Steps to Success: Crossing the Bridge Between Literacy Research and Practice

Steps to Success: Crossing the Bridge Between Literacy Research and Practice introduces instructional strategies linked to the most current research-supported practices in the field of literacy. The book includes chapters related to scientifically-based literacy research, early literacy development, literacy assessment, digital age influences on children’s literature, literacy development in underserved student groups, secondary literacy instructional strategies, literacy and modern language, and critical discourse analysis. Chapters are written by authors with expertise in both college teaching and the delivery of research-supported literacy practices in schools. The book features detailed explanations of a wide variety of literacy strategies that can be implemented by both beginning and expert practitioners. Readers will gain knowledge about topics frequently covered in college literacy courses, along with guided practice for applying this knowledge in their future or current classrooms. The book’s success-oriented framework helps guide educators toward improving their own practices and is designed to foster the literacy development of students of all ages.https://knightscholar.geneseo.edu/oer-ost/1009/thumbnail.jp

Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics

Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites. google.com/site/gaussianbhc

Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level

IL-1β, IL-6, and RANTES as Biomarkers of Chikungunya Severity

Little is known about the immunopathogenesis of Chikungunya virus. Circulating levels of immune mediators and growth factors were analyzed from patients infected during the first Singaporean Chikungunya fever outbreak in early 2008 to establish biomarkers associated with infection and/or disease severity.Adult patients with laboratory-confirmed Chikungunya fever infection, who were referred to the Communicable Disease Centre/Tan Tock Seng Hospital during the period from January to February 2008, were included in this retrospective study. Plasma fractions were analyzed using a multiplex-microbead immunoassay. Among the patients, the most common clinical features were fever (100%), arthralgia (90%), rash (50%) and conjunctivitis (40%). Profiles of 30 cytokines, chemokines, and growth factors were able to discriminate the clinical forms of Chikungunya from healthy controls, with patients classified as non-severe and severe disease. Levels of 8 plasma cytokines and 4 growth factors were significantly elevated. Statistical analysis showed that an increase in IL-1beta, IL-6 and a decrease in RANTES were associated with disease severity.This is the first comprehensive report on the production of cytokines, chemokines, and growth factors during acute Chikungunya virus infection. Using these biomarkers, we were able to distinguish between mild disease and more severe forms of Chikungunya fever, thus enabling the identification of patients with poor prognosis and monitoring of the disease

High-Throughput MicroRNA (miRNAs) Arrays Unravel the Prognostic Role of MiR-211 in Pancreatic Cancer

BACKGROUND: Only a subset of radically resected pancreatic ductal adenocarcinoma (PDAC) patients benefit from chemotherapy, and identification of prognostic factors is warranted. Recently miRNAs emerged as diagnostic biomarkers and innovative therapeutic targets, while high-throughput arrays are opening new opportunities to evaluate whether they can predict clinical outcome. The present study evaluated whether comprehensive miRNA expression profiling correlated with overall survival (OS) in resected PDAC patients. METHODOLOGY/PRINCIPAL FINDINGS: High-resolution miRNA profiles were obtained with the Toray's 3D-Gene™-miRNA-chip, detecting more than 1200 human miRNAs. RNA was successfully isolated from paraffin-embedded primary tumors of 19 out of 26 stage-pT3N1 homogeneously treated patients (adjuvant gemcitabine 1000 mg/m(2)/day, days-1/8/15, every 28 days), carefully selected according to their outcome (OS<12 (N = 13) vs. OS>30 months (N = 6), i.e. short/long-OS). Highly stringent statistics included t-test, distance matrix with Spearman-ranked correlation, and iterative approaches. Unsupervised hierarchical analysis revealed that PDACs clustered according to their short/long-OS classification, while the feature selection algorithm RELIEF identified the top 4 discriminating miRNAs between the two groups. These miRNAs target more than 1500 transcripts, including 169 targeted by two or more. MiR-211 emerged as the best discriminating miRNA, with significantly higher expression in long- vs. short-OS patients. The expression of this miRNA was subsequently assessed by quantitative-PCR in an independent cohort of laser-microdissected PDACs from 60 resected patients treated with the same gemcitabine regimen. Patients with low miR-211 expression according to median value had a significantly shorter median OS (14.8, 95%CI = 13.1-16.5, vs. 25.7 months, 95%CI = 16.2-35.1, log-rank-P = 0.004). Multivariate analysis demonstrated that low miR-211 expression was an independent factor of poor prognosis (hazard ratio 2.3, P = 0.03) after adjusting for all the factors influencing outcome. CONCLUSIONS/SIGNIFICANCE: Through comprehensive microarray analysis and PCR validation we identified miR-211 as a prognostic factor in resected PDAC. These results prompt further prospective studies and research on the biological role of miR-211 in PDAC