RFLP of analyses of an intergenic spacer region of chloroplast DNA in some wild wheat species

Several studies are being made to get high productive wheats throughout the world because they provide the most of human energy and protein needs. In this study, 11 wheat species of Triticum andAegilops were investigated. One of the intergenic regions of cpDNA was studied. This region was amplified with PCR and digested with 6 restriction endonucleases (Hpa II, Alu I, Hinc II, Ava III, Nde I andHae III). According to results, amplified products can be divided into the three groups which are Group 1 (~2000 bp), Group 2 (~1000 bp) and Group 3 (~700 bp). Group 1 contains Ae. neglecta Req. ex Bertol.(syn.:Ae. triaristata), Ae. triuncialis, Ae. mutica Boiss. and T. monococcum L. var. monococcum. Group 2 contains T. aestivum L., Ae. biuncialis Vis., T. turgidum L. (Syn.: T. turgidum var. turgidum) and T.carthlicum Nevski. Group 3 contains Ae. tauschii Cosson. subsp. tauschii and Ae. cylindrica Host. Data obtained from the restriction enzyme digestions were evaluated by using numerical taxonomy systemand a phenogram was constructed using SAHN clustering unweighted pair group method using arithmetic averages

INTEGRATION OF ICT IN EFL/ESL TEACHERS' TRAINING AND SELF-EFFICACY BELIEFS AS PERCEIVED BY THE TRAINERS

Purpose of Study: The current study aimed at revealing the integration of information and communications technology (ICT) in EFL/ESL teachers' training and self-efficacy beliefs as perceived by trainers. A group of (64) trainers from different countries (Palestine, UK, USA, Iran, Lebanon, Yemen, Iraq) completed the two instruments of the study. Methodology: The first instrument was a survey of the actuality of ICT integration in the training; it comprises (47) items distributed into nine domains, i.e.  PowerPoint, Facebook, Wiki, YouTube, Blogs, Email, Google, Mobile, and Platform\E-course. The second instrument was a self-efficacy scale which consists of (14) items. The results of trainers' responses revealed that Emails, Mobile, and Google are often used with relative weights (%78.59, %68.13, and %68.02) respectively, whereas Wikis were never used i.e. relative weight (%28.96). The differences in integrating ICTs between male and female trainers were statistically insignificant. Furthermore, there were no statistically significant differences due to the respondents' period of experience. Results: The results also showed that there were no statistically significant differences in the respondents' integration of ICT due to country of origin. The trainers' self-efficacy wobbles between 66.88 and 58.13 with a total of 61.70, which is moderate. Based on the study findings, the researchers recommend arousing trainers and trainees' awareness regarding integrating more ICTs in their training courses and encouraging them to try the different ICTs which make it easier for trainees to grasp the training material

The cBio cancer Genomics portal: An open platform for exploring multidimensional cancer genomics data

Cataloged from PDF version of article.The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications. © 2012 American Association for Cancer Research

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment