ANALYTICAL SINGULAR VALUE DECOMPOSITION FOR A CLASS OF STOICHIOMETRY MATRICES.

We present the analytical singular value decomposition of the stoichiometry matrix for a spatially discrete reaction-diffusion system. The motivation for this work is to develop a matrix decomposition that can reveal hidden spatial flux patterns of chemical reactions. We consider a 1D domain with two subregions sharing a single common boundary.

BOUNDEDNESS OF A CLASS OF SPATIALLY DISCRETE REACTION-DIFFUSION SYSTEMS.

Although the spatially discrete reaction-diffusion equation is often used to describe biological processes, the effect of diffusion in this framework is not fully understood. In the spatially continuous case, the incorporation of diffusion can cause blow-up with respect to the norm, and criteria exist to determine whether the system is bounded for all time. However, no equivalent criteria exist for the discrete reaction-diffusion system.

Reduced anterior cingulate gray matter volume and thickness in subjects with deficit schizophrenia.

Background: Patients with deficit schizophrenia (D-SZ) differ from patients with the non-deficit form of schizophrenia (ND-SZ) in several aspects such as risk factors, neurobiological correlates, treatment response and clinical outcome. It has been debated if brain morphology could differentiate D-SZ from ND-SZ. Anterior cingulate gyrus (ACG) region regulates cognitive and emotional processing and past studies reported structural changes in this region in patients with SZ.

Computational dissection of Arabidopsis smRNAome leads to discovery of novel microRNAs and short interfering RNAs associated with transcription start sites.

The profiling of small RNAs by high-throughput sequencing (smRNA-Seq) has revealed the complexity of the RNA world. Here, we describe a computational scheme for dissecting the plant smRNAome by integrating smRNA-Seq datasets in Arabidopsis thaliana. Our analytical approach first defines ab initio the genomic loci that produce smRNAs as basic units, then utilizes principal component analysis (PCA) to predict novel miRNAs.