An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

Abstract Background Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes. Results This paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra. Conclusions This paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

Atmospheric pollution: influence on hospital admissions in paediatric rheumatic diseases

Objective: To investigate the lag structure effects from exposure to atmospheric pollution in acute outbursts in hospital admissions of paediatric rheumatic diseases (PRDs). Methods: Morbidity data were obtained from the Brazilian Hospital Information System in seven consecutive years, including admissions due to seven PRDs (juvenile idiopathic arthritis, systemic lupus erythematosus, dermatomyositis, Henoch-Schonlein purpura, polyarteritis nodosa, systemic sclerosis and ankylosing spondylitis). Cases with secondary diagnosis of respiratory diseases were excluded. Daily concentrations of inhaled particulate matter (PM10), sulphur dioxide (SO2) nitrogen dioxide (NO2), ozone (O-3) and carbon monoxide (CO) were evaluated. Generalized linear Poisson regression models controlling for short-term trend, seasonality, holidays, temperature and humidity were used. Lag structures and magnitude of air pollutants' effects were adopted to estimate restricted polynomial distributed lag models. Results: The total number of admissions due to acute outbursts PRD was 1,821. The SO2 interquartile range (7.79 mu g/m(3)) was associated with an increase of 1.98% (confidence interval 0.25-3.69) in the number of hospital admissions due to outcome studied after 14 days of exposure. This effect was maintained until day 17. Of note, the other pollutants, with the exception of O-3, showed an increase in the number of hospital admissions from the second week. Conclusion: This study is the first to demonstrate a delayed association between SO2 and PRD outburst, suggesting that oxidative stress reaction could trigger the inflammation of these diseases. Lupus (2012) 21, 526-533.

Accuracy of Screening for Potentially Malignant Disorders of the Oral Mucosa by Dentists in Primary Care

Purpose: To examine the accuracy of a screening programme for potentially malignant disorders of the oral mucosa by visual inspection in primary health care. Materials and Methods: The study was based on secondary data from the Primary Care Information System maintained by seven units of family health in Sao Paulo City managed by a non-governmental agency. The reference population was composed of 15,072 residents 50 years old or more of both genders. The study population comprised 2,980 individuals. During screening in community settings, the oral mucosa was examined by trained dentists and distributed into two categories: (a) screen negative (b) screen positive. All participants underwent comprehensive clinical exams by a general dental practitioner supervised by a specialist. Individual records were grouped in a working dataset. Point and 95% confidence interval estimates were calculated regarding measures of sensitivity (Se), specificity (Sp) and positive and negative predictive values (PPV and NPV, respectively). Results: 18.0% of the population was considered screen positive. A total of 133 lesions (4.5%) were identified and 8 cases of oral cancer were confirmed, which corresponded to a prevalence rate of 27 cases in 10,000 people, a much higher rate than expected. The measures found were Se: 91.7% (85.3-95.6), Sp: 85.4% (84.1-86.7), PPV: 22.7% (19.3-26.5), NPV: 99.5% (99.2-99.8). The visual screen presented high accuracy. Conclusion: The test presented high sensibility and specificity values. From a public health point of view, the high accuracy levels showed the importance of oral health teams on family health strategy for more comprehensive primary care. Targeting risk groups and delegating the screening to community health agents may improve PPV and coverage.

GeoHealth: A Georeferenced System for Health Data Analysis in Primary Care

The Primary Care Information System (SIAB) concentrates basic healthcare information from all different regions of Brazil. The information is collected by primary care teams on a paper-based procedure that degrades the quality of information provided to the healthcare authorities and slows down the process of decision making. To overcome these problems we propose a new data gathering application that uses a mobile device connected to a 3G network and a GPS to be used by the primary care teams for collecting the families' data. A prototype was developed in which a digital version of one SIAB form is made available at the mobile device. The prototype was tested in a basic healthcare unit located in a suburb of Sao Paulo. The results obtained so far have shown that the proposed process is a better alternative for data collecting at primary care, both in terms of data quality and lower deployment time to health care authorities.

Sudden Cardiac Death in Brazil: Study Based on Physicians' Perceptions of the Public Health Care System

Background: There are no available statistical data about sudden cardiac death in Brazil. Therefore, this study has been conducted to evaluate the incidence of sudden cardiac death in our population and its implications. Methods: The research methodology was based on Thurstone's Law of Comparative Judgment, whose premise is that the more an A stimulus differs from a B stimulus, the greater will be the number of people who will perceive this difference. This technique allows an estimation of actual occurrences from subjective perceptions, when compared to official statistics. Data were collected through telephone interviews conducted with Primary and Secondary Care physicians of the Public Health Service in the Metropolitan Area of Sao Paulo (MASP). Results: In the period from October 19, 2009, to October 28, 2009, 196 interviews were conducted. The incidence of 21,270 cases of sudden cardiac death per year was estimated by linear regression analysis of the physicians responses and data from the Mortality Information System of the Brazilian Ministry of Health, with the following correlation and determination coefficients: r = 0.98 and r2= 0.95 (95% confidence interval 0.81.0, P < 0.05). The lack of waiting list for specialized care and socioadministrative problems were considered the main barriers to tertiary care access. Conclusions: The incidence of sudden cardiac death in the MASP is high, and it was estimated as being higher than all other causes of deaths; the extrapolation technique based on the physicians perceptions was validated; and the most important bureaucratic barriers to patient referral to tertiary care have been identified. (PACE 2012; 35:13261331)

Ecosystem approach and the Fuzzy logic: a dialectical proposal for information on Environmental Health

The ever-growing production and the problematization of Environmental Health have shown the need to apprehend complex realities and deal with uncertainties from the most diversified instruments which may even incorporate local aspects and subjectivities by means of qualitative realities, while broadening the capacity of the information system. This paper presents a view on the reflection upon some challenges and possible convergences between the ecosystemic approach and the Fuzzy logic in the process of dealing with scientific information and decision-making in Environmental Health.