Image Segmentation Using De-Textured Images

Image segmentation is one of the fundamental problems in computer vision. The outputs of segmentation are used to extract regions of interest and carry out identification or classification tasks. For these tasks to be reliable, segmentation has to be made more reliable. Although there are exceptionally well-built algorithms available today, they perform poorly in many instances by producing over-merged (combining many unrelated objects) or under-merged (one object appeared as many) results. This leads to far fewer or more segments than expected. Such problems primarily arise due to varying textures within a single object and/or common textures near borders of adjacent objects. The main goal of this report is to pre-process the input images to nullify the effects of such textures. We introduce a pre-processing technique that prepares the input images, before the application of segmentation algorithms. This technique has demonstrated an enhancement in quality of the segments produced. This pre-processing method is called the de-texturing method. We experimented with the effect of the proposed de-texturing method on two existing segmentation methods, namely, the Statistical Region Merging (SRM) method [1] and a k-means-based method as suggested in [2]

Determining the Statistical Significance of Rules for Rule-based Knowledge-extraction Algorithms

Domain specific knowledge bases are often built from domain-specific texts using rule-based knowledge-retrieval algorithms. These algorithms are based on semantic extraction rules that process text using a parser, looking at the resulting parse trees & dependency graphs and then applying those rules to identify possible constructs for triple extraction. The performance of such algorithms critically depends on how capable these rules are in extracting the knowledge (in the form of triples) as a fraction of the total knowledge present in the text fragment. In this paper, we propose a way to statistically analyze the significance of these rules based on the fraction of knowledge that they extract out of given text corpora

An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases

Easy Authoring of Intelligent Tutoring Systems for Synthetic Environments

ABSTRACT: We describe how the Extensible Problem Specifi

High-throughput measurement of fibroblast rhythms reveals genetic heritability of circadian phenotypes in diversity outbred mice and their founder strains.

Circadian variability is driven by genetics and Diversity Outbred (DO) mice is a powerful tool for examining the genetics of complex traits because their high genetic and phenotypic diversity compared to conventional mouse crosses. The DO population combines the genetic diversity of eight founder strains including five common inbred and three wild-derived strains. In DO mice and their founders, we established a high-throughput system to measure cellular rhythms using in vitro preparations of skin fibroblasts. Among the founders, we observed strong heritability for rhythm period, robustness, phase and amplitude. We also found significant sex and strain differences for these rhythms. Extreme differences in period for molecular and behavioral rhythms were found between the inbred A/J strain and the wild-derived CAST/EiJ strain, where A/J had the longest period and CAST/EiJ had the shortest. In addition, we measured cellular rhythms in 329 DO mice, which displayed far greater phenotypic variability than the founders-80% of founders compared to only 25% of DO mice had periods of ~ 24 h. Collectively, our findings demonstrate that genetic diversity contributes to phenotypic variability in circadian rhythms, and high-throughput characterization of fibroblast rhythms in DO mice is a tractable system for examining the genetics of circadian traits

Genetic and Morphological Features of Human iPSC-Derived Neurons with Chromosome 15q11.2 (BP1-BP2) Deletions

Producción CientíficaBackground: Copy number variation on chromosome 15q11.2 (BP1-BP2) causes deletion of CYFIP1, NIPA1, NIPA2 and TUBGCP5; it also affects brain structure and elevates risk for several neurodevelopmental disorders that are associated with dendritic spine abnormalities. In rodents, altered cyfip1 expression changes dendritic spine morphology, motivating analyses of human neuronal cells derived from iPSCs (iPSC-neurons). Methods: iPSCs were generated from a mother and her offspring, both carrying the 15q11.2 (BP1-BP2) deletion, and a non-deletion control. Gene expression in the deletion region was estimated using quantitative real-time PCR assays. Neural progenitor cells (NPCs) and iPSC-neurons were characterized using immunocytochemistry. Results: CYFIP1, NIPA1, NIPA2 and TUBGCP5 gene expression was lower in iPSCs, NPCs and iPSC-neurons from the mother and her offspring in relation to control cells. CYFIP1 and PSD95 protein levels were lower in iPSC-neurons derived from the CNV bearing individuals using Western blot analysis. At 10 weeks post-differentiation, iPSC-neurons appeared to show dendritic spines and qualitative analysis suggested that dendritic morphology was altered in 15q11.2 deletion subjects compared with control cells. Conclusions: The 15q11.2 (BP1-BP2) deletion is associated with reduced expression of four genes in iPSC-derived neuronal cells; it may also be associated altered iPSC-neuron dendritic morphology

POPcorn: An Online Resource Providing Access to Distributed and Diverse Maize Project Data

The purpose of the online resource presented here, POPcorn (Project Portal for corn), is to enhance accessibility of maize genetic and genomic resources for plant biologists. Currently, many online locations are difficult to find, some are best searched independently, and individual project websites often degrade over time—sometimes disappearing entirely. The POPcorn site makes available (1) a centralized, web-accessible resource to search and browse descriptions of ongoing maize genomics projects, (2) a single, stand-alone tool that uses web Services and minimal data warehousing to search for sequence matches in online resources of diverse offsite projects, and (3) a set of tools that enables researchers to migrate their data to the long-term model organism database for maize genetic and genomic information: MaizeGDB. Examples demonstrating POPcorn's utility are provided herein

Metabolomics of Oxidative Stress in Recent Studies of Endogenous and Exogenously Administered Intermediate Metabolites

Aerobic metabolism occurs in a background of oxygen radicals and reactive oxygen species (ROS) that originate from the incomplete reduction of molecular oxygen in electron transfer reactions. The essential role of aerobic metabolism, the generation and consumption of ATP and other high energy phosphates, sustains a balance of approximately 3000 essential human metabolites that serve not only as nutrients, but also as antioxidants, neurotransmitters, osmolytes, and participants in ligand-based and other cellular signaling. In hypoxia, ischemia, and oxidative stress, where pathological circumstances cause oxygen radicals to form at a rate greater than is possible for their consumption, changes in the composition of metabolite ensembles, or metabolomes, can be associated with physiological changes. Metabolomics and metabonomics are a scientific disciplines that focuse on quantifying dynamic metabolome responses, using multivariate analytical approaches derived from methods within genomics, a discipline that consolidated innovative analysis techniques for situations where the number of biomarkers (metabolites in our case) greatly exceeds the number of subjects. This review focuses on the behavior of cytosolic, mitochondrial, and redox metabolites in ameliorating or exacerbating oxidative stress. After reviewing work regarding a small number of metabolites—pyruvate, ethyl pyruvate, and fructose-1,6-bisphosphate—whose exogenous administration was found to ameliorate oxidative stress, a subsequent section reviews basic multivariate statistical methods common in metabolomics research, and their application in human and preclinical studies emphasizing oxidative stress. Particular attention is paid to new NMR spectroscopy methods in metabolomics and metabonomics. Because complex relationships connect oxidative stress to so many physiological processes, studies from different disciplines were reviewed. All, however, shared the common goal of ultimately developing “omics”-based, diagnostic tests to help influence therapies

Apoptotic Engulfment Pathway and Schizophrenia

Background: Apoptosis has been speculated to be involved in schizophrenia. In a previously study, we reported the association of the MEGF10 gene with the disease. In this study, we followed the apoptotic engulfment pathway involving the MEGF10, GULP1, ABCA1 and ABCA7 genes and tested their association with the disease. Methodology/Principal Findings: Ten, eleven and five SNPs were genotyped in the GULP1, ABCA1 and ABCA7 genes respectively for the ISHDSF and ICCSS samples. In all 3 genes, we observed nominally significant associations. Rs2004888 at GULP1 was significant in both ISHDSF and ICCSS samples (p = 0.0083 and 0.0437 respectively). We sought replication in independent samples for this marker and found highly significant association (p = 0.0003) in 3 Caucasian replication samples. But it was not significant in the 2 Chinese replication samples. In addition, we found a significant 2-marker (rs2242436 * rs3858075) interaction between the ABCA1 and ABCA7 genes in the ISHDSF sample (p = 0.0022) and a 3-marker interaction (rs246896 * rs4522565 * rs3858075) amongst the MEGF10, GULP1 and ABCA1 genes in the ICCSS sample (p = 0.0120). Rs3858075 in the ABCA1 gene was involved in both 2- and 3-marker interactions in the two samples. Conclusions/Significance: From these data, we concluded that the GULP1 gene and the apoptotic engulfment pathway are involved in schizophrenia in subjects of European ancestry and multiple genes in the pathway may interactively increase the risks to the disease. © 2009 Chen et al

Fine-mapping reveals novel alternative splicing of the dopamine transporter

Center for Human Genetic Research, Massachusetts General Hospital and Department of Neurology, Harvard Medical School, Harvard University, Boston, Massachusetts.Graduate Program in Biology and Biomedical Science, Yale University, New Haven, Connecticut.The dopamine transporter gene (, ) has been implicated in the pathogenesis of numerous psychiatric and neurodevelopmental disorders, including schizophrenia (SZ). We previously detected association between SZ and intronic variants that replicated in two independent Caucasian samples, but had no obvious function. In follow-up analyses, we sequenced the coding and intronic regions of to identify complete linkage disequilibrium patterns of common variations. We genotyped 78 polymorphisms, narrowing the potentially causal region to two correlated clusters of associated SNPs localized predominantly to introns 3 and 4. Our computational analysis of these intronic regions predicted a novel cassette exon within intron 3, designated E3b, which is conserved among primates. We confirmed alternative splicing of E3b in post-mortem human substantia nigra (SN). As E3b introduces multiple in-frame stop codons, the open reading frame is truncated and the spliced product may undergo nonsense mediated decay. Thus, factors that increase E3b splicing could reduce the amount of unspliced product available for translation. Observations consistent with this prediction were made using cellular assays and in post-mortem human SN. In mini-gene constructs, the extent of splicing is also influenced by at least two common haplotypes, so the alternative splicing was evaluated in relation to SZ risk. Meta-analyses across genome-wide association studies did not support the initial associations and further post-mortem studies did not suggest case-control differences in splicing. These studies do not provide a compelling link to schizophrenia. However, the impact of the alternative splicing on other neuropsychiatric disorders should be investigated. © 2010 Wiley-Liss, Inc