A systematic review of the routine monitoring of growth in children of primary school age to identify growth-related conditions

Objectives: To clarify the role of growth monitoring in primary school children, including obesity, and to examine issues that might impact on the effectiveness and cost-effectiveness of such programmes. Data sources: Electronic databases were searched up to July 2005. Experts in the field were also consulted. Review methods: Data extraction and quality assessment were performed on studies meeting the review's inclusion criteria. The performance of growth monitoring to detect disorders of stature and obesity was evaluated against National Screening Committee (NSC) criteria. Results: In the 31 studies that were included in the review, there were no controlled trials of the impact of growth monitoring and no studies of the diagnostic accuracy of different methods for growth monitoring. Analysis of the studies that presented a 'diagnostic yield' of growth monitoring suggested that one-off screening might identify between 1: 545 and 1: 1793 new cases of potentially treatable conditions. Economic modelling suggested that growth monitoring is associated with health improvements [ incremental cost per quality-adjusted life-year (QALY) of pound 9500] and indicated that monitoring was cost-effective 100% of the time over the given probability distributions for a willingness to pay threshold of pound 30,000 per QALY. Studies of obesity focused on the performance of body mass index against measures of body fat. A number of issues relating to human resources required for growth monitoring were identified, but data on attitudes to growth monitoring were extremely sparse. Preliminary findings from economic modelling suggested that primary prevention may be the most cost-effective approach to obesity management, but the model incorporated a great deal of uncertainty. Conclusions: This review has indicated the potential utility and cost-effectiveness of growth monitoring in terms of increased detection of stature-related disorders. It has also pointed strongly to the need for further research. Growth monitoring does not currently meet all NSC criteria. However, it is questionable whether some of these criteria can be meaningfully applied to growth monitoring given that short stature is not a disease in itself, but is used as a marker for a range of pathologies and as an indicator of general health status. Identification of effective interventions for the treatment of obesity is likely to be considered a prerequisite to any move from monitoring to a screening programme designed to identify individual overweight and obese children. Similarly, further long-term studies of the predictors of obesity-related co-morbidities in adulthood are warranted. A cluster randomised trial comparing growth monitoring strategies with no growth monitoring in the general population would most reliably determine the clinical effectiveness of growth monitoring. Studies of diagnostic accuracy, alongside evidence of effective treatment strategies, could provide an alternative approach. In this context, careful consideration would need to be given to target conditions and intervention thresholds. Diagnostic accuracy studies would require long-term follow-up of both short and normal children to determine sensitivity and specificity of growth monitoring.

Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Problems in four-dimensional data assimilation

With the introduction of new observing systems based on asynoptic observations, the analysis problem has changed in character. In the near future we may expect that a considerable part of meteorological observations will be unevenly distributed in four dimensions, i.e. three dimensions in space and one in time. The term analysis, or objective analysis in meteorology, means the process of interpolating observed meteorological observations from unevenly distributed locations to a network of regularly spaced grid points. Necessitated by the requirement of numerical weather prediction models to solve the governing finite difference equations on such a grid lattice, the objective analysis is a three-dimensional (or mostly two-dimensional) interpolation technique. As a consequence of the structure of the conventional synoptic network with separated data-sparse and data-dense areas, four-dimensional analysis has in fact been intensively used for many years. Weather services have thus based their analysis not only on synoptic data at the time of the analysis and climatology, but also on the fields predicted from the previous observation hour and valid at the time of the analysis. The inclusion of the time dimension in objective analysis will be called four-dimensional data assimilation. From one point of view it seems possible to apply the conventional technique on the new data sources by simply reducing the time interval in the analysis-forecasting cycle. This could in fact be justified also for the conventional observations. We have a fairly good coverage of surface observations 8 times a day and several upper air stations are making radiosonde and radiowind observations 4 times a day. If we have a 3-hour step in the analysis-forecasting cycle instead of 12 hours, which is applied most often, we may without any difficulties treat all observations as synoptic. No observation would thus be more than 90 minutes off time and the observations even during strong transient motion would fall within a horizontal mesh of 500 km * 500 km.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Are Reanalysis Data Useful for Calculating Climate Indices over South America?

Precipitation and temperature climate indices are calculated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and validated against observational data from some stations over Brazil and other data sources. The spatial patterns of the climate indices trends are analyzed for the period 1961-1990 over South America. In addition, the correlation and linear regression coefficients for some specific stations were also obtained in order to compare with the reanalysis data. In general, the results suggest that NCEP/NCAR reanalysis can provide useful information about minimum temperature and consecutive dry days indices at individual grid cells in Brazil. However, some regional differences in the climate indices trends are observed when different data sets are compared. For instance, the NCEP/NCAR reanalysis shows a reversal signal for all rainfall annual indices and the cold night index over Argentina. Despite these differences, maps of the trends for most of the annual climate indices obtained from the NCEP/NCAR reanalysis and BRANT analysis are generally in good agreement with other available data sources and previous findings in the literature for large areas of southern South America. The pattern of trends for the precipitation annual indices over the 30 years analyzed indicates a change to wetter conditions over southern and southeastern parts of Brazil, Paraguay, Uruguay, central and northern Argentina, and parts of Chile and a decrease over southwestern South America. All over South America, the climate indices related to the minimum temperature (warm or cold nights) have clearly shown a warming tendency; however, no consistent changes in maximum temperature extremes (warm and cold days) have been observed. Therefore, one must be careful before suggesting an), trends for warm or cold days.

Discrepancies in solar irradiation data for Stockholm and Athens

The aim of this study is to evaluate the variation of solar radiation data between different data sources that will be free and available at the Solar Energy Research Center (SERC). The comparison between data sources will be carried out for two locations: Stockholm, Sweden and Athens, Greece. For the desired locations, data is gathered for different tilt angles: 0°, 30°, 45°, 60° facing south. The full dataset is available in two excel files: “Stockholm annual irradiation” and “Athens annual irradiation”. The World Radiation Data Center (WRDC) is defined as a reference for the comparison with other dtaasets, because it has the highest time span recorded for Stockholm (1964–2010) and Athens (1964–1986), in form of average monthly irradiation, expressed in kWh/m2. The indicator defined for the data comparison is the estimated standard deviation. The mean biased error (MBE) and the root mean square error (RMSE) were also used as statistical indicators for the horizontal solar irradiation data. The variation in solar irradiation data is categorized in two categories: natural or inter-annual variability, due to different data sources and lastly due to different calculation models. The inter-annual variation for Stockholm is 140.4kWh/m2 or 14.4% and 124.3kWh/m2 or 8.0% for Athens. The estimated deviation for horizontal solar irradiation is 3.7% for Stockholm and 4.4% Athens. This estimated deviation is respectively equal to 4.5% and 3.6% for Stockholm and Athens at 30° tilt, 5.2% and 4.5% at 45° tilt, 5.9% and 7.0% at 60°. NASA’s SSE, SAM and RETScreen (respectively Satel-light) exhibited the highest deviation from WRDC’s data for Stockholm (respectively Athens). The essential source for variation is notably the difference in horizontal solar irradiation. The variation increases by 1-2% per degree of tilt, using different calculation models, as used in PVSYST and Meteonorm. The location and altitude of the data source did not directly influence the variation with the WRDC data. Further examination is suggested in order to improve the methodology of selecting the location; Examining the functional dependence of ground reflected radiation with ambient temperature; variation of ambient temperature and its impact on different solar energy systems; Im pact of variation in solar irradiation and ambient temperature on system output.

An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing

Satellite remote sensing of ocean colour is the only method currently available for synoptically measuring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical and ecological methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this study, several of these techniques were evaluated against in situ observations to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). The techniques are applied to a 10-year ocean-colour data series from the SeaWiFS satellite sensor and compared with in situ data (6504 samples) from a variety of locations in the global ocean. Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. Detection of microplankton and picoplankton were generally better than detection of nanoplankton. Abundance-based approaches were shown to provide better spatial retrieval of PSCs. Individual model performance varied according to PSC, input satellite data sources and in situ validation data types. Uncertainty in the comparison procedure and data sources was considered. Improved availability of in situ observations would aid ongoing research in this field. (C) 2010 Elsevier B.V. All rights reserved.

Ambiente data cleaning: suporte extensível, semântico e automático para análise e transformação de dados

Pós-graduação em Ciência da Computação - IBILCE

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Multiplicity of data in trial reports and the reliability of meta-analyses: empirical study

Objectives To examine the extent of multiplicity of data in trial reports and to assess the impact of multiplicity on meta-analysis results. Design Empirical study on a cohort of Cochrane systematic reviews. Data sources All Cochrane systematic reviews published from issue 3 in 2006 to issue 2 in 2007 that presented a result as a standardised mean difference (SMD). We retrieved trial reports contributing to the first SMD result in each review, and downloaded review protocols. We used these SMDs to identify a specific outcome for each meta-analysis from its protocol. Review methods Reviews were eligible if SMD results were based on two to ten randomised trials and if protocols described the outcome. We excluded reviews if they only presented results of subgroup analyses. Based on review protocols and index outcomes, two observers independently extracted the data necessary to calculate SMDs from the original trial reports for any intervention group, time point, or outcome measure compatible with the protocol. From the extracted data, we used Monte Carlo simulations to calculate all possible SMDs for every meta-analysis. Results We identified 19 eligible meta-analyses (including 83 trials). Published review protocols often lacked information about which data to choose. Twenty-four (29%) trials reported data for multiple intervention groups, 30 (36%) reported data for multiple time points, and 29 (35%) reported the index outcome measured on multiple scales. In 18 meta-analyses, we found multiplicity of data in at least one trial report; the median difference between the smallest and largest SMD results within a meta-analysis was 0.40 standard deviation units (range 0.04 to 0.91). Conclusions Multiplicity of data can affect the findings of systematic reviews and meta-analyses. To reduce the risk of bias, reviews and meta-analyses should comply with prespecified protocols that clearly identify time points, intervention groups, and scales of interest.

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review

OBJECTIVE: To determine the accuracy of magnetic resonance imaging criteria for the early diagnosis of multiple sclerosis in patients with suspected disease. DESIGN: Systematic review. DATA SOURCES: 12 electronic databases, citation searches, and reference lists of included studies. Review methods Studies on accuracy of diagnosis that compared magnetic resonance imaging, or diagnostic criteria incorporating such imaging, to a reference standard for the diagnosis of multiple sclerosis. RESULTS: 29 studies (18 cohort studies, 11 other designs) were included. On average, studies of other designs (mainly diagnostic case-control studies) produced higher estimated diagnostic odds ratios than did cohort studies. Among 15 studies of higher methodological quality (cohort design, clinical follow-up as reference standard), those with longer follow-up produced higher estimates of specificity and lower estimates of sensitivity. Only two such studies followed patients for more than 10 years. Even in the presence of many lesions (> 10 or > 8), magnetic resonance imaging could not accurately rule multiple sclerosis in (likelihood ratio of a positive test result 3.0 and 2.0, respectively). Similarly, the absence of lesions was of limited utility in ruling out a diagnosis of multiple sclerosis (likelihood ratio of a negative test result 0.1 and 0.5). CONCLUSIONS: Many evaluations of the accuracy of magnetic resonance imaging for the early detection of multiple sclerosis have produced inflated estimates of test performance owing to methodological weaknesses. Use of magnetic resonance imaging to confirm multiple sclerosis on the basis of a single attack of neurological dysfunction may lead to over-diagnosis and over-treatment.

Prenatal diagnosis and treatment planning of congenital heart defects-possibilities and limits

BACKGROUND: Newborns with hypoplastic left heart syndrome (HLHS) or right heart syndrome or other malformations with a single ventricle physiology and associated hypoplasia of the great arteries continue to be a challenge in terms of survival. The vast majority of these forms of congenital heart defects relate to abnormal morphogenesis during early intrauterine development and can be diagnosed accurately by fetal echocardiography. Early knowledge of these conditions not only permits a better understanding of the progression of these malformations but encourages some researchers to explore new minimally invasive therapeutic options with a view to early pre- and postnatal cardiac palliation. DATA SOURCES: PubMed database was searched with terms of "congenital heart defects", "fetal echocardiography" and "neonatal cardiac surgery". RESULTS: At present, early prenatal detection has been applied for monitoring pregnancy to avoid intrauterine cardiac decompensation. In principle, the majority of congenital heart defects can be diagnosed by prenatal echocardiography and the detection rate is 85%-95% at tertiary perinatal centers. The majority, particularly of complex congenital lesions, show a steadily progressive course including subsequent secondary phenomena such as arrhythmias or myocardial insufficiency. So prenatal treatment of an abnormal fetus is an area of perinatal medicine that is undergoing a very dynamic development. Early postnatal treatment is established for some time, and prenatal intervention or palliation is at its best experimental stage in individual cases. CONCLUSION: The upcoming expansion of fetal cardiac intervention to ameliorate critically progressive fetal lesions intensifies the need to address issues about the adequacy of technological assessment and patient selection as well as the morbidity of those who undergo these procedures.

3G network traffic sources measurement and analysis

In this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows. Copyright © 2013 John Wiley & Sons, Ltd.

A new methodological protocol for the use of dendrogeomorphological data in flood risk analysis

Dendrogeomorphology uses information sources recorded in the roots, trunks and branches of trees and bushes located in the fluvial system to complement (or sometimes even replace) systematic and palaeohydrological records of past floods. The application of dendrogeomorphic data sources and methods to palaeoflood analysis over nearly 40 years has allowed improvements to be made in frequency and magnitude estimations of past floods. Nevertheless, research carried out so far has shown that the dendrogeomorphic indicators traditionally used (mainly scar evidence), and their use to infer frequency and magnitude, have been restricted to a small, limited set of applications. New possibilities with enormous potential remain unexplored. New insights in future research of palaeoflood frequency and magnitude using dendrogeomorphic data sources should: (1) test the application of isotopic indicators (16O/18O ratio) to discover the meteorological origin of past floods; (2) use different dendrogeomorphic indicators to estimate peak flows with 2D (and 3D) hydraulic models and study how they relate to other palaeostage indicators; (3) investigate improved calibration of 2D hydraulic model parameters (roughness); and (4) apply statistics-based cost–benefit analysis to select optimal mitigation measures. This paper presents an overview of these innovative methodologies, with a focus on their capabilities and limitations in the reconstruction of recent floods and palaeofloods.