Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation–maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed

Evidence for a Type-II band alignment between cubic and hexagonal phases of GaN

The study of photoluminescence spectra of a series of thin, undoped, hexagonal GaN films containing cubic GaN inclusions grown by molecular-beam epitaxy on 6H-SiC was presented. It was shown that an emission peak at ∼3.17 eV in thin, hexagonal GaN films exhibits behaviors typical of a spatially indirect transition. The values of the band offsets extracted from the data were in good agreement with theoretical predictions.published_or_final_versio

Hybrid shrinking projection method for a generalized equilibrium problem, a maximal monotone operator and a countable family of relatively nonexpansive mappings

AbstractThe purpose of this paper is to introduce and consider a hybrid shrinking projection method for finding a common element of the set EP of solutions of a generalized equilibrium problem, the set ⋂n=0∞F(Sn) of common fixed points of a countable family of relatively nonexpansive mappings {Sn}n=0∞ and the set T−10 of zeros of a maximal monotone operator T in a uniformly smooth and uniformly convex Banach space. It is proven that under appropriate conditions, the sequence generated by the hybrid shrinking projection method, converges strongly to some point in EP∩T−10∩(⋂n=0∞F(Sn)). This new result represents the improvement, complement and development of the previously known ones in the literature

Fractional Anisotropy in Corpus Callosum Is Associated with Facilitation of Motor Representation during Ipsilateral Hand Movements

BACKGROUND: Coactivation of primary motor cortex ipsilateral to a unilateral movement (M1(ipsilateral)) has been observed, and the magnitude of activation is influenced by the contracting muscles. It has been suggested that the microstructural integrity of the callosal motor fibers (CMFs) connecting M1 regions may reflect the observed response. However, the association between the structural connectivity of CMFs and functional changes in M1(ipsilateral) remains unclear. The purpose of this study was to investigate the relationship between functional changes within M1(ipsilateral) during unilateral arm or leg movements and the microstructure of the CMFs connecting both homotopic representations (arm or leg). METHODS: Transcranial magnetic stimulation was used to assess changes in motor evoked potentials (MEP) in an arm muscle during unilateral movements compared to rest in fifteen healthy adults. Functional magnetic resonance imaging was then used to identify regions of M1 associated with either arm or leg movements. Diffusion-weighted imaging data was acquired to generate CMFs for arm and leg areas using the areas of activation from the functional imaging as seed masks. Individual values of regional fractional anisotropy (FA) of arm and leg CMFs was then calculated by examining the overlap between CMFs and a standard atlas of corpus callosum. RESULTS: The change in the MEP was significantly larger in the arm movement compared to the leg movement. Additionally, regression analysis revealed that FA in the arm CMFs was positively correlated with the change in MEP during arm movement, whereas a negative correlation was observed during the leg movement. However, there was no significant relationship between FA in the leg CMF and the change in MEP during the movements. CONCLUSIONS: These findings suggest that individual differences in interhemispheric structural connectivity may be used to explain a homologous muscle-dominant effect within M1(ipsilateral) hand representation during unilateral movement with topographical specificity

Robust Facial Alignment for Face Recognition

© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images

Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets

An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways