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Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined

Validation studies have been used to increase the reliability of the statistical conclusions for scientific discoveries; such studies improve the reproducibility of the findings and reduce the possibility of false positives. Here, one of the important roles of statistics is to quantify reproducibility rigorously. Two concepts were recently defined for this purpose: (i) rediscovery rate (RDR), which is the expected proportion of statistically significant findings in a study that can be replicated in the validation study and (ii) false discovery rate in the validation study (vFDR). In this paper, we aim to develop a nonparametric approach to estimate the RDR and vFDR and show an explicit link between the RDR and the FDR. Among other things, the link explains why reproducing statistically significant results even with low FDR level may be difficult. Two metabolomics datasets are considered to illustrate the application of the RDR and vFDR concepts in high-throughput data analysis. Copyright © 2016 John Wiley & Sons, Ltd.

Alcoholic liver disease (ALD) is characterized by lipid accumulation and liver injury. However, how chronic alcohol consumption causes hepatic lipid accumulation remains elusive. The present study demonstrates that activation of the mechanistic target of rapamycin complex 1 (mTORC1) plays a causal role in alcoholic steatosis, inflammation, and liver injury. Chronic-plus-binge ethanol feeding led to hyperactivation of mTORC1, as evidenced by increased phosphorylation of mTOR and its downstream kinase S6 kinase 1 (S6K1) in hepatocytes. Aberrant activation of mTORC1 was likely attributed to the defects of the DEP domain-containing mTOR-interacting protein (DEPTOR) and the nicotinamide adenine dinucleotide-dependent deacetylase sirtuin 1 (SIRT1) in the liver of chronic-plus-binge ethanol-fed mice and in the liver of patients with ALD. Conversely, adenoviral overexpression of hepatic DEPTOR suppressed mTORC1 signaling and ameliorated alcoholic hepatosteatosis, inflammation, and acute-on-chronic liver injury. Mechanistically, the lipid-lowering effect of hepatic DEPTOR was attributable to decreased proteolytic processing, nuclear translocation, and transcriptional activity of the lipogenic transcription factor sterol regulatory element-binding protein-1 (SREBP-1). DEPTOR-dependent inhibition of mTORC1 also attenuated alcohol-induced cytoplasmic accumulation of the lipogenic regulator lipin 1 and prevented alcohol-mediated inhibition of fatty acid oxidation. Pharmacological intervention with rapamycin alleviated the ability of alcohol to up-regulate lipogenesis, to down-regulate fatty acid oxidation, and to induce steatogenic phenotypes. Chronic-plus-binge ethanol feeding led to activation of SREBP-1 and lipin 1 through S6K1-dependent and independent mechanisms. Furthermore, hepatocyte-specific deletion of SIRT1 disrupted DEPTOR function, enhanced mTORC1 activity, and exacerbated alcoholic fatty liver, inflammation, and liver injury in mice. Conclusion The dysregulation of SIRT1-DEPTOR-mTORC1 signaling is a critical determinant of ALD pathology; targeting SIRT1 and DEPTOR and selectively inhibiting mTORC1-S6K1 signaling may have therapeutic potential for treating ALD in humans. (Hepatology 2018).

Summary This paper suggests a simple convex optimization approach to state-feedback adaptive stabilization problem for a class of discrete-time LTI systems subject to polytopic uncertainties. The proposed method relies on estimating the uncertain parameters by solving an online optimization at each time step, such as a linear or quadratic programming, and then, on tuning the control law with that information, which can be conceptually viewed as a kind of gain-scheduling or indirect adaptive control. Specifically, an admissible domain of stabilizing state-feedback gain matrices is designed offline by means of linear matrix inequality problems, and based on the online estimation of the uncertain parameters, the state-feedback gain matrix is calculated over the set of stabilizing feedback gains. The proposed stabilization algorithm guarantees the asymptotic stability of the overall closed-loop control system. An example is given to show the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.

The partial least-square (PLS) method has been adapted to the Cox's proportional hazards model for analyzing high-dimensional survival data. But because the latent components constructed in PLS employ all predictors regardless of their relevance, it is often difficult to interpret the results. In this paper, we propose a new formulation of sparse PLS (SPLS) procedure for survival data to allow simultaneous sparse variable selection and dimension reduction. We develop a computing algorithm for SPLS by modifying an iteratively reweighted PLS algorithm and illustrate the method with the Swedish and the Netherlands Cancer Institute breast cancer datasets. Through the numerical studies, we find that our SPLS method generally performs better than the standard PLS and sparse Cox regression methods in variable selection and prediction. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY In this paper, the finite frequency robust filtering problem (-FFRFP), design of a robust filter minimizing the norm from the disturbance input to the estimation error evaluated over a prescribed finite frequency domain, is considered for continuous-time and discrete-time linear time-invariant (LTI) systems with polytopic parameter uncertainties. By means of the generalized Kalman–Yakubovich–Popov (KYP) lemma in combination with a result known as Finsler's lemma, the -FFRFP is cast as a linear matrix inequality (LMI) optimization. Examples are given to demonstrate that the proposed condition can achieve improvement over the previous ones in the literature. Copyright © 2012 John Wiley & Sons, Ltd.

The investigators quantified the relationship between the genetic polymorphism of HMGCR (3-hydroxy-3-methylglutaryl coenzyme A reductase) and the low-density lipoprotein cholesterol (LDL-C)-lowering effects of atorvastatin in a prospective clinical study. Twenty-four healthy participants were grouped into HMGCR rs3846662 GG (n = 13) and AA (n = 11) genotypes and given atorvastatin (20 mg/d) for 14 days. Serum levels of LDL-C, high-density lipoprotein cholesterol, total cholesterol, triglycerides, and creatinine kinase (CK) were measured before (day 1) and 7, 13, 14, 15, 16, 21, and 28 days after dosing initiation. Blood samples for pharmacokinetics were taken on days 14 through 16. The levels of LDL-C in the GG group were significantly higher than in the AA group at all observation times, with mean differences of 18% to 33% (P < .05). The area under the LDL-C-time curve and the minimum value of LDL-C in the GG group were 24% and 23% higher than in the AA group, respectively (P < .01). There was no significant difference in other lipids, CK, and pharmacokinetic parameters. The HMGCR rs3846662 GG genotype was quantitatively documented to be a significant determinant for higher LDL-C level in basal state and possibly in response to atorvastatin.

With the introduction of the new generation RFID technology, EPCglobal Class-1 Generation-2, there is considerable interest in improving the performance of the framed slotted Aloha (FSA)-based tag collision arbitration protocol. We suggest a novel time-optimal anti-collision algorithm for the FSA protocol. Our performance evaluation demonstrates that our algorithm outperforms other tag collision arbitration schemes.