWC property of Orlicz sequence space

Necessary and sufficient conditions for \textbf{URWC} points and \textbf{LURWC} property are given in Orlicz sequence space $l_M$

Incremental Computation: A Semantics-Based Systematic Transformational Approach

Incremental computation takes advantage of repeated computations on inputs that differ slightly from one another, computing each new output incrementally by making use of the previous output rather than from scratch. This thesis concerns the theory, design, and implementation of a general approach to incremental computation. It also elucidates the essence of improving the efficiency of computations by relating it to incremental computation. Our general approach allows incremental computation to be obtained systematically from non-incremental computation and program efficiency to be systematically improved. This research focuses on identifying the fundamentals of efficient incremental computation out of domain-specific properties and language-specific features, devising a general framework that accommodates these fundamentals, and developing a systematic approach based on the framework that exploits program semantics. Three fundamental aspects of incremental computation are identified: avoiding repeated identical computations, caching useful intermediate results, and discovering appropriate auxiliary information. Given a program $f$ and an operation $\oplus$, an incremental program is developed to compute $f(x\oplus y)$ efficiently by using $f(x)$, the intermediate results computed in computing $f(x)$, and auxiliary information about $x$ that can be inexpensively maintained. The approach in this thesis is formalized for a simple functional language, but the underlying principles also apply to other programming languages. It exploits program semantics to discover incrementality that is not directly embedded in primitive operators and takes into consideration properties of application domains as well. It is composed of step-wise program analysis and transformation modules that can, for the most part, be mechanized. Since every non-trivial computation proceeds by iteration (or recursion), the approach is used straightforwardly for achieving efficient computation in general, by computing each iteration incrementally using an appropriate incremental program. This method is applied to problems in interactive systems, optimizing compilers, transformational program development, {\it etc}. The design and implementation of a prototype system, CACHET, for obtaining incremental programs is also described

Cost Recurrences for DML Programs

A cost recurrence describes an upper bound for the running time of a program in terms of the size of its input. Finding cost recurrences is a frequent intermediate step in complexity analysis, and this step requires an abstraction from data to data size. In this article, we use information contained in dependent types to achieve such an abstraction: Dependent ML (DML), a conservative extension of ML, provides dependent types that can be used to associate data with size information, thus describing a possible abstraction. We systematically extract cost recurrences from first-order DML programs, guiding the abstraction from data to data size with information contained in DML type derivations

Enzymatically Digested Food Waste Altered Fecal Microbiota But Not Meat Quality and Carcass Characteristics of Growing-Finishing Pigs.

This experiment aimed to evaluate meat quality, fatty acid profile in back-fat, and fecal microbiota of growing-finishing pigs fed with liquid enzymatically digested food waste. Fifty-six crossbred pigs (approximately 32.99 kg body weight) were assigned to one of two treatments with seven replicate pens and four pigs per pen. Pigs were fed with control (corn-soybean meal diets) or food waste from d 0 to 53, while all pigs were fed with the control diet from d 53 to 79. The 16S rRNA sequencing was used to analyze microbiota of feces collected on d 0, 28, 53, and 79. Meat quality and carcass characteristics were measured in one pig per pen at the end of the experiment. Pigs fed with food waste contained more (p < 0.05) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in back-fat. Feeding food waste increased (p < 0.05) the relative abundances of Lachnospiraceae and Ruminococcaceae, but decreased (p < 0.05) the relative abundances of Streptococcaceae and Clostridiaceae in feces on d 29 or d 53. In conclusion, feeding enzymatically digested food waste did not affect pork quality, but provided more beneficial fatty acids to pork consumers and altered the fecal microbiota in growing-finishing pigs

Enzymatically Digested Food Waste Altered Fecal Microbiota But Not Meat Quality and Carcass Characteristics of Growing-Finishing Pigs

This experiment aimed to evaluate meat quality, fatty acid profile in back-fat, and fecal microbiota of growing-finishing pigs fed with liquid enzymatically digested food waste. Fifty-six crossbred pigs (approximately 32.99 kg body weight) were assigned to one of two treatments with seven replicate pens and four pigs per pen. Pigs were fed with control (corn-soybean meal diets) or food waste from d 0 to 53, while all pigs were fed with the control diet from d 53 to 79. The 16S rRNA sequencing was used to analyze microbiota of feces collected on d 0, 28, 53, and 79. Meat quality and carcass characteristics were measured in one pig per pen at the end of the experiment. Pigs fed with food waste contained more (p < 0.05) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in back-fat. Feeding food waste increased (p < 0.05) the relative abundances of Lachnospiraceae and Ruminococcaceae, but decreased (p < 0.05) the relative abundances of Streptococcaceae and Clostridiaceae in feces on d 29 or d 53. In conclusion, feeding enzymatically digested food waste did not affect pork quality, but provided more beneficial fatty acids to pork consumers and altered the fecal microbiota in growing-finishing pigs

Discovery of 7‑Methyl-5-(1-{[3-(trifluoromethyl)phenyl]acetyl}-2,3-dihydro‑1<i>H</i>‑indol-5-yl)‑7<i>H</i>‑pyrrolo[2,3‑<i>d</i>]pyrimidin-4-amine (GSK2606414), a Potent and Selective First-in-Class Inhibitor of Protein Kinase R (PKR)-like Endoplasmic Reticulum Kinase (PERK)

Protein kinase R (PKR)-like endoplasmic reticulum kinase (PERK) is activated in response to a variety of endoplasmic reticulum stresses implicated in numerous disease states. Evidence that PERK is implicated in tumorigenesis and cancer cell survival stimulated our search for small molecule inhibitors. Through screening and lead optimization using the human PERK crystal structure, we discovered compound <b>38</b> (GSK2606414), an orally available, potent, and selective PERK inhibitor. Compound <b>38</b> inhibits PERK activation in cells and inhibits the growth of a human tumor xenograft in mice