Using Web Technology to Teach Students about Their Digital World

In the School of Business at The College of New Jersey, students are required to take two courses in Management Information Technology (MIT). All students enroll in the same first course. This course focuses on Emerging Technologies and intermediate level data analysis skills. Students are then free to choose their second course. Each MIT course requires that we spend time discussing the social, ethical, and legal issues surrounding technology. This is often a difficult lecture because students think that we are preaching rather than educating them. We also face an expanding curriculum due to the explosion in technology but our core curriculum remains limited to these two classes. To help us overcome our challenges, we turned to Web 2.0 technology to create an interactive learning platform where faculty and students could write and comment about emerging social issues surrounding the Web. In the learning environment that we created, students were provided the opportunity to work with many Web 2.0 tools to learn, interact with fellow students, and express their opinions and ideas. Our team employed the open source content management system Wordpress to build, manage, and monitor this site. In addition, we used Google Analytics to gather usage information. This paper will discuss the process we took to build and use the site and some of the lessons we learned along the way

RNA transcripts for I-J polypeptides are apparently not encoded between the I-A and I-E subregions of the murine major histocompatibility complex

The I-J subregion of the mouse major histocompatibility complex has been reported to encode antigenic determinants expressed by suppressor T cells. Previously, cosmid clones were obtained from mouse sperm DNA that contain all of the sequences between the I-A and I-E subregions, where I-J has been mapped genetically. However, hybridization of these sequences to RNA prepared from several I-J-positive suppressor T-cell hybridomas did not reveal the presence of a transcript. In addition, no rearrangements in this DNA were detected in the suppressor T cells that we have analyzed. Our results indicate that the I-J polypeptides are not encoded between the I-A and I-E subregions of the major histocompatibility complex. We discuss several hypotheses concerning the possible location and expression of I-J genes

Survival disparities in non-Hispanic Black and White cervical cancer patients vary by histology and are largely explained by modifiable factors

PurposeWe investigated racial disparities in survival by histology in cervical cancer and examined the factors contributing to these disparities.MethodsNon-Hispanic Black and non-Hispanic White (hereafter known as Black and White) patients with stage I-IV cervical carcinoma diagnosed between 2004 and 2017 in the National Cancer Database were studied. Survival differences were compared using Cox modeling to estimate hazard ratio (HR) or adjusted HR (AHR) and 95% confidence interval (CI). The contribution of demographic, socioeconomic and clinical factors to the Black vs White differences in survival was estimated after applying propensity score weighting in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC).ResultsThis study included 10,111 Black and 43,252 White patients with cervical cancer. Black patients had worse survival than White cervical cancer patients (HR = 1.40, 95% CI = 1.35-1.45). Survival disparities between Black and White patients varied significantly by histology (HR = 1.20, 95% CI = 1.15-1.24 for SCC; HR = 2.32, 95% CI = 2.12-2.54 for AC, interaction p < 0.0001). After balancing the selected demographic, socioeconomic and clinical factors, survival in Black vs. White patients was no longer different in those with SCC (AHR = 1.01, 95% CI 0.97-1.06) or AC (AHR = 1.09, 95% CI = 0.96-1.24). In SCC, the largest contributors to survival disparities were neighborhood income and insurance. In AC, age was the most significant contributor followed by neighborhood income, insurance, and stage. Diagnosis of AC (but not SCC) at ≥65 years old was more common in Black vs. White patients (26% vs. 13%, respectively).ConclusionsHistology matters in survival disparities and diagnosis at ≥65 years old between Black and White cervical cancer patients. These disparities were largely explained by modifiable factors

Identification of alternative splice variants in Aspergillus flavus through comparison of multiple tandem MS search algorithms

<p>Abstract</p> <p>Background</p> <p>Database searching is the most frequently used approach for automated peptide assignment and protein inference of tandem mass spectra. The results, however, depend on the sequences in target databases and on search algorithms. Recently by using an alternative splicing database, we identified more proteins than with the annotated proteins in <it>Aspergillus flavus</it>. In this study, we aimed at finding a greater number of eligible splice variants based on newly available transcript sequences and the latest genome annotation. The improved database was then used to compare four search algorithms: Mascot, OMSSA, X! Tandem, and InsPecT.</p> <p>Results</p> <p>The updated alternative splicing database predicted 15833 putative protein variants, 61% more than the previous results. There was transcript evidence for 50% of the updated genes compared to the previous 35% coverage. Database searches were conducted using the same set of spectral data, search parameters, and protein database but with different algorithms. The false discovery rates of the peptide-spectrum matches were estimated < 2%. The numbers of the total identified proteins varied from 765 to 867 between algorithms. Whereas 42% (1651/3891) of peptide assignments were unanimous, the comparison showed that 51% (568/1114) of the RefSeq proteins and 15% (11/72) of the putative splice variants were inferred by all algorithms. 12 plausible isoforms were discovered by focusing on the consensus peptides which were detected by at least three different algorithms. The analysis found different conserved domains in two putative isoforms of UDP-galactose 4-epimerase.</p> <p>Conclusions</p> <p>We were able to detect dozens of new peptides using the improved alternative splicing database with the recently updated annotation of the <it>A. flavus </it>genome. Unlike the identifications of the peptides and the RefSeq proteins, large variations existed between the putative splice variants identified by different algorithms. 12 candidates of putative isoforms were reported based on the consensus peptide-spectrum matches. This suggests that applications of multiple search engines effectively reduced the possible false positive results and validated the protein identifications from tandem mass spectra using an alternative splicing database.</p

Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area. SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

[Image: see text] Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives