Moderate Precision Research Articles

Readmission of ICU patients: A quality indicator?

PurposeReadmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate. Material and methodsIn a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores. ResultsIn all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70. ConclusionsReadmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator.

Network design for quantifying urban CO<sub>2</sub> emissions: assessing trade-offs between precision and network density

Abstract. The majority of anthropogenic CO2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO2 Observation Network (BEACO2N) in California's Bay Area, in combination with an inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km2. The model uses an hourly 1 × 1 km2 emission inventory and 1 × 1 km2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO2 emissions. We examine uncertainty in estimated CO2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO2N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.

Diagnosis-based emergency department alcohol harm surveillance: What can it tell us about acute alcohol harms at the population level?

Introduction and AimsAcute harm from heavy drinking episodes is an increasing focus of public health policy, but capturing timely data on acute harms in the population is challenging. This study aimed to evaluate the precision of readily available administrative emergency department (ED) data in public health surveillance of acute alcohol harms.Design and MethodsWe selected a random sample of 1000 ED presentations assigned an ED diagnosis code for alcohol harms (the ‘alcohol syndrome’) in the New South Wales, Australia, automatic syndromic surveillance system. The sample was selected from 68 public hospitals during 2014. Nursing triage free‐text fields were independently reviewed to confirm alcohol consumption and classify each presentation into either an ‘acute’ or ‘chronic’ harm. Positive predictive value (PPV) for acute harm was calculated, and predictors of acute harm presentations were estimated using logistic regression.ResultsThe PPV of the alcohol syndrome for acute alcohol harm was 53.5%. Independent predictors of acute harm were ambulance arrival [adjusted odds ratio (aOR) = 3.4, 95% confidence interval (CI) 2.4–4.7], younger age (12–24 vs. 25–39 years: aOR = 3.4, 95% CI 2.2–5.3), not being admitted (aOR 2.2, 95% CI 1.5–3.2) and arriving between 10 pm and 5.59 am (aOR 2.1, 95% CI 1.5–2.8). PPV among 12 to 24‐year‐olds was 82%.Discussion and Conclusions The alcohol syndrome provides moderate precision as an indicator of acute alcohol harms presenting to the ED. Precision for monitoring acute harm in the population is improved by filtering the syndrome by the strongest independent predictors of acute alcohol harm presentations. [Whitlam G, Dinh M, Rodgers C, Muscatello DJ, McGuire R, Ryan T, Thackway S. Diagnosis‐based emergency department alcohol harm surveillance: What can it tell us about acute alcohol harms at the population level? Drug Alcohol Rev 2016;35:693–701]

Dynamic contrast-enhanced MRI measurement of renal function in healthy participants.

Background High repeatability, accuracy, and precision for renal function measurements need to be achieved to establish renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a clinically useful diagnostic tool. Purpose To investigate the repeatability, accuracy, and precision of DCE-MRI measured renal perfusion and glomerular filtration rate (GFR) using iohexol-GFR as the reference method. Material and Methods Twenty healthy non-smoking volunteers underwent repeated DCE-MRI and an iohexol-GFR within a period of 10 days. Single-kidney (SK) MRI measurements of perfusion (blood flow, Fb) and filtration (GFR) were derived from parenchymal intensity time curves fitted to a two-compartment filtration model. The repeatability of the SK-MRI measurements was assessed using coefficient of variation (CV). Using iohexol-GFR as reference method, the accuracy of total MR-GFR was determined by mean difference (MD) and precision by limits of agreement (LoA). Results SK-Fb (MR1, 345 ± 84; MR2, 371 ± 103 mL/100 mL/min) and SK-GFR (MR1, 52 ± 14; MR2, 54 ± 10 mL/min/1.73 m2) measurements achieved a repeatability (CV) in the range of 15-22%. With reference to iohexol-GFR, MR-GFR was determined with a low mean difference but high LoA (MR1, MD 1.5 mL/min/1.73 m2, LoA [-42, 45]; MR2, MD 6.1 mL/min/1.73 m2, LoA [-26, 38]). Eighty percent and 90% of MR-GFR measurements were determined within ± 30% of the iohexol-GFR for MR1 and MR2, respectively. Conclusion Good repeatability of SK-MRI measurements and good agreement between MR-GFR and iohexol-GFR provide a high clinical potential of DCE-MRI for renal function assessment. A moderate precision in MR-derived estimates indicates that the method cannot yet be used in clinical routine.

Effective Customer Churn Prediction on Large Scale Data using Metaheuristic Approach

Objectives: Customer retention is one of the major requirements of any organization to gain competitive advantage. Accurately predicting the customer’s status can help organizations reduce and prevent churns. Methods/Analysis: This paper presents an analysis of churn data and issues related to churn data in terms of data size, attribute density, data sparsity and abstraction contained in the data. It discusses the advantages of utilizing metaheuristic techniques for churn prediction and in specific analyses ACO for churn prediction and performs a comparison with other metaheuristic algorithms and emphasizes the importance of using ACO. Findings: Experiments were conducted by implementing ACO and applying it on Orange Dataset. It was observed from the ROC curve that the points plotted falls to the top left of the graph, hence indicating good efficiency and a fluctuation from low to moderate false positive rates were observed. It could be observed from the PR curve that the ACO algorithm exhibits high recall rates and moderate precision rates. ROC and the PR plots indicate that there is still scope for enhancement in terms of reduction in false positive rates and increase in precision levels. It was identified that though ACO exhibits effective performance, the size of the dataset acted as a huge downside increasing the time taken. Due to the huge size of the data, memory requirements are very high, but due to the skewed nature of the data most of them contain null values. Applications/Improvement: Findings exhibited scope for improvement, hence research directions namely data structure identification to reduce memory requirements, graph based churn prediction and fuzziness incorporation in the prediction process were proposed.

Technical note: Assessment of the oxygen pulse and heart rate method using respiration chambers and comparative slaughter for measuring heat production of cattle.

The objective of this study was to assess the oxygen pulse and heart rate (O2P-HR) technique using the respiration chamber (RC) and comparative slaughter (CS) methods for measuring the heat production (HP) of crossbred (Holstein × Gyr) yearling bulls. Twenty-four bulls were used. Six bulls were slaughtered at the beginning of the experiment as a reference group to estimate the initial empty body weight (BW) and energy content of the remaining animals. The remaining bulls were assigned to a completely randomized design with 3 levels of dry matter intake, with 6 replicates. The levels of dry matter intake were 1.2% of BW, 1.8% of BW and ad libitum, with target orts of 5%. The bulls were fed a diet consisting of 59.6% corn silage and 40.4% concentrate on a dry matter basis. The HP (kcal/BW0.75) was measured using 3 techniques, first using O2P-HR, followed by the RC and CS methods. The HP did not differ among assessed techniques, averaging 162.7kcal/BW0.75. The intercepts of the linear regressions (mean ± SE) were 64.82±25.515 (H0: intercept=0; P=0.024), 33.77±13.418 (H0: intercept=0), and 50.02±27.495 (H0: intercept=0) for O2P-HR versus RC, CS versus RC, and O2P-HR versus CS, respectively. The slopes of the linear regressions were 0.59±0.153 (H0: slope=1), 0.88±0.081 (H0: slope=1), and 0.62±0.155 (H0: slope=1) for O2P-HR versus RC, CS versus RC, and O2P-HR versus CS, respectively. The coefficients of determination were 0.52, 0.90, and 0.52 for O2P-HR versus RC, CS versus RC, and O2P-HR versus CS, respectively. The concordance correlation coefficients, 0.70 and 0.68, were moderate for O2P-HR versus RC and O2P-HR versus CS, respectively, but high, 0.90, for CS versus RC. The between-animal coefficient of variation was greater for the O2P-HR method (16.6%) compared with RC (7.7%) or CS (6.7%). We conclude that there was an agreement among the HP measurements detected using the assessed methods and that O2P-HR is able to predict HP in cattle with great accuracy but only moderate precision. Therefore, the O2P-HR method may have limitations in terms of assessing HP in low numbers of replications due to greater between-animal coefficient of variation than either the RC or CS methods.

Study regarding the monthly evolution of beef meat consumption by gender and age group of the respondents

Data from 180 bulls from Charolais, Limousine and Retinta breeds were used to evaluate image analysis of cross-sections as method of predicting the physical composition of the 10th–11th–12th rib-cut. The site along the longissimus thoracis muscle (between either the 9th and10th ribs or the 12th and 13th ribs), and the breed had significant influence on most of the variables analyzed. The correlation coefficients between the rib composition obtained by image analysis and by dissection were low to moderate (r = 0.18–0.59, P < 0.01–P < 0.001). The R2 values of the composition components of the 12th–13th rib cross-section to 10th–11th–12th rib-cut composition were higher than those recorded from the 9th–10th rib cross-section (R2 = 0.535 to 0.759 vs. 0.148 to 0.502). The accuracy of the predictions of lean, longissimus thoracis m. and bone percentages improved significantly with the addition of carcass weight. Results indicate that image analysis can predict rib composition in lean cattle with moderate accuracy and precision.

Evaluation of physiological strain in hot work areas using thermal imagery

BackgroundMonitoring core body temperature to identify heat strain in workers engaged in hot work in heat stress environments is intrusive and expensive. Nonintrusive, inexpensive methods are needed to calculate individual Physiological Strain Index (PSI). ObjectiveThermal imaging and heart rate monitoring were used in this study to calculate Physiological Strain Index (PSI) from thermal imaging temperatures of human subjects wearing thermal protective garments during recovery from hot work. MethodsTen male subjects were evaluated for physiological strain while participating in hot work. Thermal images of the head and neck were captured with a high-resolution thermal imaging camera concomitant with measures of gastrointestinal and skin temperature. Lin's concordance correlation coefficient (rho_c), Pearson's coefficient (r) and bias correction factor (C-b) were calculated to compare thermal imaging based temperatures to gastrointestinal temperatures. Calculations of PSI based thermal imaging recorded temperatures were compared to gastrointestinal based PSI. ResultsParticipants reached a peak PSI of 5.2, indicating moderate heat strain. Sagittal measurements showed low correlation (rho_c=0.133), moderate precision (r=0.496) and low accuracy (C_b=0.269) with gastrointestinal temperature. Bland-Altman plots of imaging measurements showed increasing agreement as gastrointestinal temperature rose; however, the Limits of Agreement (LoA) fell outside the ±0.25C range of clinical significance. Bland-Altman plots of PSI calculated from imaging measurements showed increasing agreement as gastrointestinal temperature rose; however, the LoA fell outside the ±0.5 range of clinical significance. ConclusionResults of this study confirmed previous research showing thermal imagery is not highly correlated to body core temperature during recovery from moderate heat strain in mild ambient conditions. Measurements display a trend toward increasing correlation at higher body core temperatures. Accuracy was not sufficient at mild to moderate heat strain to allow calculation of individual physiological stress.

Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research

The Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings.

Integration of SCOR-Modeling and Logistical Concept of Management in the System of Internal Transportation of Milk Cooperative

Features of combined application of concepts/technologies of SCOR-modeling with logistical concept/technology of management of a separate link in a chain of supply in the milk business have been studied in the article. The term has been introduced and the essence has been provided of the private-cooperative, cooperative and mixed supply chain. The link of the cooperative supply chain “milk producer – serving cooperative” in the system of SCOR-model. Attention has been focused on rationalistic type of logistics as a base of logistical concept/technology for optimization of business processes in the system of SCМ. Mechanism and methodology of logistical optimization of internal transportation in the milk serving cooperative has been added focused on metrics of SCOR-model. Integrated application of SCOR-modeling has been suggested along with the method of linear programming on stages of creation (projecting) and functioning of the milk cooperative. Methods of formation of restrictions of mathematical model concerning possible negative effect from using additional points of milk gathering by cooperative through the mechanism of planning progressiveness (digressiveness) of additional transportation costs have been studied. Expediency of using in limited models both individualized or exact and averaged and wide-band parameters corresponding to factors of significant and insignificant influence on effectiveness of cooperative activity has been defined. Conditions of application of the developed methods and models for the level of planning with horizon moderate precision have been grounded. DOI: 10.5901/mjss.2015.v6n1s2p14

A New Six-Parameter Model Based on Chebyshev Polynomials for Solar Cells

This paper presents a new current-voltage (I-V) model for solar cells. It has been proved that series resistance of a solar cell is related to temperature. However, the existing five-parameter model ignores the temperature dependence of series resistance and then only accurately predicts the performance of monocrystalline silicon solar cells. Therefore, this paper uses Chebyshev polynomials to describe the relationship between series resistance and temperature. This makes a new parameter called temperature coefficient for series resistance introduced into the single-diode model. Then, a new six-parameter model for solar cells is established in this paper. This new model can improve the accuracy of the traditional single-diode model and reflect the temperature dependence of series resistance. To validate the accuracy of the six-parameter model in this paper, five kinds of silicon solar cells with different technology types, that is, monocrystalline silicon, polycrystalline silicon, thin film silicon, and tripe-junction amorphous silicon, are tested at different irradiance and temperature conditions. Experiment results show that the six-parameter model proposed in this paper is anI-Vmodel with moderate computational complexity and high precision.

Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

Abstract. In order to exploit the full-earth viewing potential of satellite instruments to globally characterise aerosols, new algorithms are required to deduce key microphysical parameters like the particle size distribution and optical parameters associated with scattering and absorption from space remote sensing data. Here, a methodology based on neural networks is developed to retrieve such parameters from satellite inputs and to validate them with ground-based remote sensing data. For key combinations of input variables available from the MODerate resolution Imaging Spectro-radiometer (MODIS) and the Ozone Measuring Instrument (OMI) Level 3 data sets, a grid of 100 feed-forward neural network architectures is produced, each having a different number of neurons and training proportion. The networks are trained with principal components accounting for 98% of the variance of the inputs together with principal components formed from 38 AErosol RObotic NETwork (AERONET) Level 2.0 (Version 2) retrieved parameters as outputs. Daily averaged, co-located and synchronous data drawn from a cluster of AERONET sites centred on the peak of dust extinction in Northern Africa is used for network training and validation, and the optimal network architecture for each input parameter combination is identified with reference to the lowest mean squared error. The trained networks are then fed with unseen data at the coastal dust site Dakar to test their simulation performance. A neural network (NN), trained with co-located and synchronous satellite inputs comprising three aerosol optical depth measurements at 470, 550 and 660 nm, plus the columnar water vapour (from MODIS) and the modelled absorption aerosol optical depth at 500 nm (from OMI), was able to simultaneously retrieve the daily averaged size distribution, the coarse mode volume, the imaginary part of the complex refractive index, and the spectral single scattering albedo – with moderate precision: correlation coefficients in the range 0.368 ≤ R ≤ 0.514. The network failed to recover the spectral behaviour of the real part of the complex refractive index. This new methodological approach appears to offer some potential for moderately accurate daily retrieval of the total volume concentration of the coarse mode of aerosol at the Saharan dust peak in the area of Northern Africa.

Evaluation of prediction equations to estimate gross, digestible, and metabolizable energy content of maize dried distillers grains with solubles (DDGS) for swine based on chemical composition

The objective of this study was to cross-validate prediction equations to estimate the concentration of GE, DE, and ME among sources of corn distillers dried grains with solubles (DDGS) with variable chemical composition in growing pigs. Published concentrations (DM basis) of GE, CP, ether extract (EE), NDF, and total dietary fiber (TDF) along with particle size (PS, μm), bulk density (BD, g/cm3) and in vivo determinations of DE and ME from 45 sources of DDGS samples were obtained from 5 published studies. Prediction equations for GE (5 equations), DE (20 equations), and ME (19 equations) from published studies were used to calculate the concentration of GE, DE, and ME among DDGS sources and compare with experimentally determined in vivo values. Each equation was evaluated using the entire data set, and data sets that excluded data from which the equation was developed (cross-validation). Equations were compared for their overall explanation of variance (R2), precision for reduction in prediction error (PE, kcal/kg DM), and accuracy in deviation of the predicted mean from the overall observed mean (bias, kcal/kg DM). Prediction of GE concentrations among DDGS sources was poor (PE<200 and biases>150) despite moderate explanation of overall variance (R2<0.6). Therefore, we tested DE and ME equations that included GE as an input using the actual analyzed GE concentration of samples. Under this condition, the most precise (PE=144) and accurate (bias=19) DE equation was DE=−2,161+(1.39×GE)−(20.7×NDF)−(49.3×EE). The most precise (PE=149) and accurate (bias=−82) ME equation was ME=−261+(1.05×GE)−(7.89×CP)+(2.47×NDF)−(4.99×EE). Predicting GE with equation GE=4,583+(50.6×EE)−(0.1×PS), and using this estimate in the equation of ME=−261+(1.05×GE)−(7.89×CP)+(2.47×NDF)−(4.99×EE), resulted in moderate precision (PE=134) and accuracy (bias=48). Cross-validation of equations that require PS, BD, or TDF as inputs was not possible because these inputs were only measured in 1 of the 5 published studies used in this evaluation.

Predictive values and other quality criteria of the German version of the Nurse-Work Instability Scale (Nurse-WIS) - follow-up survey findings of a prospective study of a cohort of geriatric care workers.

BackgroundUntil now there has been a lack of effective screening instruments for health care workers at risk. To counteract the forecast shortage for health care workers, the offer of early interventions to maintain their work ability will become a central concern. The Nurse-Work Instability Scale (Nurse-WIS) seems to be suitable as a screening instrument and therefore a prospective study of a cohort of nursing staff from nursing homes was undertaken to validate the Nurse-Work Instability Scale (Nurse-WIS).MethodsThe follow-up data was used to test the sensitivity, specificity and the predictive values of the Nurse-WIS. The participants answered a questionnaire in the baseline investigation (T1) and in a follow-up 12 month after baseline. The hypothesis was that geriatric care workers with an increased risk according to the Nurse-WIS in T1 would be more likely to have taken long-term sick leave or drawn a pension for reduced work capacity in T2.Results396 persons took part in T1 (21.3% response), 225 in T2 (42.3% loss-to-follow-up). In T1, 28.4% indicated an increased risk according to the Nurse-WIS. In T2, 10.2% had taken long-term sick leave or had drawn a pension for reduced work capacity. The sensitivity is 73.9% (95%-CI 55.7%–92.3%), the specificity is 76.7% (95%-CI 71.2%–82.8%). The ROC AUC indicated a moderate precision for the scale, at 0.74 (95%-CI 0.64–0.84).The PPV of the Nurse-WIS is 26.6%, and the NPV is 96.3%. For those with an increased risk according to the Nurse-WIS, the probability in T2 of long-term sick leave or a pension for reduced work capacity is around eight times higher (OR 8.3, 95%-CI 2.90–23.07). Persons who had indicated a long-term sick leave or made an application for a pension for reduced work capacity in T1 had a 17 times higher risk (OR 17.4, 95%-CI 3.34–90.55).ConclusionThe German version of the Nurse-WIS appears to be a valid instrument with satisfactory predictive capabilities for recording an impending long-term sick leave. Whether the Nurse-WIS can be used as a screening tool which helps to design risk adjusted prevention programs for the afflicted nurse should be studied.

Agreement between different sleep states and behaviour indicators in dairy cows

Conclusive data regarding behavioural indicators of different sleep states in adult dairy cows are lacking, i.e. agreement between behavioural indicators of sleep and corresponding electrophysiological measures. Behavioural estimates for quantifying total sleep time in calves have been developed, so this study examined whether these behavioural estimates also apply for adult cows.Behaviour observations and electrophysiological readings were recorded for a total of 13 cows during one recording session per cow lasting on average 4h 22min. Recording started when the cow was fully awake and finished when at least one probable sleep bout had been recorded. The behavioural estimates used in the study were: ‘lying with head lifted and still’ for non-rapid eye movement (NREM) sleep, ‘lying with head resting’ for rapid eye movement (REM) sleep and ‘lying with head lifted and moving’ for awakeness. As statistical measures of agreement between behavioural estimates and electrophysiological status (both recorded at 30s intervals), Cohen's kappa as well as sensitivity and specificity measures were calculated. Additionally, misclassifications were evaluated to better understand agreement between the behaviour and electrophysiological sleep classification. Since interval length might have affected the agreement, the output data were also aggregated into 60, 90 and 120s intervals and analysed using Wilcoxon sign-rank test to determine the most appropriate interval length.It was found that the behavioural estimates for assessing total sleep time in calves could not be applied to adult cows as they markedly overestimated NREM and REM sleep time. Behavioural estimates for NREM and REM sleep time were on average 124±17 and 14±4min per cow, respectively, while the electrophysiological estimate for NREM and REM was on average 20±5 and 10±3min per cow, respectively. Using the behavioural estimate ‘lying with head resting’, REM sleep could be identified with moderate precision, but this indicator alone likely underestimates total duration of REM sleep. Behavioural estimates for NREM sleep showed high sensitivity (81%) but low specificity (6%) while the behavioural estimates for REM sleep showed high sensitivity (70%) and moderate specificity (41%). For both categories, both sensitivity and specificity increased with increasing measurement interval length. Drowsing as identified from electrophysiological data was present mainly when the cows were lying with head lifted and still, whereas awakeness was present when the cows were lying with head lifted and moving.

Object attention in moderate precision tasks: Mechanisms of the elaborated template model.

Object attention in moderate precision tasks: Mechanisms of the elaborated template model.

Moderate precision of prognostic scoring systems in a consecutive, prospective cohort of 544 patients with metastatic spinal cord compression.

Improved survival among cancer patients and diverse conclusions from recent studies make it relevant to reassess the performance of the Tokuhashi Revised score and the Tomita score. The aim of this study was to validate and compare these two scoring systems in a recent and unselected cohort of patients with metastatic spinal cord compression (MSCC). In 2011, we conducted a prospective cohort study of 544 patients who were consecutively admitted with MSCC to one treatment facility. Patients estimated survival were assessed with the Tokuhashi Revised score and the Tomita score and compared to the observed survival. We assessed how precise the scoring systems predicted survival with McNemar's test. The prognostic value was illustrated with Kaplan-Meier curves, and the individual prognostic components were analyzed with Cox regression analysis. The mean age was 65years (range 20-95), and 57% of the patients were men. The majority of tumors were lung (23%), prostate (21%), and breast tumors (18%). The overall precision of predicted survival was 58.7% for the Tokuhashi Revised score and 52.9% for the Tomita score. The observed survival in each of the scoring groups categorized by the scoring systems was statistically significantly different (p<0.0001). The Tokuhashi Revised score and the Tomita score are useful in categorizing patients into prognostic groups, and the individual components have important prognostic values. The Tokuhashi Revised score was most precise in predicting survival. However, due to the relatively low precision, we suggest that a modification of both scoring systems is necessary.

An IO-efficient parallel implementation of an R2 viewshed algorithm for large terrain maps on a CUDA GPU

A rapid and flexible parallel approach for viewshed computation on large digital elevation models is presented. Our work is focused on the implementation of a derivate of the R2 viewshed algorithm. Emphasis has been placed on input/output (IO) efficiency that can be achieved by memory segmentation and coalesced memory access. An implementation of the parallel viewshed algorithm on the Compute Unified Device Architecture (CUDA), which exploits the high parallelism of the graphics processing unit, is presented. This version is referred to as r.cuda.visibility. The accuracy of our algorithm is compared to the r.los R3 algorithm (integrated into the open-source Geographic Resources Analysis Support System geographic information system environment) and other IO-efficient algorithms. Our results demonstrate that the proposed implementation of the R2 algorithm is faster and more IO efficient than previously presented IO-efficient algorithms, and that it achieves moderate calculation precision compared to the R3 algorithm. Thus, to the best of our knowledge, the algorithm presented here is the most efficient viewshed approach, in terms of computational speed, for large data sets.

Accuracy and precision of echocardiography versus right heart catheterization for the assessment of pulmonary hypertension

Echocardiographic studies have contributed to progress in the understanding of the pathophysiology of the pulmonary circulation and have been shown to be useful for screening for and prognostication of pulmonary hypertension, but are considered unreliable for the diagnosis of pulmonary hypertension. We explored this apparent paradox with rigorous Bland and Altman analysis of the accuracy and the precision of measurements collected in a large patient population. A total of 161 patients referred for a suspicion of pulmonary hypertension were prospectively evaluated by a Doppler echocardiography performed by dedicated cardiologists within 1 h of an indicated right heart catheterization. Nine of the patients (6%) were excluded due to an insufficient signal quality. Of the remaining 152 patients, 10 (7%) had no pulmonary hypertension and most others had either pulmonary arterial hypertension (36%) or pulmonary venous hypertension (40%) of variable severities. Mean pulmonary artery pressure, left atrial pressure and cardiac output were nearly identical at echocardiography and catheterization, with no bias and tight confidence intervals, respectively ± 3 mm Hg, ± 5 mm Hg and ± 0.3 L/min. However, the ± 2SD limits of agreement were respectively of + 19 and - 18 mm Hg for mean pulmonary artery pressure, + 8 and - 12 mm Hg for left atrial pressure and + 1.8 and - 1.7 L/min for cardiac output. Doppler echocardiography allows for accurate measurements of the pulmonary circulation, but with moderate precision, which explains why the procedure is valid for population studies but cannot be used for the individual diagnosis of pulmonary hypertension.

Towards constraints on fossil fuel emissions from total column carbon dioxide

Abstract. We assess the large-scale, top-down constraints on regional fossil fuel emissions provided by observations of atmospheric total column CO2, XCO2. Using an atmospheric general circulation model (GCM) with underlying fossil emissions, we determine the influence of regional fossil fuel emissions on global XCO2 fields. We quantify the regional contrasts between source and upwind regions and probe the sensitivity of atmospheric XCO2 to changes in fossil fuel emissions. Regional fossil fuel XCO2 contrasts can exceed 0.7 ppm based on 2007 emission estimates, but have large seasonal variations due to biospheric fluxes. Contamination by clouds reduces the discernible fossil signatures. Nevertheless, our simulations show that atmospheric fossil XCO2 can be tied to its source region and that changes in the regional XCO2 contrasts scale linearly with emissions. We test the GCM results against XCO2 data from the GOSAT satellite. Regional XCO2 contrasts in GOSAT data generally scale with the predictions from the GCM, but the comparison is limited by the moderate precision of and relatively few observations from the satellite. We discuss how this approach may be useful as a policy tool to verify national fossil emissions, as it provides an independent, observational constraint.