Spectral characteristics of propylitic alteration minerals as a vectoring tool for porphyry copper deposits

publisher: Elsevier articletitle: Spectral characteristics of propylitic alteration minerals as a vectoring tool for porphyry copper deposits journaltitle: Journal of Geochemical Exploration articlelink: http://dx.doi.org/10.1016/j.gexplo.2017.10.019 content_type: article copyright: © 2017 Elsevier B.V. All rights reserved.© 2017 Elsevier B.V. All rights reserved. The attached document is the authors’ final submitted version of the journal article. You are advised to consult the publisher’s version if you wish to cite from it

A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies

Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders. © 2014 Chang et al

Online video streaming for human tracking based on weighted resampling particle filter

© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a weighted resampling method for particle filter which is applied for human tracking on active camera. The proposed system consists of three major parts which are human detection, human tracking, and camera control. The codebook matching algorithm is used for extracting human region in human detection system, and the particle filter algorithm estimates the position of the human in every input image. The proposed system in this paper selects the particles with highly weighted value in resampling, because it provides higher accurate tracking features. Moreover, a proportional-integral-derivative controller (PID controller) controls the active camera by minimizing difference between center of image and the position of object obtained from particle filter. The proposed system also converts the position difference into pan-tilt speed to drive the active camera and keep the human in the field of view (FOV) camera. The intensity of image changes overtime while tracking human therefore the proposed system uses the Gaussian mixture model (GMM) to update the human feature model. As regards, the temporal occlusion problem is solved by feature similarity and the resampling particles. Also, the particle filter estimates the position of human in every input frames, thus the active camera drives smoothly. The robustness of the accurate tracking of the proposed system can be seen in the experimental results

A computational method for genotype calling in family-based sequencing data

Background: As sequencing technologies can help researchers detect common and rare variants across the human genome in many individuals, it is known that jointly calling genotypes across multiple individuals based on linkage disequilibrium (LD) can facilitate the analysis of low to modest coverage sequence data. However, genotype-calling methods for family-based sequence data, particularly for complex families beyond parent-offspring trios, are still lacking. Results: In this study, first, we proposed an algorithm that considers both linkage disequilibrium (LD) patterns and familial transmission in nuclear and multi-generational families while retaining the computational efficiency. Second, we extended our method to incorporate external reference panels to analyze family-based sequence data with a small sample size. In simulation studies, we show that modeling multiple offspring can dramatically increase genotype calling accuracy and reduce phasing and Mendelian errors, especially at low to modest coverage. In addition, we show that using external panels can greatly facilitate genotype calling of sequencing data with a small number of individuals. We applied our method to a whole genome sequencing study of 1339 individuals at ~10X coverage from the Minnesota Center for Twin and Family Research. Conclusions: The aggregated results show that our methods significantly outperform existing ones that ignore family constraints or LD information. We anticipate that our method will be useful for many ongoing family-based sequencing projects. We have implemented our methods efficiently in a C++ program FamLDCaller, which is available from http://www.pitt.edu/~wec47/famldcaller.html

Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline

Background: As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.Results: We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HSA: DE genes with non-zero effect sizes in all studies, (2) HSB: DE genes with non-zero effect sizes in one or more studies and (3) HSr: DE gene with non-zero effect in "majority"of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.Conclusions: The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HSA, HSB, and HSr). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website. © 2013 Chang et al.; licensee BioMed Central Ltd

Students go through the gears at the iGEM competition for engineering biology

The annual International Genetically Engineered Machine (iGEM) competition, represents an exciting opportunity for students to experience first-hand the potential of synthetic biology approaches to solve real-world problems. In this article, an iGEM team based at Imperial College London share some of the highlights from their participation in the 2018 iGEM event, including sharing their work at the annual Jamboree in Boston, Massachusetts

The ansamycin antibiotic, rifamycin SV, inhibits BCL6 transcriptional repression and forms a complex with the BCL6-BTB/POZ domain

BCL6 is a transcriptional repressor that is over-expressed due to chromosomal translocations, or other abnormalities, in ~40% of diffuse large B-cell lymphoma. BCL6 interacts with co-repressor, SMRT, and this is essential for its role in lymphomas. Peptide or small molecule inhibitors, which prevent the association of SMRT with BCL6, inhibit transcriptional repression and cause apoptosis of lymphoma cells in vitro and in vivo. In order to discover compounds, which have the potential to be developed into BCL6 inhibitors, we screened a natural product library. The ansamycin antibiotic, rifamycin SV, inhibited BCL6 transcriptional repression and NMR spectroscopy confirmed a direct interaction between rifamycin SV and BCL6. To further determine the characteristics of compounds binding to BCL6-POZ we analyzed four other members of this family and showed that rifabutin, bound most strongly. An X-ray crystal structure of the rifabutin-BCL6 complex revealed that rifabutin occupies a partly non-polar pocket making interactions with tyrosine58, asparagine21 and arginine24 of the BCL6-POZ domain. Importantly these residues are also important for the interaction of BLC6 with SMRT. This work demonstrates a unique approach to developing a structure activity relationship for a compound that will form the basis of a therapeutically useful BCL6 inhibitor

Deciphering interplay between Salmonella invasion effectors

Bacterial pathogens have evolved a specialized type III secretion system (T3SS) to translocate virulence effector proteins directly into eukaryotic target cells. Salmonellae deploy effectors that trigger localized actin reorganization to force their own entry into non-phagocytic host cells. Six effectors (SipC, SipA, SopE/2, SopB, SptP) can individually manipulate actin dynamics at the plasma membrane, which acts as a ‘signaling hub’ during Salmonella invasion. The extent of crosstalk between these spatially coincident effectors remains unknown. Here we describe trans and cis binary entry effector interplay (BENEFIT) screens that systematically examine functional associations between effectors following their delivery into the host cell. The results reveal extensive ordered synergistic and antagonistic relationships and their relative potency, and illuminate an unexpectedly sophisticated signaling network evolved through longstanding pathogen–host interaction