Search

Scientific datasets are growing rapidly and becoming critical to next-generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance. Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad-hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real-time (online) and offline analyses. We employ a variety of fast-to-compute numerical analyses, graphical data change representations, and more resource-intensive recomputations of a subset of the data product. We illustrate the application of our approach using three scientific diverse use cases, namely, satellite, cosmological, and x-ray data. The results show that a variety of data change metrics should be employed to enable a comprehensive representation and qualitative evaluation of data changes.

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three data producer scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We posit that a new mechanism is also needed to enable citation of multiple datasets and credit to data producers.

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

Scientific discoveries are increasingly dependent upon the analysis of large volumes of data from observations and simulations of complex phenomena. Scientists compose the complex analyses as workflows and execute them on large-scale HPC systems. The workflow structures are in contrast with monolithic single simulations that have often been the primary use case on HPC systems. Simultaneously, new storage paradigms such as Burst Buffers are becoming available on HPC platforms. In this paper, we analyze the performance characteristics of a Burst Buffer and two representative scientific workflows with the aim of optimizing the usage of a Burst Buffer, extending our previous analyses (Daley et al., 2016). Our key contributions are (a) developing a performance analysis methodology pertinent to Burst Buffers, (b) improving the use of a Burst Buffer in workflows with bandwidth-sensitive and metadata-sensitive I/O workloads, (c) highlighting the key data management challenges when incorporating a Burst Buffer in the studied scientific workflows.

Scientifc software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientifc knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientifc end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientifc software development work. The use of user research techniques in scientifc environments can be challenging due to the nascent, fuid problem spaces of scientifc work, varying scope of projects and their user communities, and funding/economic constraints on projects. In this paper, we refect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientifc environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes refections and the lessons we have learned from our experiences. We ofer key takeaways for scientifc software project teams to efectively and fexibly incorporate similar processes into their projects.

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of 512\times 512 pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope-transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning-i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem-to generalize labels and achieve an accuracy of approximately 70 % despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.

Data are essential products of scientific work that move among and through research infrastructures over time. Data constantly changes due to evolving practices and knowledge, requiring improvisational work by scientists to determine the effects on analyses. Today for end users of datasets much of the information about changes, and the processes leading to them, is invisible—embedded elsewhere in the work of a collaboration. Simultaneously scientists use increasing quantities of data, making ad hoc approaches to identifying change difficult to scale effectively. Our research investigates data change by examining how scientists make sense of change in datasets being created and sustained by the collaborative infrastructures they engage with. We examine two forms of change, before examining how trust and project rhythms influence a scientist’s notion that the newest available data are the best. We explore the opportunity to design tools and practices to support user examinations of data change and surface key provenance information embedded in research infrastructures.

Qualitative user research is a human-intensive approach that draws upon ethnographic methods from social sciences to develop insights about work practices to inform software design and development. Recent advances in data science, and in particular, natural language processing (NLP), enables the derivation of machine-generated insights to augment existing techniques. Our work describes our prototype framework based in Jupyter, a software tool that supports interactive data science and scientific computing, that leverages NLP techniques to make sense of transcribed texts from user interviews. This work also serves as a starting point for incorporating data science techniques in the qualitative analyses process.

Scientific facilities are increasingly generating and handling large amounts of data from experiments and simulations. Next-generation scientific discoveries rely on insights derived from data, especially across domain boundaries. Search capabilities are critical to enable scientists to discover datasets of interest. However, scientific datasets often lack the signals or metadata required for effective searches. Thus, we need formalized methods and systems to automatically annotate scientific datasets from the data and its surrounding context. Additionally, a search infrastructure needs to account for the scale and rate of application data volumes. In this paper, we present ScienceSearch, a system infrastructure that uses machine learning techniques to capture and learn the knowledge, context, and surrounding artifacts from data to generate metadata and enable search. Our current implementation is focused on a dataset from the National Center for Electron Microscopy (NCEM), an electron microscopy facility at Lawrence Berkeley National Laboratory sponsored by the Department of Energy which supports hundreds of users and stores millions of micrographs. In this paper, we describe a) our search infrastructure and model, b) methods for generating metadata using machine learning techniques, and c) optimizations to improve search latency, and deployment on an HPC system. We demonstrate that ScienceSearch is capable of producing valid metadata for NCEM's dataset and providing low-latency good quality search results over a scientific dataset.

High performance computing (HPC) scheduling landscape currently faces new challenges due to the changes in the workload. Previously, HPC centers were dominated by tightly coupled MPI jobs. HPC workloads increasingly include high-throughput, data-intensive, and stream-processing applications. As a consequence, workloads are becoming more diverse at both application and job levels, posing new challenges to classical HPC schedulers. There is a need to understand the current HPC workloads and their evolution to facilitate informed future scheduling research and enable efficient scheduling in future HPC systems. In this paper, we present a methodology to characterize workloads and assess their heterogeneity, at a particular time period and its evolution over time. We apply this methodology to the workloads of three systems (Hopper, Edison, and Carver) at the National Energy Research Scientific Computing Center (NERSC). We present the resulting characterization of jobs, queues, heterogeneity, and performance that includes detailed information of a year of workload (2014) and evolution through the systems’ lifetime (2010–2014).

The volume and variety of scientific data being generated at experimental facilities requires the seamless interaction of the scientist's knowledge with the large-scale machines and software that is required to process the data. In the last few years, scientific software tools are being developed to address these increasingly complex workflow and data management needs. However, current approaches for designing systems and tools focus on the hardware and software of the machine and do not consider the user. Our experience shows us that user experience research needs to be tightly integrated with the software development life cycle for building sustainable software for science. It has become not just necessary, but critical, to consider the user interaction in the design of the entire system for data-intensive sciences that have complex human interaction with the data, software and systems. The dynamic nature of science projects and the complex roles of personnel in the projects makes it difficult to apply classical user research methodologies from industry. In this paper, we make three specific contributions towards improving the usability and sustainability of scientific software. First, we examine the software life cycle in science environments and identify the differences with commercial software development. Next, we outline ten principles we have developed to guide user engagement and software development and illustrate it with examples from our projects over the last several years. Finally, we provide guidelines to other eScience projects on applying the ten principles in the software development life cycle.

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This paper describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. The visualization system is based on a JavaScript front end communicating via REST with a back end web server.

Reliable and fine resolution estimates of surface net-radiation are required for estimating latent and sensible heat fluxes between the land surface and the atmosphere. However, currently, fine resolution estimates of net-radiation are not available and consequently it is challenging to develop multi-year estimates of evapotranspiration at scales that can capture land surface heterogeneity and are relevant for policy and decision-making. We developed and evaluated a global net-radiation product at 5 km and 8-day resolution by combining mutually consistent atmosphere and land data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra. Comparison with net-radiation measurements from 154 globally distributed sites (414 site-years) from the FLUXNET and Surface Radiation budget network (SURFRAD) showed that the net-radiation product agreed well with measurements across seasons and climate types in the extratropics (Wilmott's index ranged from 0.74 for boreal to 0.63 for Mediterranean sites). Mean absolute deviation between the MODIS and measured net-radiation ranged from 38.0 ± 1.8 W.m-2 in boreal to 72.0 ± 4.1 W.m-2 in the tropical climates. The mean bias was small and constituted only 11%, 0.7%, 8.4%, 4.2%, 13.3%, and 5.4% of the mean absolute error in daytime net-radiation in boreal, Mediterranean, temperate-continental, temperate, semi-arid, and tropical climate, respectively. To assess the accuracy of the broader spatiotemporal patterns, we upscaled error-quantified MODIS net-radiation and compared it with the net-radiation estimates from the coarse spatial (1° x 1°) but high temporal resolution gridded net-radiation product from the Clouds and Earth's Radiant Energy System (CERES). Our estimates agreed closely with the net-radiation estimates from the CERES. Difference between the two was less than 10W.m-22 in 94% of the total land area. MODIS net-radiation product will be a valuable resource for the science community studying turbulent fluxes and energy budget at the Earth's surface.

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterative workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechansims) that affect the performance of scientific workflows on HPC systems.

Scientific data volumes have been growing exponentially. This has resulted in the need for new tools that enable users to operate on and analyze data. Cyber infrastructure tools, including workflow tools, that have been developed in the last few years has often fallen short if user needs and suffered from lack of wider adoption. User-centered Design (UCD) process has been used as an effective approach to develop usable software with high adoption rates. However, UCD has largely been applied for user-interfaces and there has been limited work in applying UCD to application program interfaces and cyber infrastructure tools. We use an adapted version of UCD that we refer to as Scientist-Centered Design (SCD) to engage with users in the design and development of Tigres, a workflow application programming interface. Tigres provides a simple set of programming templates (e.g., sequence, parallel, split, merge) that can be can used to compose and execute computational and data transformation pipelines. In this paper, we describe Tigres and discuss our experiences with the use of UCD for the initial development of Tigres. Our experience-to-date is that the UCD process not only resulted in better requirements gathering but also heavily influenced the architecture design and implementation details. User engagement during the development of tools such as Tigres is critical to ensure usability and increase adoption.

Vinnyfred Vincent,1 Himani Thakkar,1 Sandeep Aggarwal,2 Asit Ranjan Mridha,3 Lakshmy Ramakrishnan,4 Archna Singh1 1Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India; 2Department of Surgical Disciplines, All India Institute of Medical Sciences, New Delhi, India; 3Department of Pathology, All India Institute of Medical Sciences, New Delhi, India; 4Department of Cardiac Biochemistry, All India Institute of Medical Sciences, New Delhi, India Purpose: Adipose tissue dysfunction is at the center of metabolic dysfunctions associated with obesity. Through studies in isolated adipocytes and mouse models, ATP-binding cassette transporter A1 (ABCA1) expression in the adipose tissue has been shown to regulate high-density lipoprotein (HDL) cholesterol levels in the circulation and insulin sensitivity at both adipose tissue and whole-body levels. We aimed to explore the possible link between ABCA1 expression in the adipose tissue and metabolic derangements associated with obesity in humans.Patients and methods: This exploratory study among individuals who were lean (body mass index [BMI]: 22.3±0.34 kg/m2, n=28) and obese (BMI: 44.48±5.3 kg/m2, n=34) compared the expression of ABCA1, adiponectin and GLUT4 (SLC2A4) in visceral and subcutaneous adipose tissue using quantitative real-time PCR and immunohistochemistry. Homeostatic model assessment for insulin resistance (HOMA-IR) and adipose tissue insulin resistance (adipo-IR) were used as insulin resistance markers.Results: Visceral adipose tissue from individuals who were obese had significantly lower ABCA1 (P=0.04 for mRNA and protein) and adiponectin (P=0.001 for mRNA) expression compared to that from lean individuals. Subcutaneous adipose tissue did not show any significant difference in the expression. When individuals were divided into insulin-sensitive (IS) and insulin-resistant (IR) groups based on HOMA-IR, IR individuals had lower ABCA1 (P=0.0001 for mRNA and P=0.009 for protein) expression compared to IS individuals in visceral adipose tissue, but not in subcutaneous adipose tissue. The difference was significant after adjusting for age, gender and BMI. ABCA1 mRNA expression in visceral adipose tissue correlated negatively with both HOMA-IR (r=–0.44, P=0.0003) and adipo-IR (r=–0.35, P=0.005) after adjusting for age, gender and BMI. ABCA1 expression in either visceral or subcutaneous adipose tissue did not have any significant correlation with HDL cholesterol levels or mean adipocyte area.Conclusion: Obesity and insulin resistance are associated with lower expression of ABCA1 in visceral adipose tissue in humans. Keywords: metabolic syndrome, cholesterol, diabetes, adiponectin

Vincent V, Thakkar H, Aggarwal S, et al. Diabetes Metab Syndr Obes. 2019:12;275–284.On page 283, the Acknowledgements section should be presented as follows: The authors would like to acknowledge Dr Manjunath Maruti Pol, Dr Harshit Garg and Dr Abhineet KP for helping with the collection of adipose tissue samples and Mr Rintu Kutum for helping with the statistical analysis. Dr Vincent would like to acknowledge the contributions of Dr Anurag Kumar, Dr Ambrish Tiwari and Dr Prajwal Aathreya. This study was supported by The Science & Engineering Research Board (SERB), Department of Science and Technology (DST), India (reference number: EMR/2016/006018) and intramural research grant from All India Institute of Medical Sciences, New Delhi to Dr Archna Singh. The sponsors did not have any role in any of the stages from study design to submission of thepaper for publication.Following a review of the paper post-publication, the authors found an incorrect sentence in the Acknowledgements section: “This study was supported by an intramural research grant from All India Institute of Medical Sciences, New Delhi to Dr Archna Singh.” This sentence is now shown correctly as “This study was supported by The Science & Engineering Research Board (SERB), Department ofScience and Technology (DST), India (reference number: EMR/2016/006018) and intramural research grant from All India Institute of Medical Sciences, New Delhi to Dr Archna Singh”.Read the original article

Abstract Background Avahan, the India AIDS Initiative, a large-scale HIV prevention program, using peer-mediated approaches and STI services, was implemented for high-risk groups for HIV in six states in India. This paper describes the assessment of the program among female sex workers (FSWs) in the southern state of Tamil Nadu. Methods An analytical framework based on the Avahan impact evaluation design was used. Routine program monitoring data, two rounds of cross-sectional biological and behavioural surveys among FSWs in 2006 (Round 1) and 2009 (Round 2) and quality assessments of clinical services for sexually transmitted infections (STIs) were used to assess trends in coverage, condom use and prevalence of STIs, HIV and their association with program exposure. Logistic regression analysis was used to examine trends in intermediate outcomes and their associations with intervention exposure. Results The Avahan program in Tamil Nadu was scaled up and achieved monthly reported coverage of 79% within four years of implementation. The cross-sectional survey data showed an increasing proportion of FSWs being reached by Avahan, 54% in Round 1 and 86% in Round 2 [AOR=4.7;p=0.001]. Quality assessments of STI clinical services showed consistent improvement in quality scores (3.0 in 2005 to 4.5 in 2008). Condom distribution by the program rose to cover all estimated commercial sex acts. Reported consistent condom use increased between Round 1 and Round 2 with occasional (72% to 93%; AOR=5.5; p=0.001) and regular clients (68% to 89%; AOR=4.3; p=0.001) while reactive syphilis serology declined significantly (9.7% to 2.2% AOR=0.2; p=0.001). HIV prevalence remained stable at 6.1% between rounds. There was a strong association between Avahan exposure and consistent condom use with commercial clients; however no association was seen with declines in STIs. Conclusions The Avahan program in Tamil Nadu achieved high coverage of FSWs, resulting in outcomes of improved condom use, declining syphilis and stabilizing HIV prevalence. These expected outcomes following the program logic model and declining HIV prevalence among general population groups suggest potential impact of high risk group interventions on HIV epidemic in Tamil Nadu.

Objective To establish a surveillance network for cardiovascular diseases (CVD) risk factors in industrial settings and estimate the risk factor burden using standardized tools. Methods We conducted a baseline cross-sectional survey (as part of a CVD surveillance programme) of industrial populations from 10 companies across India, situated in close proximity to medical colleges that served as study centres. The study subjects were employees (selected by age and sex stratified random sampling) and their family members. Information on behavioural, clinical and biochemical determinants was obtained through standardized methods (questionnaires, clinical measurements and biochemical analysis). Data collation and analyses were done at the national coordinating centre. Findings We report the prevalence of CVD risk factors among individuals aged 20-69 years (n = 19 973 for the questionnaire survey, n = 10 442 for biochemical investigations); mean age was 40 years. The overall prevalence of most risk factors was high, with 50.9% of men and 51.9% of women being overweight, central obesity was observed among 30.9% of men and 32.8% of women, and 40.2% of men and 14.9% of women reported current tobacco use. Self-reported prevalence of diabetes (5.3%) and hypertension (10.9%)was lower than when measured clinically and biochemically (10.1% and 27.7%, respectively). There was marked heterogeneity in the prevalence of risk factors among the study centres. Conclusion There is a high burden of CVD risk factors among industrial populations across India. The surveillance system can be used as a model for replication in India as well as other developing countries. Resume Methodes pour mettre en place un reseau de surveillance des maladies cardiovasculaires chez les emploves d'entreprises industrielles indiennes et leurs families Objectif Mettre en place un systeme de surveillance des facteurs de risques de maladie cardiovasculaire (MCM) sur des sites industriels et estimer au moyen d'outils standardises la charge de facteurs de risque. Methodes Une etude transversale de reference a ete menee (dans le cadre d'un programme de surveillance des MCM) parmi les employes de 10 entreprises industrielles et leurs families, ces entreprises etant situees dans differentes regions de l'lnde et proximite de facultes de medecine qui ont servi de centres d'etude. Les sujets de l'etude ont ete selectionnes parmi les employes et leurs families (echantillon aleatoire stratifie selon l'age et le sexe). Des informations relatives aux determinants comportementaux, cliniques et biochimiques ont ete obtenues par des methodes standardisees (questionnaires, mesures cliniques et analyse biochimique). La collecte des donnees et les analyses ont ete effectuees par le centre coordinateur national. Resultats L'article indique la prevalence des facteurs de risque de MCV parmi des sujets de 20 a 69 ans (n = 19 973 pour l'enquete par questionnaire, n = 10 442 pour les etudes biochimiques), dont l'age moyen etait de 40 ans. Il relive une forte prevalence globale de la plupart des facteurs de risque : 50,9 % des hommes et 51,9 % des femmes presentent une surcharge ponderale, 30,9 % des hommes et 32,8 % des femmes souffrent d'une obesite centrale, 40,2 % des hommes et 14,9 % des femmes signalent qu'ils ou elles fument actuellement du tabac. Les prevalences autorapportees du diabete (5,3 %) et de l'hypertension (10,9 %) sont plus faibles que celles mesurees par des examens cliniques et biochimiques (10,1% et 27,7 % respectivement). On constate une heterogeneite marquee de la prevalence des facteurs de risque entre les centres d'etude. Conclusion La charge de facteurs de risque de MCV relevee chez les employes d'entreprises industrielles reparties a travers l'lnde et leurs families est elevee. Le systeme de surveillance etabli peut etre utilise comme module pour etre transpose a d'autres populations Indiennes et a d'autres pays en developpement. Resumen Metodos para establecer un sistema de vigilancia de las enfermedades cardiovasculares en poblaciones industriales de la India Objetivo Establecer una red de vigilancia de los factores de riesgo de enfermedades cardiovasculares (ECV) en entornos industriales y estimar la carga de factores de riesgo utilizando instrumentos normalizados. Metodos Llevamos a cabo una encuesta transversal basal (como parte de un programa de vigilancia de las ECV) de las poblaciones industriales de 10 empresas de la India, situadas muy cerca de las escuelas de medicina empleadas como centros de estudio. Los sujetos estudiados eran empleados de esas empresas (seleccionados mediante muestreo aleatorio estratificado por edad y sexo) y sus familiares, y la informacion referente a los determinantes comportamentales, clinicos y bioquimicos se obtuvo mediante metodos normalizados (cuestionarios, variables clinicas y analisis bioquimicos). La recopilacion y el analisis de los datos se hicieron en el centro coordinador nacional. Resultados Notificamos aqui la prevalencia de los factores de riesgo de ECV entre los individuos de 20 a 69 anos (n = 19 973 para la encuesta mediante cuestionario, n = 10 442 para los analisis bioquimicos), con una edad media de 40 anos. La prevalencia general de la mayoria de los factores de riesgo fue alta: exceso de peso en el 50,9% de los hombres y el 51,9% de las mujeres, obesidad central en el 30,9% de los hombres y el 32,8% de las mujeres, y consumo de tabaco en el 40,2% de los hombres y el 14,9% de las mujeres. La prevalencia autonotificada de diabetes (5,3%) e hipertension (10,9%) fue menor que la determinada clinica y bioquimicamente (10,1% y 27,7%, respectivamente). Se observo una notable heterogeneidad en la prevalencia de factores de riesgo entre los centros de estudio. Conclusion Existe una alta carga de factores de riesgo de ECV entre las poblaciones industriales en la India. El sistema de vigilancia puede servir de modelo para repetir la experiencia en este pais yen otros paises en desarrollo., [TEXT NOT REPRODUCIBLE IN ASCII] Introduction Cardiovascular diseases (CVDs are major contributors to the global burden of chronic diseases with 29.3% of global deaths and 9.9% of total disease burden, [...]

Byline: R. Srivastava, A. Batra, A. Tyagi, D. Dhawan, L. Ramakrishnan, S. Bakhshi Background: Acute lymphoblastic leukemia survivors are predisposed to obesity. However, the exact underlying mechanisms are not known. [...]