Location: Engineering Library- R858.H57 2016

Location: Engineering Library- QA76.76.E95A395 2016

Location: Engineering Library- TA409.L346 2016

Location: Electronic Resource- 

Adam Hall’s Biloxi Shuckers have released their season game notes. In 2024 Biloxi Shuckers a double-A affiliate of the Milwaukee Brewers, finished the first half of the season with a record of 30-37, 4th in the south division, and then they had a second half record of 36-32, finishing 2nd in the south division. The […]

The police have released their 2021 Official Statistics Report, noting that “while total crime was reduced, the Covid-19 regulations that were in effect throughout the year, limited gatherings and social activities, and so impacted crime patterns.” Mr. Darrin Simons, Commissioner of Police said, “The Bermuda Police Service [BPS] has released its Official Statistics Report for […]

By Ryan Menezes Published: November 12th, 2024

Dr. Outi Tervo of Greenland Institute for Natural Resources, shares how mathematics helps recommend speed limits for marine vessels, which benefits narwhals and Inuit culture. Narwhals "can only be found in the Arctic," said Outi Tervo, a senior scientist at GINR. "These species are going to be threatened by climate change more than other species that can live in a bigger geographical area." The collaboration has already lobbied on behalf of the narwhals to reduce the level of sea traffic in their habitat, after using mathematical analysis to identify how noise from passing boats changes the narwhals' foraging behavior.

Influenza A virus (IAV) mutates rapidly, resulting in antigenic drift and poor year-to-year vaccine effectiveness. One challenge in designing effective vaccines is that genetic mutations frequently cause amino acid variations in IAV envelope protein hemagglutinin (HA) that create new N-glycosylation sequons; resulting N-glycans cause antigenic shielding, allowing viral escape from adaptive immune responses. Vaccine candidate strain selection currently involves correlating antigenicity with HA protein sequence among circulating strains, but quantitative comparison of site-specific glycosylation information may likely improve the ability to design vaccines with broader effectiveness against evolving strains. However, there is poor understanding of the influence of glycosylation on immunodominance, antigenicity, and immunogenicity of HA, and there are no well-tested methods for comparing glycosylation similarity among virus samples. Here, we present a method for statistically rigorous quantification of similarity between two related virus strains that considers the presence and abundance of glycopeptide glycoforms. We demonstrate the strength of our approach by determining that there was a quantifiable difference in glycosylation at the protein level between WT IAV HA from A/Switzerland/9715293/2013 (SWZ13) and a mutant strain of SWZ13, even though no N-glycosylation sequons were changed. We determined site-specifically that WT and mutant HA have varying similarity at the glycosylation sites of the head domain, reflecting competing pressures to evade host immune response while retaining viral fitness. To our knowledge, our results are the first to quantify changes in glycosylation state that occur in related proteins of considerable glycan heterogeneity. Our results provide a method for understanding how changes in glycosylation state are correlated with variations in protein sequence, which is necessary for improving IAV vaccine strain selection. Understanding glycosylation will be especially important as we find new expression vectors for vaccine production, as glycosylation state depends greatly on the host species.

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

The Delaware Division of Public Health (DPH) announces that the Office of Vital Statistics (OVS) in Dover will be closed to the public for renovations beginning February 12 through April 5, 2024.

High Visibility Enforcement Against Impaired Driving Scheduled for March 11-21 in Maryland and Delaware Along US 13 and US 113 DOVER, DE (March 12, 2021) – This year St. Patrick’s Day may look a little different as Delaware continues to stress social distancing and COVID-19 precautions. But for those planning to celebrate with family and friends, […]

A previous article about how to display missing values in SAS prompted a comment about special missing values in ODS tables in SAS. Did you know that statistical tables in SAS include special missing values to represent certain situations in statistical analyses? This article explains how to interpret four special [...]

The post Special missing values in SAS statistical tables appeared first on The DO Loop.

As noted in white papers, posts on the Team Specman Blog, and the Specman documentation, IntelliGen is a totally new stimulus generator than the original "Pgen" and, as a result, there is some amount of effort needed to migrate an existing verification environment to fully leverage the power of IntelliGen.  One of the main steps in migrating code is running the linters on your code and adressing the issues highlighted. 

Included below is a simple utility you can include in your environment that allows you to collect some valuable statistics about your code base to allow you to better gauge the amount of work that might be required to migrate from Pgen to IntelliGen.  The ICFS statistics reported are of particular benefit as the utility not only identifies the approximate number of ICFSs in the environment, it also breaks the total number down according to generation contexts (structs/units and gen-on-the-fly statements) allowing you to better focus your migration efforts. 

IMPORTANT: Sometimes a given environment can trigger a large number of IntelliGen linting messages right off the bat.  Don't let this freak you out!  This does not mean that migration will be a long effort as quite often some slight changes to an environment remove a large number of identified issues.  I recently encountered a situation where a simple change to three locations in the environment, removed 500+ ICFSs!

The methods included in the utility can be used to report information on the following:
- Number of e modules
- Number of lines in the environment (including blanks and comments)
- Number and type of IntelliGen Guidelines linting messages
- Number of Inconsistently Connected Field Sets (ICFSs)
- Number of ICFS contexts and how many ICFSs per context
- Number of soft..select overlays found in the envioronment
- Number of Laces identified in the environment

To use the code below, simply load it before/after loading e-code and then
you can execute any of the following methods:

- sys.print_file_stats()             : prints # of lines and files
- sys.print_constraint_stats()   : prints # of constraints in the environment
- sys.print_guideline_stats()    : prints # of each type of linting message
- sys.print_icfs_stats()            : prints # of ICFSs, contexts and #ICFS/context
- sys.print_soft_select_stats() : prints # of soft select overlay issues
- sys.print_lace_stats()           : *Only works for SPMNv6.2s4 and later* prints # of laces identified in the environment

Each of the above calls to methods produces it's own log files (stored in the current working directory) containing relevant information for more detailed analysis.
- file_stats_log.elog : Output of "show modules" command
- constraint_log.elog : Output of the "show constraint" command
- guidelines_log.elog : Output of "gen lint -g" (with notification set to MAX_INT in order to get all warnings)
- icfs_log.elog       : Output of "gen lint -i" command
- soft_select_log.elog: Output of the "gen lint -s" command
- lace_log.elog       : Output of the "show lace" command

Happy generating!

Corey Goss

Decision makers need to analyze, exchange, and disseminate statistical information effectively in today’s data-driven world. See how Statistical Data and Metadata eXchange (SDMX) standards are helping create robust data links across the globe.

Climate change poses an increasing threat to people and their livelihoods. Record heat waves, catastrophic floods, prolonged droughts, and other extreme weather events in Asia and the Pacific are becoming more frequent.

to write a statistical report

Descriptive statistics (DS) play a crucial role in establishing a solid foundation for study analysis and are important for understanding the results of a study or data set. If the data from DS is used incorrectly, the study may be misinterpreted. Descriptive statistics summarizes and organizes data, making analysis easier and providing an overview of the characteristics of sampled data. This analysis is comprised of measures of central tendency, which includes the mean, median, and mode of a particular data set. Understanding how to use each metric is essential for basic statistical analysis. The purpose of this short report is to review descriptive statistics and describe how to best utilize them during data analysis. The authors aim to provide this short report as an educational resource to assist the dental hygiene research community in understanding statistical analysis through descriptive statistics.

The SARS-CoV-2 frameshifting element (FSE) has been intensely studied and explored as a therapeutic target for coronavirus diseases, including COVID-19. Besides the intriguing virology, this small RNA is known to adopt many length-dependent conformations, as verified by multiple experimental and computational approaches. However, the role these alternative conformations play in the frameshifting mechanism and how to quantify this structural abundance has been an ongoing challenge. Here, we show by DMS and dual-luciferase functional assays that previously predicted FSE mutants (using the RAG graph theory approach) suppress structural transitions and abolish frameshifting. Furthermore, correlated mutation analysis of DMS data by three programs (DREEM, DRACO, and DANCE-MaP) reveals important differences in their estimation of specific RNA conformations, suggesting caution in the interpretation of such complex conformational landscapes. Overall, the abolished frameshifting in three different mutants confirms that all alternative conformations play a role in the pathways of ribosomal transition.

This paper provides a high-level tour of five modern approaches to model building that are available in recent releases of SAS/STAT.

 This paper presents the essential information that you need to get started with ODS Graphics in SAS 9.4.

Announcement Number: DOE-20-EIA-ES-00885
Closing Date: 23 March 2020

Announcement Number: TN-19-EI-00849-DE
Closing Date: 14 January 2019

Announcement Number: TN-19-EI-00849-MP
Closing Date: 13 November 2024

Announcement Number: TN-19-EI-00869-DH
Closing Date: 12 September 2019

Announcement Number: SE-18-EI-00811-RCG
Closing Date: 21 May 2018

Announcement Number: HQ-10-MP-09-EI20-005
Closing Date: 11 December 2009

Announcement Number: HQ-10-DE-09-EI20-005
Closing Date: 11 December 2009

Announcement Number: TN-21-DOE-1529-OCDH
Closing Date: 30 September 2021

Announcement Number: SE-18-EI-00804-DE
Closing Date: 21 May 2018

Announcement Number: SE-18-EI-00804-MP
Closing Date: 21 May 2018

Announcement Number: TN-20-DOE-1529-OCDH-R
Closing Date: 28 May 2021

Announcement Number: TN-19-EI-00863-DH-R
Closing Date: 02 April 2020

Announcement Number: TN-19-EI-00863-DH
Closing Date: 02 April 2020

Announcement Number: PMF-2017-0393
Closing Date: 14 April 2017

Announcement Number: TN-21-DOE-1530-OCDH
Closing Date: 30 September 2021

Announcement Number: TN-20-DOE-1530-OCDH
Closing Date: 28 May 2021

Announcement Number: TN-19-EI-STEM-INTERN
Closing Date: 20 February 2019

Announcement Number: SE-18-EI-00820-DE
Closing Date: 10 August 2018

Announcement Number: SE-18-EI-00820-MP
Closing Date: 10 August 2018

Announcement Number: SE-18-EI-00796-MP
Closing Date: 17 January 2018

Announcement Number: SE-18-EI-00808-MP
Closing Date: 05 November 2018

Announcement Number: HQ-10-DE-10-EI20-006
Closing Date: 11 December 2009

Announcement Number: SE-18-EI-00808-DE
Closing Date: 05 November 2018

Announcement Number: TN-19-EI-00854-DH
Closing Date: 02 April 2020

Manchester : Manchester University Press, [2020]

London : Penguin, 2000, c1981.

Jeffrey Rosenthal, a professor of statistics at the University of Toronto, answers the internet's burning questions about statistics. What are the most common statistical errors? Why do polls get it so wrong? What's the worst casino game in terms of odds? How does probability work in roulette? Jeffrey answers all these questions and much more!

Statistical analyst and ex-NBA assistant coach Dean Oliver visits WIRED to answer sports math questions from the internet. What’s the most efficient shot to take on a basketball court? Are NFL running backs historically undervalued at the moment? Why are there more no-hitters in baseball lately? And statistically speaking who’s better: LeBron James or Michael Jordan? Answers to these questions and more await on Sports Math Support.