Pregnancy in neuromyelitis optica spectrum disorder: A multicenter study from South China.

Objective: This study aimed to assess the effect of pregnancy on the course of neuromyelitis optica spectrum disorder (NMOSD), and the effect of this disease on pregnancy outcomes.

Methods: Consecutive patients with NMOSD were recruited between September 2015 and April 2016 at an outpatient clinic from four referral institutes in South China. Demographic, clinical, and pregnancy data were retrieved by questionnaires to analyze the association between NMOSD and pregnancy, as well as the potential risk factors for relapse.

Random positioning of nucleosomes enhances heritable bistability.

Chromosomal regions are often dynamically modified by histones, leading to the uncertainty of nucleosome positions. Experiments have provided evidence for this randomness, but it is unclear how it impacts epigenetic heritability. Here, by analyzing a mechanic model at the molecular level, which considers three representative types of nucleosomes (unmodified, methylated, and acetylated) and dynamic nucleosome modifications, we find that in contrast to the equidistance partition of nucleosomes, random partition can significantly enhance heritable bistability.

Theoretical analysis and simulation for a facilitated asymmetric exclusion process.

Driven diffusive systems are important models in nonequilibrium state statistical mechanics. This paper studies an asymmetric exclusion process model with nearest rear neighbor interactions associated with energy. The exact flux expression of the model is obtained by a cluster mean-field method.

Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer.

Purpose: Breast cancer patients with high proportion of cancer stem cells (BCSCs) have unfavorable clinical outcomes. MicroRNAs (miRNAs) regulate key features of BCSCs. We hypothesized that a biology-driven model based on BCSC-associated miRNAs could predict prognosis for the most common subtype, hormone receptor (HR)-positive, HER2-negative breast cancer patients.

IFGFA: Identification of featured genes from genomic data using factor analysis.

In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed. Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types. The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM.

Mechanisms of information decoding in a cascade system of gene expression.

Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However, the mechanism of how this response heterogeneity is generated remains elusive.

A moment-convergence method for stochastic analysis of biochemical reaction networks.

Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods.

Effect of Interaction between Chromatin Loops on Cell-to-Cell Variability in Gene Expression.

According to recent experimental evidence, the interaction between chromatin loops, which can be characterized by three factors-connection pattern, distance between regulatory elements, and communication form, play an important role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effect that addresses the question of how changes in these factors affect variability at the expression level in a systematic rather than case-by-case fashion. Here we make such an effort, based on a mechanic model that maps three fundamental patterns for two interacting DNA loops into a 4-state model of stochastic transcription.

Decomposition and tunability of expression noise in the presence of coupled feedbacks.

Expression noise results in cell-to-cell variability in expression levels, and feedback regulation may complicate the tracing of sources of this noise. Using a representative model of gene expression with feedbacks, we analytically show that the expression noise (or the total noise) is decomposed into three parts: feedback-dependent promoter noise determined by a continuous approximation, birth-death noise determined by a simple Poisson process, and correlation noise induced by feedbacks. We clarify confused relationships between feedback and noise in previous studies, by showing that feedback-regulated noisy sources have different contributions to the total noise in different cases of promoter switching (it is an essential reason resulting in confusions).

POSTPROCESSING MIXED FINITE ELEMENT METHODS FOR SOLVING CAHN-HILLIARD EQUATION: METHODS AND ERROR ANALYSIS.

A postprocessing technique for mixed finite element methods for the Cahn-Hilliard equation is developed and analyzed. Once the mixed finite element approximations have been computed at a fixed time on the coarser mesh, the approximations are postprocessed by solving two decoupled Poisson equations in an enriched finite element space (either on a finer grid or a higher-order space) for which many fast Poisson solvers can be applied. The nonlinear iteration is only applied to a much smaller size problem and the computational cost using Newton and direct solvers is negligible compared with the cost of the linear problem.

Evaluation of the availability of bound analyte for passive sampling in the presence of mobile binding matrix.

Elucidating the availability of the bound analytes for the mass transfer through the diffusion boundary layers (DBLs) adjacent to passive samplers is important for understanding the passive sampling kinetics in complex samples, in which the lability factor of the bound analyte in the DBL is an important parameter. In this study, the mathematical expression of lability factor was deduced by assuming a pseudo-steady state during passive sampling, and the equation indicated that the lability factor was equal to the ratio of normalized concentration gradients between the bound and free analytes. Through the introduction of the mathematical expression of lability factor, the modified effective average diffusion coefficient was proven to be more suitable for describing the passive sampling kinetics in the presence of mobile binding matrixes.

Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error.

Myopia is the most common human eye disorder and it results from complex genetic and environmental causes. The rapidly increasing prevalence of myopia poses a major public health challenge. Here, the CREAM consortium performs a joint meta-analysis to test single-nucleotide polymorphism (SNP) main effects and SNP × education interaction effects on refractive error in 40,036 adults from 25 studies of European ancestry and 10,315 adults from 9 studies of Asian ancestry.

A scoring system based on artificial neural network for predicting 10-year survival in stage II A colon cancer patients after radical surgery.

Nearly 20% patients with stage II A colon cancer will develop recurrent disease post-operatively. The present study aims to develop a scoring system based on Artificial Neural Network (ANN) model for predicting 10-year survival outcome. The clinical and molecular data of 117 stage II A colon cancer patients from Sun Yat-sen University Cancer Center were used for training set and test set; poor pathological grading (score 49), reduced expression of TGFBR2 (score 33), over-expression of TGF-β (score 45), MAPK (score 32), pin1 (score 100), β-catenin in tumor tissue (score 50) and reduced expression of TGF-β in normal mucosa (score 22) were selected as the prognostic risk predictors.

Decreased functional connectivity in the language regions in bipolar patients during depressive episodes but not remission.

Background: Retardation of thought is a crucial clinical feature in patients with bipolar depression, characterized by dysfunctional semantic processing and language communication. However, the underlying neuropathological mechanisms remain largely unknown. The objective of this study was to evaluate the disruption in resting-state functional connectivity in 90 different brain regions during the depressive episodes of bipolar disorder and during disease remission.

Finite-volume method with lattice Boltzmann flux scheme for incompressible porous media flow at the representative-elementary-volume scale.

Based on the Darcy-Brinkman-Forchheimer equation, a finite-volume computational model with lattice Boltzmann flux scheme is proposed for incompressible porous media flow in this paper. The fluxes across the cell interface are calculated by reconstructing the local solution of the generalized lattice Boltzmann equation for porous media flow. The time-scaled midpoint integration rule is adopted to discretize the governing equation, which makes the time step become limited by the Courant-Friedricks-Lewy condition.

Exponential decay of spatial correlation in driven diffusive system: A universal feature of macroscopic homogeneous state.

Driven diffusive systems have been a paradigm for modelling many physical, chemical, and biological transport processes. In the systems, spatial correlation plays an important role in the emergence of a variety of nonequilibrium phenomena and exhibits rich features such as pronounced oscillations. However, the lack of analytical results of spatial correlation precludes us from fully understanding the effect of spatial correlation on the dynamics of the system.

MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.

Motivation: Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors.

Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies.

Background: Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratification have been developed to test the association between multiple markers and disease-susceptibility genes. Only a few methods focus on testing for maternally mediated genetic effects, which is a critical risk for birth defects.

Full Eulerian lattice Boltzmann model for conjugate heat transfer.

In this paper a full Eulerian lattice Boltzmann model is proposed for conjugate heat transfer. A unified governing equation with a source term for the temperature field is derived. By introducing the source term, we prove that the continuity of temperature and its normal flux at the interface is satisfied automatically.

Correction: Division time-based amplifiers for stochastic gene expression.

Correction for 'Division time-based amplifiers for stochastic gene expression' by Haohua Wang et al., Mol. BioSyst.

Fundamental principles of energy consumption for gene expression.

How energy is consumed in gene expression is largely unknown mainly due to complexity of non-equilibrium mechanisms affecting expression levels. Here, by analyzing a representative gene model that considers complexity of gene expression, we show that negative feedback increases energy consumption but positive feedback has an opposite effect; promoter leakage always reduces energy consumption; generating more bursts needs to consume more energy; and the speed of promoter switching is at the cost of energy consumption. We also find that the relationship between energy consumption and expression noise is multi-mode, depending on both the type of feedback and the speed of promoter switching.

Energy bagging tree.

This paper introduces Energy Bagging Tree (EBT) for multivariate nonparametric regression problems. The EBT makes use of a measure of dispersion constructed from a generalized Gini's mean difference as node impurity, and the tree split function therefore corresponds to the product of energy distance and descendants' proportions. As a non-parametric extension of the between-sample variation in the analysis of variance, this measure of dispersion serves well for EBT in understanding certain complex data.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person.

Stability of two-dimensional soft quasicrystals in systems with two length scales.

The relative stability of two-dimensional soft quasicrystals in systems with two length scales is examined using a recently developed projection method, which provides a unified numerical framework to compute the free energy of periodic crystal and quasicrystals. Accurate free energies of numerous ordered phases, including dodecagonal, decagonal, and octagonal quasicrystals, are obtained for a simple model, i.e.

A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma.

Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens.