A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem

The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices. We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions. We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.Comment: accepted by AAAI 201

A proteomic approach to indentifying defense related proteins in rice challenged with the fungal pathogen Rhizoctonia solani

Sheath blight caused by the fungal pathogen fungus Rhizoctonia solani Kuhn, is an economically important disease of rice in the southern United States. The overall goal of this study was to identify proteins that were affected by sheath blight development by comparing protein expression patterns between the susceptible, wild-type cultivar Labelle and the resistant, mutant line LSBR-5. Protein samples were extracted from inoculated and non-inoculated rice leaf sheaths after 24 hrs and then loaded onto a Bio-Rad 2-DE gel system. Approximately 1,000 protein spots stained with Sypro-Ruby were reproducibly resolved in all gels used in the comparison analysis. The comparison analysis of relative abundances of protein spots between inoculated and non-inoculated samples was carried out with PDQUEST image analysis software. With MS/MS spectra produced by ESI-Q-TOF analysis, 27 out of a total of 36 protein spots were identified through NCBI nr and NCBI EST database searching with Mascot MS/MS Ion Search Engines (Matrix Sciences). Twenty two protein spots were detected in response to inoculation of both susceptible and resistant plants where 21 protein spots were up-regulated and 1 protein spot was down- regulated. Sixteen of the 22 proteins were identified. The presumed functions of the identified proteins were related to antifungal activity, energy metabolism, photosynthesis, protein degradation, and antioxidation. Eight of 16 identified proteins showed higher expression ratios in the inoculated LSBR-5 than in the inoculated Labelle. An additional 14 protein spots were detected in the response of the resistant LSBR-5. Eleven of 14 protein spots were identified with presumed functions relating to antifungal activity, signal transduction, energy metabolism, photosynthesis, molecular chaperone, protein degradation, and antioxidation. This study is the first to monitor protein expression patterns of the rice leaf-sheath responding to challenge by R. solani and to detect response differences between resistant mutant and susceptible parental material. The information and detected proteins in this study will serve as a solid foundation for future studies to elucidate induced defense mechanisms of rice when infected with R. solani

NOX Inhibitors - A Promising Avenue for Ischemic Stroke.

NADPH-oxidase (NOX) mediated superoxide originally found on leukocytes, but now recognized in several types of cells in the brain. It has been shown to play an important role in the progression of stroke and related cerebrovascular disease. NOX is a multisubunit complex consisting of 2 membrane-associated and 4 cytosolic subunits. NOX activation occurs when cytosolic subunits translocate to the membrane, leading to transport electrons to oxygen, thus producing superoxide. Superoxide produced by NOX is thought to function in long-term potentiation and intercellular signaling, but excessive production is damaging and has been implicated to play an important role in the progression of ischemic brain. Thus, inhibition of NOX activity may prove to be a promising treatment for ischemic brain as well as an adjunctive agent to prevent its secondary complications. There is mounting evidence that NOX inhibition in the ischemic brain is neuroprotective, and targeting NOX in circulating immune cells will also improve outcome. This review will focus on therapeutic effects of NOX assembly inhibitors in brain ischemia and stroke. However, the lack of specificity and toxicities of existing inhibitors are clear hurdles that will need to be overcome before this class of compounds could be translated clinically

A label-free quantitative shotgun proteomics analysis of rice grain development

<p>Abstract</p> <p>Background</p> <p>Although a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages.</p> <p>Results</p> <p>Proteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and <it>pI </it>values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain.</p> <p>Conclusion</p> <p>This is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.</p

Dynamic Anchor Selection and Real-Time Pose Prediction for Ultra-wideband Tagless Gate

Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides a seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. Therefore, the knowledge about the exact location of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. The pose is defined as the actual location of MD on the human body, which affects the localization accuracy. DynaPose is based on line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for anchor selection and pose prediction. Deep learning models use the UWB channel impulse response and the inertial measurement unit embedded in the smartphone. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, 62% higher DL-TDoA localization accuracy, and ultimately detects four different poses with an accuracy of 0.961 in real-time.Comment: arXiv admin note: substantial text overlap with arXiv:2402.0839