False Discovery Rate Controlling Procedures with BLOSUM62 substitution matrix and their application to HIV Data

Identifying significant sites in sequence data and analogous data is of fundamental importance in many biological fields. Fisher's exact test is a popular technique, however this approach to sparse count data is not appropriate due to conservative decisions. Since count data in HIV data are typically very sparse, it is crucial to use additional information to statistical models to improve testing power. In order to develop new approaches to incorporate biological information in the false discovery controlling procedure, we propose two models: one based on the empirical Bayes model under independence of amino acids and the other uses pairwise associations of amino acids based on Markov random field with on the BLOSUM62 substitution matrix. We apply the proposed methods to HIV data and identify significant sites incorporating BLOSUM62 matrix while the traditional method based on Fisher's test does not discover any site. These newly developed methods have the potential to handle many biological problems in the studies of vaccine and drug trials and phenotype studies

A Novel Mitigation Method for Noise-Induced Temperature Error in CPU Thermal Control

It has been reported that in the thermal control of real-time computing systems, zero-mean thermal sensor noise can induce a significant steady-state error between the target and actual temperatures of a CPU. Unlike the usual case of zero-mean sensor noise resulting in zero-mean temperature fluctuations around the target value, this noise-induced temperature error manifests in the form of a bias, i.e., the mean of the error is not zero. Existing work has analyzed the main cause of this error and produced a solution, known as TCUB-VS. However, this existing solution has a few drawbacks: the transient response is sluggish, and the exact value of the noise standard deviation is necessary in the design stage. In this paper, we propose a novel method of avoiding noise-induced temperature error while overcoming the limitations of the existing work. The proposed method uses an estimated CPU temperature for the part of the controller that is sensitive to noise while using actual measurements for the other part of the controller. In this way, our proposed method eliminates noise-induced temperature error and overcomes the drawbacks of the existing work. To show the efficacy of our proposed method, theoretical results are obtained using a stochastic averaging approach, and experimental results are presented along with simulations.1

HIGH CROSSOVER RATE1 encodes PROTEIN PHOSPHATASE X1 and restricts meiotic crossovers in Arabidopsis.

Meiotic crossovers are tightly restricted in most eukaryotes, despite an excess of initiating DNA double-strand breaks. The majority of plant crossovers are dependent on class I interfering repair, with a minority formed via the class II pathway. Class II repair is limited by anti-recombination pathways; however, similar pathways repressing class I crossovers have not been identified. Here, we performed a forward genetic screen in Arabidopsis using fluorescent crossover reporters to identify mutants with increased or decreased recombination frequency. We identified HIGH CROSSOVER RATE1 (HCR1) as repressing crossovers and encoding PROTEIN PHOSPHATASE X1. Genome-wide analysis showed that hcr1 crossovers are increased in the distal chromosome arms. MLH1 foci significantly increase in hcr1 and crossover interference decreases, demonstrating an effect on class I repair. Consistently, yeast two-hybrid and in planta assays show interaction between HCR1 and class I proteins, including HEI10, PTD, MSH5 and MLH1. We propose that HCR1 plays a major role in opposition to pro-recombination kinases to restrict crossovers in Arabidopsis.Marie Curie International Training Network COMREC European Research Council (ERC) National Research Foundation of Korea Suh Kyungbae Foundatio

Power Boosting for ordered multiple hypotheses with application to Genome-Wide Association Studies

A method for addressing the multiplicity problem is proposed in the setting where the hypotheses test sites may be arranged in some order based on a notion of proximity, such as SNPs of a chromosome in genetic association studies. It is shown that this method is able to control family-wise error rate in the weak sense and numerical evidence shows that this method controls false discovery rate in the strong sense under sparsity. The method is applied to some genome- wide association studies data with asthma and it is argued that this Power Boosting method may be combined with existing error- rate controlling methods in order to improve true positive rates at controllable and possibly negligible cost to the nominal level of error- rate control

Estimation of empirical null using a mixture of normals and its use in local false discovery rate

When high dimensional microarray data is given, it is of interest to select significant genes by controlling a given level of Type-I error. One popular way to control the level is the false discovery rate (FDR). This paper considers gene selection based on the local false discovery rate. In most of the previous studies, the null distribution of gene expression is commonly assumed to be a normal distribution. However, if the null distribution has heavier tail than that of normal, there may exist too many false discoveries leading to the failure of controlling the given level of FDR. We propose a novel procedure which enriches a class of null distribution based on a mixture of normals. We present simulation studies to show that our proposed procedure is less sensitive to variation of null distribution than local false discovery rate with a single normal for the null. We also provide real example of gene expression profiles of antigen-specific human CD8+ T-lymphocytes treated with cytokine Interleukin-2 (IL-2) and Interleukin-15 (IL-15) for comparison.Local false discovery rate Normal mixture Sparsity Gene selection

HEAT SHOCK FACTOR BINDING PROTEIN limits Meiotic Recombination by repressing HEI10 transcription in Arabidopsis

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HEAT SHOCK FACTOR BINDING PROTEIN limits Meiotic crossovers by repressing HEI10 transcription in Arabidopsis

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Resilient architecture for network and control co‐design under wireless channel uncertainty in cyber‐physical systems

In this paper, we propose a resilient architecture for network and control co-design, Wireless-Simplex (W-Simplex), which can ensure control performance by adaptively tuning the network and control parameters against wireless channel uncertainty in cyber-physical systems. To the best of our knowledge, there has been no research into resilient network and control co-design in response to the unreliable wireless channel. Our key observation is that rate adaptation may cause significant degradation in control performance or even system instability. This performance degradation is contrary to the intuition that rate adaptation provides a reliable link under wireless channel uncertainty. We explain the cause of this phenomenon and resolve the situation by proposing a resilient co-design algorithm in an optimization framework. Our simulation study with ns-2 shows the effectiveness of the proposed scheme to provide resilience of cyber-physical systems against wireless channel uncertainty. © 2018 John Wiley & Sons, Ltd.1

When thermal control meets sensor noise: analysis of noise-induced temperature error

Thermal control is critical for real-time systems as overheated processors can result in serious performance degradation or even system breakdown due to hardware throttling. The major challenges in thermal control for real-time systems are (i) the need to enforce both real-time and thermal constraints; (ii) uncertain system dynamics; and (iii) thermal sensor noise. Previous studies have resolved the first two, but the practical issue of sensor noise has not been properly addressed yet. In this paper, we introduce a novel thermal control algorithm that can appropriately handle thermal sensor noise. Our key observation is that even a small zero-mean sensor noise can induce a significant steady-state error between the target and the actual temperature of a processor. This steady-state error is contrary to our intuition that zero-mean sensor noise induces zero-mean fluctuations. We show that an intuitive attempt to resolve this unusual situation is not effective at all. By a rigorous approach, we analyze the underlying mechanism and quantify the noised-induced error in a closed form in terms of noise statistics and system parameters. Based on our analysis, we propose a simple and effective solution for eliminating the error and maintaining the desired processor temperature. Through extensive simulations, we show the advantages of our proposed algorithm, referred to as Thermal Control under Utilization Bound with Virtual Saturation (TCUB-VS). © 2015 IEEE