Synthesis and degranulation-inhibiting activities of the proposed apteniols B,C, and G

The synthesis of compounds with the structures proposed for the oxyneolignan apteniols B, C, and G is described. The diphenyl ether skeletons of the proposed apteniols were formed via Ullmann ether synthesis. In particular, the spectral data for the synthesized apteniols B, C, and G did not agree with those previously reported for the isolated compounds. Furthermore, the synthesized proposed apteniol B did not show degranulation-inhibiting activity, while the prepared proposed apteniols C and G exhibited activities considerably weaker than that of the methyl ester of proposed apteniol A.     

Estimated quadratic inference function for correlated failure time data

An estimated quadratic inference function method is proposed for correlated failure time data with auxiliary covariates. The proposed method makes efficient use of the auxiliary information for the incomplete exposure covariates and preserves the property of the quadratic inference function method that requires the covariates to be completely observed. It can improve the estimation efficiency and easily deal with the situation when the cluster size is large. The proposed estimator which minimizes the estimated quadratic inference function is shown to be consistent and asymptotically normal. A chi-squared test based on the estimated quadratic inference function is proposed to test hypotheses about the regression parameters. The small-sample performance of the proposed method is investigated through extensive simulation studies. The proposed method is then applied to analyze the Study of Left Ventricular Dysfunction (SOLVD) data as an illustration.     

A random effects model for multivariate failure time data from multicenter clinical trials

A random effects model for analyzing multivariate failure time data is proposed. The work is motivated by the need for assessing the mean treatment effect in a multicenter clinical trial study, assuming that the centers are a random sample from an underlying population. An estimating equation for the mean hazard ratio parameter is proposed. The proposed estimator is shown to be consistent and asymptotically normally distributed. A variance estimator, based on large sample theory, is proposed. Simulation results indicate that the proposed estimator performs well in finite samples. The proposed variance estimator effectively corrects the bias of the naive variance estimator, which assumes independence of individuals within a group. The methodology is illustrated with a clinical trial data set from the Studies of Left Ventricular Dysfunction. This shows that the variability of the treatment effect is higher than found by means of simpler models.     

Linking quantal absorption rate to the visual response

A photochemical system is proposed that offers a possible link between the rate of quantal absorption and the visual response. In the proposed system the time courses of the concentrations of certain photoproducts turn out to be equal to the quantal absorption rate passed through various linear filters. The proposed system is consistent with the cone pigment kinetics proposed by Rushton (1958). The system produces afterimages and can provide Weber's law behavior.     

Generalized varying coefficient models for longitudinal data

We propose a generalization of the varying coefficient modelfor longitudinal data to cases where not only current but alsorecent past values of the predictor process affect current response.More precisely, the targeted regression coefficient functionsof the proposed model have sliding window supports around currenttime t. A variant of a recently proposed two-step estimationmethod for varying coefficient models is proposed for estimationin the context of these generalized varying coefficient models,and is found to lead to improvements, especially for the caseof additive measurement errors in both response and predictors.The proposed methodology for estimation and inference is alsoapplicable for the case of additive measurement error in thecommon versions of varying coefficient models that relate onlycurrent observations of predictor and response processes toeach other. Asymptotic distributions of the proposed estimatorsare derived, and the model is applied to the problem of predictingprotein concentrations in a longitudinal study. Simulation studiesdemonstrate the efficacy of the proposed estimation procedure.     

Surface EMG-force modelling for the biceps brachii and its experimental evaluation during isometric isotonic contractions

The aim of this study was to evaluate a surface electromyography (sEMG) signal and force model for the biceps brachii muscle during isotonic isometric contractions for an experimental set-up as well as for a simulation. The proposed model includes a new rate coding scheme and a new analytical formulation of the muscle force generation. The proposed rate coding scheme supposes varying minimum and peak firing frequencies according to motor unit (MU) type (I or II). Practically, the proposed analytical mechanogram allows us to tune the force contribution of each active MU according to its type and instantaneous firing rate. A subsequent sensitivity analysis using a Monte Carlo simulation allows deducing optimised input parameter ranges that guarantee a realistic behaviour of the proposed model according to two existing criteria and an additional one. In fact, this proposed new criterion evaluates the force generation efficiency according to neural intent. Experiments and simulations, at varying force levels and using the optimised parameter ranges, were performed to evaluate the proposed model. As a result, our study showed that the proposed sEMG–force modelling can emulate the biceps brachii behaviour during isotonic isometric contractions.     

Iterative Nonlocal Total Variation Regularization Method for Image Restoration

In this paper, a Bregman iteration based total variation image restoration algorithm is proposed. Based on the Bregman iteration, the algorithm splits the original total variation problem into sub-problems that are easy to solve. Moreover, non-local regularization is introduced into the proposed algorithm, and a method to choose the non-local filter parameter locally and adaptively is proposed. Experiment results show that the proposed algorithms outperform some other regularization methods.     

Extreme learning machines for regression based on V-matrix method

This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.     

A Confidence Region Approach for Assessing Equivalence in Variability of Bioavailability

The problem for assessment of equivalence in variability of bioavailability between two drug products is considered. Similar to the method for assessing bioequivalence in average bioavailability proposed by Chow and Shao (1990), an exact confidence region approach is derived when the intersubject variance is known. When the intersubject variance is unknown, a large sample approximation is considered. The proposed method for assessing equivalence of variability of bioavailability appears to be asymptotically uncorrelated with that of Chow and Shao (1990) for average bioavailability. As a result, the proposed method in conjunction with the method proposed by Chow and Shao (1990) constitutes a confidence region approach for assessing population bioequivalence. An example concerning a bioequivalence trial with 24 healthy volunteers is provided to illustrate the proposed method.     

Robust semiparametric microarray normalization and significance analysis

Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.     

Mathematical multi-scale model of the cardiovascular system including mitral valve dynamics. Application to ischemic mitral insufficiency

The aim of this research is to propose a small intestine model for electrically propelled capsule endoscopy. The electrical stimulus can cause contraction of the small intestine and propel the capsule along the lumen. The proposed model considered the drag and friction from the small intestine using a thin walled model and Stokes' drag equation. Further, contraction force from the small intestine was modeled by using regression analysis. From the proposed model, the acceleration and velocity of various exterior shapes of capsule were calculated, and two exterior shapes of capsules were proposed based on the internal volume of the capsules. The proposed capsules were fabricated and animal experiments were conducted. One of the proposed capsules showed an average (SD) velocity in forward direction of 2.91 ± 0.99 mm/s and 2.23 ± 0.78 mm/s in the backward direction, which was 5.2 times faster than that obtained in previous research. The proposed model can predict locomotion of the capsule based on various exterior shapes of the capsule.     

Exact-corrected confidence interval for risk difference in noninferiority binomial trials

A novel confidence interval estimator is proposed for the risk difference in noninferiority binomial trials. The proposed confidence interval, which is dependent on the prespecified noninferiority margin, is consistent with an exact unconditional test that preserves the type-I error and has improved power, particularly for smaller sample sizes, compared to the confidence interval by Chan and Zhang. The improved performance of the proposed confidence interval is theoretically justified and demonstrated with simulations and examples. An R package is also distributed that implements the proposed methods along with other confidence interval estimators.     

A Proposed Pathway for Catabolism of Propionate in Methanogenic Cocultures

A metabolic pathway for the catabolism of propionate is proposed. This pathway incorporates a transcarboxylation reaction involving propionyl coenzyme A and oxaloacetate and a carboxylation of pyruvate to regenerate oxaloacetate. Data indicated that the proposed pathway is reversible. The proposed pathway and its apparent reversibility provide a reasonable explanation of observations obtained from metabolism of labeled substrate.     

Partial characterization of a tropoelastin precursor isolated from chick aorta.

Evidence is presented that indicates tropoelastin is derived from a soluble elastin with a molecular weight of 95000. Tropoelastin and its proposed precursor were isolated from the aortas of copper-deficient chicks. Although it is doubtful that the proposed precursor is an initial product of elastin translation, i.e., a proelastin, it is proposed to be at least a truncated form of proelastin that is converted to tropoelastin. The key to its isolation was the presence of alpha 1-antitrypsin at each step in the purification procedure. The first 11 amino acid residues at the NH2 terminal of the proposed tropoelastin precursor (GGVPGVAVPGGV) are the same as those for tropoelastin. Its amino acid composition is similar to that of tropoelastin, except for higher amounts of acidic amino acid residues. Further, the proposed precursor contains a limited number of aldehydic functions, presumably in the form of peptidyl allysine. This was taken as an indication that the proposed precursor serves as a substract for lysyl oxidase. Under the conditions used for the isolation, the precursor appeared to be in higher concentrations than tropoelastin in aorta extracts from copper-deficient chicks.     

An Effective Approach of Secured Medical Image Transmission Using Encryption Method

Various chaos-based image encryption schemes have been proposed in last few years. The proposed image encryption method uses chaotic map. The encryption is done by using 256 bit long external secret key. The initial condition for the chaotic mapping is evaluated by the use of external secret key along with the mapping function. Besides that, the proposed method is made more robust by applying multiple operations to the pixels of the image depending on the outcome of the calculation of the logistic map. Moreover, block shuffling of the image and modifying the secret key after encryption of each row is also done to add chaos to the proposed method.     

Beam Manipulation by Hybrid Plasmonic-Dielectric Metasurfaces

A hybrid plasmonic-dielectric metasurface is proposed in order to manipulate beam propagation in desired manners. The metasurface is composed of patterned hybrid graphene-silicon nano-disks deposited on a low-index substrate, namely silica. It is shown that the proposed hybrid metasurface simultaneously benefits from the advantages of graphene-based metasurfaces and dielectric ones. Specially, we show that the proposed hybrid metasurface not only provides reconfigurability, just like previously proposed graphene-based metasurfaces, but also similar to dielectric metasurfaces, is of low loss and CMOS-compatible. Such exceptional features give the metasurface exceptional potentials to realize high efficient optical components. To demonstrate the latter point, focusing and anomalous reflection are performed making use of the proposed hybrid structure as examples of two well-known optical functionalities. This work opens up a new route in realization of reconfigurable meta-devices with widely real-world applications which cannot be achieved with their passive counterparts.

     

Extended Mantel-Haenszel estimating procedure for multivariate logistic regression models

A class of estimating functions is proposed for the estimation of multivariate relative risk in stratified case-control studies. It reduces to the well-known Mantel-Haenszel estimator when there is a single binary risk factor. Large-sample properties of the solutions to the proposed estimating equations are established for two distinct situations. Efficiency calculations suggest that the proposed estimators are nearly fully efficient relative to the conditional maximum likelihood estimator for the parameters considered. Application of the proposed method to family data and longitudinal data, where the conditional likelihood approach fails, is discussed. Two examples from case-control studies and one example from a study on familial aggregation are presented.     

Response Adaptive Designs with a Variance‐penalized Criterion

We consider a response adaptive design of clinical trials with a variance‐penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop‐the‐loser design. Simulation comparisons of the proposed design with some existing designs are presented.     

Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm

Storey's estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini–Hochberg procedure. Control over the false discovery rate by the proposed adaptive procedure has been proved analytically. The proposed estimate is also used to formulate an adaptive version of the Benjamini–Hochberg–Heyse procedure. Simulation experiments establish the conservative nature of this new adaptive procedure. Substantial amount of gain in power is observed for the new adaptive procedures over the standard procedures. For demonstration of the proposed method, two important real life gene expression data sets, one related to the study of HIV and the other related to methylation study, are used.     

A delayed neural network method for solving convex optimization problems

In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network.