Analysis of an OpenMP Program for Race Detection

The race condition in a shared memory parallel program is subtle and harder to find than in a sequential program. The race conditions cause non-deterministic and unexpected results from the program. It should be avoided in the parallel region of OpenMP programs. The proposed OpenMP Race Avoidance Tool statically analyzes the parallel region. It gives alerts regarding possible data races in that parallel region. The proposed tool has the capability to analyze the basic frequently occurring non-nested ‘for loop(s)’. We are comparing the results of the proposed tool with the commercially available static analysis tool named Intel Parallel Lint and the dynamic analysis tool named Intel Thread Checker for race detection in OpenMP program. The proposed tool detects race conditions in the ‘critical’ region that have not been detected by existing analysis tools. The proposed tool also detects the race conditions for the ‘atomic’, ‘parallel’, ‘master’, ‘single’ and ‘barrier’ constructs. The OpenMP beginner programmers can use this tool to understand how to create a shared-memory parallel program

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Cancer Epidemiol Biomarkers Prev

Background:Limited literature is available about cancer in the Appalachian Region. This is the only known analysis of all cancers for Appalachia and non-Appalachia covering 100% of the US population. Appalachian cancer incidence and trends were evaluated by state, sex, and race and compared with those found in non-Appalachian regions.Methods:US counties were identified as Appalachian or non-Appalachian. Age-adjusted cancer incidence rates, standard errors, and confidence intervals were calculated using the most recent data from the United States Cancer Statistics for 2004 to 2011.Results:Generally, Appalachia carries a higher cancer burden compared with non-Appalachia, particularly for tobacco-related cancers. For all cancer sites combined, Appalachia has higher rates regardless of sex, race, or region. The Appalachia and non-Appalachia cancer incidence gap has narrowed, with the exception of oral cavity and pharynx, larynx, lung and bronchus, and thyroid cancers.Conclusions:Higher cancer incidence continues in Appalachia and appears at least in part to reflect high tobacco use and potential differences in socioeconomic status, other risk factors, patient health care utilization, or provider practices. It is important to continue to evaluate this population to monitor results from screening and early detection programs, understand behavioral risk factors related to cancer incidence, increase efforts to reduce tobacco use and increase cancer screening, and identify other areas where effective interventions may mediate disparities.Impact:Surveillance and evaluation of special populations provide means to monitor screening and early detection programs, understand behavioral risk factors, and increase efforts to reduce tobacco use to mediate disparities.CC999999/Intramural CDC HHS/United States2019-01-03T00:00:00Z26819264PMC6317710vault:3130

A Detection Method for Tropical Race 4 of the Banana Pathogen Fusarium oxysporum f. sp. cubense

Fusarium oxysporum f. sp. cubense (Foc) is the causal agent of Fusarium wilt, the devastating disease that ruined the ‘Gros Michel’ (AAA)-based banana production in the first half of the 20th century. The occurrence of a new variant in Southeast Asia that overcomes the resistance in Cavendish clones such as ‘Grand Naine’ (AAA) is a major concern to current banana production worldwide. The threat posed by this new variant, called tropical race 4 (TR4), may be overcome by the introduction of resistant cultivars. However, the identification of new resistant sources or breeding for resistance is a long-term effort. Currently, the only option to control the disease is to avoid or reduce the spread of the pathogen by eradication of infected plants and isolation of infested plantations. This requires sensitive and highly specific diagnostics that enable early detection of the pathogen. A two-locus database of DNA sequences, from over 800 different isolates from multiple formae speciales of F. oxysporum, was used to develop a molecular diagnostic tool that specifically detects isolates from the vegetative compatibility group (VCG) 01213, which encompasses the Foc TR4 genotype. This diagnostic tool was able to detect all Foc TR4 isolates tested, while none of the Foc isolates from 19 VCGs other than 01213 showed any reaction. In addition, the developed diagnostic tool was able to detect Foc TR4 when using DNA samples from different tissues of ‘Grand Naine’ plants inoculated with TR4 isolate

Nucleotide sequence and genomic organization of an ophiovirus associated with lettuce big-vein disease

The complete nucleotide sequence of an ophiovirus associated with lettuce big-vein disease has been elucidated. The genome consisted of four RNA molecules of approximately 7ò8, 1ò7, 1ò5 and 1ò4 kb. Virus particles were shown to contain nearly equimolar amounts of RNA molecules of both polarities. The 5'- and 3'-terminal ends of the RNA molecules are largely, but not perfectly, complementary to each other. The virus genome contains seven open reading frames. Database searches with the putative viral products revealed homologies with the RNA-dependent RNA polymerases of rhabdoviruses and Ranunculus white mottle virus, and the capsid protein of Citrus psorosis virus. The gene encoding the viral polymerase appears to be located on the RNA segment 1, while the nucleocapsid protein is encoded by the RNA3. No significant sequence similarities were observed with other viral proteins. In spite of the morphological resemblance with species in the genus Tenuivirus, the ophioviruses appear not to be evolutionary closely related to this genus nor any other viral genus

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

Evaluation of the Program Delivery of Every Women\u27s Life in Virginia

Introduction: Among women, breast cancer is the most prevalent cancer and the second leading cause of cancer death. Although technology advances have improved survival rates for breast cancer overall, improvements have not been universally experienced by all socioeconomic and racial groups. Known determinants of breast cancer care disparities include socioeconomic status, race, age, and social support. As a part of the Breast and Cervical Cancer Mortality Prevention Act of 1990 and with the help of CDC funding, the Virginia Breast and Cervical Cancer Early Detection Program (BCCEDP) or Every Woman’s Life (EWL) was created. EWL provides breast cancer screening to female VA residents between the ages of 18 and 64 who lack health insurance and fall at or below 200% of the Federal Poverty Level. Objective: The purpose of this study is to determine if delays in the diagnosis and treatment of breast cancer, within the VDH program EWL, differs based on sociodemographic characteristics and/ or regional location. Methods: From its inception to July 2008, 705 women received a breast cancer diagnosis through the EWL program. For these 705 cases prevalence and crude odds ratios were calculated for both diagnosis and treatment delays for all of the demographic variables along with 95% confidence intervals. Adjusted odds ratios were calculated for sociodemographic variables against screening to diagnosis delays and diagnosis to treatment disparities along with 95% confidence intervals. Results: According to the crude odds ratios more women who fall into the other category of race experienced diagnosis delays (OR=2.28 [1.11, 4.67]), but they were more likely to receive treatment in a timely manner (OR=0.29 [0.11, 0.79]). Women living alone were also more likely to experience diagnosis delays (OR=1.49 [1.10, 3.02]). Hispanic women were more likely to receive treatment in a more timely manner than non-Hispanic women (OR=0.21 [0.05, 0.81]). Also, women being treated in any other region than northern VA were more likely to experience treatment delays. However, according to the adjusted odds ratios, the only significant timing delay was the one experienced more often by women in the other race category. Conclusion: The research indicates known indicators of disparities within cancer care as socioeconomic status, race, ethnicity, age, and social support. The findings of this study indicate that the only significant indicator of disparity within the Every Women’s Life program is race. Although, African-American women were just as likely to receive timely diagnosis and treatment as white women in the program, it was the combined groups of Asian, American Indian, and other women that were more likely to experience diagnosis, but not treatment, delays. The fact that no other significant indicators of disparities were found within EWL indicates a success of the program, as EWL is targeting those women that would have otherwise been missed by the system