Maximum Percentile Research Articles

Temporal variation in the effect of heat and the role of the Italian heat prevention plan.

The aim of the article is to evaluate the temporal change in the effect of heat on mortality in Italy in the last 12 years after the introduction of the national heat plan. Time series analysis. Distributed lag non-linear models were used to estimate the association between maximum apparent temperature and mortality in 23 Italian cities included in the national heat plan in four study periods (before the introduction of the heat plan and three periods after the plan was in place between 2005 and 2016). The effect (relative risks) and impact (attributable fraction [AF] and number of heat-related deaths) were estimated for mild summer temperatures (20th and 75th percentile maximum apparent temperature [Tappmax]) and extreme summer temperatures (75th and 99th percentile Tappmax) in each study period. A survey of the heat preventive measures adopted over time in the cities included in the Italian heat plan was carried out to better describe adaptation measures and response. Although heat still has an impact on mortality in Italian cities, a reduction in heat-related mortality is observed progressively over time. In terms of the impact, the heat AF related to extreme temperatures declined from 6.3% in the period 1999-2002 to 4.1% in 2013-2016. Considering the entire temperature range (20th vs 99th percentile), the total number of heat-related deaths spared over the entire study period was 1900. Considering future climate change and the health burden associated to heat waves, it is important to promote adaptation measures by showing the potential effectiveness of heat prevention plans.

Estimating Normal Values of Rare T-Lymphocyte Populations in Peripheral Blood of Healthy Cuban Adults.

INTRODUCTION Flow cytometry allows immunophenotypic characterization of important lymphocyte subpopulations for diagnosis of diseases such as cancer, autoimmune diseases, immunodeficiencies and some infections. Normal values of rare lymphoid cells in blood, quantified by cytometry, vary among different populations; so it is indispensable to obtain normal national values that can be used in clinical practice. OBJECTIVE Characterize distribution of rare T-lymphocyte populations in peripheral blood, specifically double-positive T, natural killer T and activated T lymphocytes, as well as their relationship to sex and age. METHODS A cross-sectional study was carried out in 129 adults (68 women, 61 men) aged >18 years, without chronic diseases or unhealthy habits, who signed informed consent. Peripheral blood was collected for immunophenotyping of lymphocyte subpopulations with monoclonal antibodies specific for CD4+CD8+ double-positive T cells, CD3+CD56+ natural killer T cells, and CD3+CD25+HLA-DR+ activated T cells. An eight-color flow cytometer (Beckman Coulter Gallios) was used. The analytic strategy was modified, associating variables of interest in a single graphic, using conventional monoclonal labeling antibodies. Medians and minimum and maximum percentiles (2.5 and 97.5, respectively) were used as descriptive statistics, stratified by sex, for cell counts and percentages. A linear regression model was applied to assess age effects and a two-tailed Mann-Whitney U test for independent samples was used to assess sex differences. The significance threshold was set as p ≤0.05. RESULTS Median percentages of total lymphocytes: natural killer T cells 6.3% (1.4%-23%) in men and 4.7% (0.8%-11.3%) in women (p = 0.003); activated T cells 1.0% (0.2%-2.2%) in men and 1.2% (0.4%-3.1%) in women, without statistical significance; and double positives 0.8% (0.1%-4.2%) in men and 0.9% (0.3-5.1) in women, also without statistical significance. Median cell counts (cells/mL) were: natural killer T cells, 126 (27-580) in men and 105 (20-279) in women (p = 0.023); activated T cells: 20 (4-46) in men and 25 (7-75) in women, (p = 0.013) and double-positive T cells: 17 (2-85) in men and 21 (7-154) in women, without statistical significance. Sex influenced natural killer T cells, but age did not. CONCLUSIONS Age does not affect counts and percentages of rare T lymphocyte subpopulations in the blood of healthy Cuban adults. Sex differences found for some phenotypes suggest the need for different reference values for women and men.

Sensitivity analysis of gene ranking methods in phenotype prediction

IntroductionIt has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. Materials and methodsThis study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. Results and discussionIn the case of synthetic modeling, we showed that Fisher’s Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM provided the highest predictive accuracies with the smallest number of genes. Biological pathways were found with an expanded list of genes whose discriminatory power has been established via FR. ConclusionsWe have shown that noise in expression data and class assignment partially falsifies the sets of discriminatory probes in phenotype prediction problems. FR and SAM better exploit the principle of parsimony and are able to find subsets with less number of high discriminatory genes. The predictive accuracy and the precision are two different metrics to select the important genes, since in the presence of noise the most predictive genes do not completely coincide with those that are related to the phenotype. Based on the synthetic results, FR and SAM are recommended to unravel the biological pathways that are involved in the disease development.

ATTRIBUTION AND CHARACTERISATION OF SCLEROPHYLL FORESTED LANDSCAPES OVER LARGE AREAS

Abstract. This paper presents a methodology for the attribution and characterisation of Sclerophyll forested landscapes over large areas. First we define a set of woody vegetation data primitives (e.g. canopy cover, leaf area index (LAI), bole density, canopy height), which are then scaled-up using multiple remote sensing data sources to characterise and extract landscape woody vegetation features. The advantage of this approach is that vegetation landscape features can be described from composites of these data primitives. The proposed data primitives act as building blocks for the re-creation of past woody characterisation schemes as well as allowing for re-compilation to support present and future policy and management and decision making needs. Three main research sites were attributed; representative of different sclerophyll woody vegetated systems (Box Iron-bark forest; Mountain Ash forest; Mixed Species foothills forest). High resolution hyperspectral and full waveform LiDAR data was acquired over the three research sites. At the same time, land management agencies (Victorian Department of Environment, Land Water and Planning) and researchers (RMIT, CRC for Spatial Information and CSIRO) conducted fieldwork to collect structural and functional measurements of vegetation, using traditional forest mensuration transects and plots, terrestrial lidar scanning and high temporal resolution in-situ autonomous laser (VegNet) scanners. Results are presented of: 1) inter-comparisons of LAI estimations made using ground based hemispherical photography, LAI 2200 PCA, CI-110 and terrestrial and airborne laser scanners; 2) canopy height and vertical canopy complexity derived from airborne LiDAR validated using ground observations; and, 3) time-series characterisation of land cover features. 1. Accuracy targets for remotely sensed LAI products to match within ground based estimates are ± 0.5 LAI or a 20% maximum (CEOS/GCOS) with new aspirational targets of 5%). In this research we conducted a total of 67 ground-based method-to-method pairwise comparisons across 11 plots in five sites, incorporating the previously mentioned LAI methods. Out of the 67 comparisons, 29 had an RMSE ≥ 0.5 LAIe. This has important implications for the validation of remotely sensed products since ground based techniques themselves exhibit LAI variations greater than internationally recommended guidelines for satellite product accuracies. 2. Two methods of canopy height derivation are proposed and tested over a large area (4 Million Ha). 99th percentile maximum height achieved a RMSE of 6.6%, whilst 95th percentile dominant height a RMSE = 10.3%. Vertical canopy complexity (i.e. the number of forest layers of strata) was calculated as the local maxima of vegetation density within the LiDAR canopy profile and determined using a cubic spline smoothing of Pgap. This was then validated against in-situ and LiDAR observations of canopy strata with an RMSE 0.39 canopy layers. 3. Preliminary results are presented of landcover characterisation using LandTrendr analysis of Landsat LEDAPS data. kNN is then used to link these features to a dense network of 800 field plots sites.

ATTRIBUTION AND CHARACTERISATION OF SCLEROPHYLL FORESTED LANDSCAPES OVER LARGE AREAS

This paper presents a methodology for the attribution and characterisation of Sclerophyll forested landscapes over large areas. First we define a set of woody vegetation data primitives (e.g. canopy cover, leaf area index (LAI), bole density, canopy height), which are then scaled-up using multiple remote sensing data sources to characterise and extract landscape woody vegetation features. The advantage of this approach is that vegetation landscape features can be described from composites of these data primitives. The proposed data primitives act as building blocks for the re-creation of past woody characterisation schemes as well as allowing for re-compilation to support present and future policy and management and decision making needs. <br><br> Three main research sites were attributed; representative of different sclerophyll woody vegetated systems (Box Iron-bark forest; Mountain Ash forest; Mixed Species foothills forest). High resolution hyperspectral and full waveform LiDAR data was acquired over the three research sites. At the same time, land management agencies (Victorian Department of Environment, Land Water and Planning) and researchers (RMIT, CRC for Spatial Information and CSIRO) conducted fieldwork to collect structural and functional measurements of vegetation, using traditional forest mensuration transects and plots, terrestrial lidar scanning and high temporal resolution in-situ autonomous laser (VegNet) scanners. <br><br> Results are presented of: 1) inter-comparisons of LAI estimations made using ground based hemispherical photography, LAI 2200 PCA, CI-110 and terrestrial and airborne laser scanners; 2) canopy height and vertical canopy complexity derived from airborne LiDAR validated using ground observations; and, 3) time-series characterisation of land cover features. <br><br> 1. Accuracy targets for remotely sensed LAI products to match within ground based estimates are ± 0.5 LAI or a 20% maximum (CEOS/GCOS) with new aspirational targets of 5%). In this research we conducted a total of 67 ground-based method-to-method pairwise comparisons across 11 plots in five sites, incorporating the previously mentioned LAI methods. Out of the 67 comparisons, 29 had an RMSE ≥ 0.5 LAIe. This has important implications for the validation of remotely sensed products since ground based techniques themselves exhibit LAI variations greater than internationally recommended guidelines for satellite product accuracies. <br><br> 2. Two methods of canopy height derivation are proposed and tested over a large area (4 Million Ha). 99th percentile maximum height achieved a RMSE of 6.6%, whilst 95th percentile dominant height a RMSE = 10.3%. Vertical canopy complexity (i.e. the number of forest layers of strata) was calculated as the local maxima of vegetation density within the LiDAR canopy profile and determined using a cubic spline smoothing of Pgap. This was then validated against in-situ and LiDAR observations of canopy strata with an RMSE 0.39 canopy layers. <br><br> 3. Preliminary results are presented of landcover characterisation using LandTrendr analysis of Landsat LEDAPS data. kNN is then used to link these features to a dense network of 800 field plots sites.

A Multistage Technique to Minimize Overestimations of Slope Susceptibility at Large Spatial Scales

Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They lead to significant economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability using buffer and threshold techniques to downscale large areas and minimize slope uncertainties at local scales, then in a second stage, logistic regression is used to determine susceptibility at large scales. ASTER GDEM V2 is used for topographical characterization of slope and buffer analysis. Four static parameters (slope angle, soil type, land cover and elevation) for 230 shallow rainfall-induced landslides listed in a comprehensive landslide inventory for the continental United States are examined. A delimiting buffer equivalent to 5, 25 or 50 km is created around each landslide event facilitating the statistical analysis of slope thresholds. Slope angle thresholds at the pixel points 50, 75, 95, 99 and maximum percentiles are compared to one another and tested for best fit in a logistic regression environment. It is determined that values lower than the 75-percentile threshold misrepresents susceptible slope angles by not including slopes higher than 35°. Best range of slope angles and regression fit can be achieved when utilizing the 99 percentile slope angle threshold. The resulting logistic regression model predicts the highest number of cases correctly with 97.2% accuracy. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional landslide probability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy. Keywords: Shallow landslides; Slope instability; Threshold analysis; Logistic regression; Regional analysis; GIS; Remote sensing Introduction Rainfall induced landslides are one of the most frequent natural hazards on slanted terrains. They usually result in great economic losses and fatalities globally. Worldwide at least 32,322 deaths between 2004 and 2010 have been reported [1] and in the United States alone, landslides cause $1-2 billion in damages and more than 25 fatalities in average each year [2]. Understanding, mapping, modeling and preventing the aftermath of these devastating events represents an important scientific and operational endeavor [3]. The term “Landslide” describes the downward and outward movement of slope-forming materials that include rock, earth, and debris or a combination of these [4]. Although landslides are considered to be dependent on the complex interaction of several static and dynamic factors [5-7] slope angle has great influence on the susceptibility of a slope to sliding. Increased slope angle usually correlates to increased likelihood of failure even if the material distribution on the slope is uniform and isotropic [5]. Undeniably, many other parameters are essential to the analysis of landslide risk. For example, changes in land use and land cover such as deforestation, forest logging, road construction, cultivation and fire on steep slopes can have a significant effect on landslide activity [8]. In addition, forest vegetation

On the prediction of Hodgkin lymphoma treatment response.

The cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment. We carried out a retrospective analysis of the response to treatment of a cohort of 263 Caucasians who were diagnosed with Hodgkin lymphoma in Asturias (Spain). For that purpose, we used a list of 35 clinical and biological variables that are currently measured at diagnosis before any treatment begins. To establish the list of most discriminatory prognostic variables for treatment response, we designed a machine learning approach based on two different feature selection methods (Fisher's ratio and maximum percentile distance) and backwards recursive feature elimination using a nearest-neighbor classifier (k-NN). The weights of the k-NN classifier were optimized using different terms of the confusion matrix (true- and false-positive rates) to minimize risk in the decisions. We found that the optimum strategy to predict treatment response in Hodgkin lymphoma consists in solving two different binary classification problems, discriminating first if the patient is in progressive disease; if not, then discerning among complete and partial remission. Serum ferritin turned to be the most discriminatory variable in predicting treatment response, followed by alanine aminotransferase and alkaline phosphatase. The importance of these prognostic variables suggests a close relationship between inflammation, iron overload, liver damage and the extension of the disease.

Maximum forces and joint stability implications during in-line arm pushes

Current ergonomic software packages have no way of determining manual arm strength limits when the force vector passes directly through the elbow and shoulder, such that the moment demands on those joints are very low. The first step in overcoming this dilemma is to understand what the arm force limitations are when there is no moment to balance. Sixteen participants generated isometric in-line arm forces at varying intensities. Surface electromyography was used to monitor individual muscle activity, which was used in combination with moment arm estimates, to make inference about elbow stability. There was a significant difference in the average maximum forces produced by males compared to females, such that the 25th percentile maximum in-line arm push force is suggested to be 636 N for males and 359 N for females. Our force limits can be easily implemented into any ergonomic assessment tool as boundaries for maximum in-line arm forces.

Assigning publications to multiple subject categories for bibliometric analysis

Purpose – This study is concerned with a problem in measuring citation impact with the aid of percentile data, which arises from the assignment of publications (or the journals in which the publications have appeared) by Thomson Reuters for the Web of Science to more than one subject category. If there is more than one subject category for a publication, it is initially unclear which category is to be used to create the reference set for the calculation of the percentile. This paper seeks to address these issues. Design/methodology/approach – In this study the author would like to look at whether the calculation of differences between the citation impact of research institutions is affected by whether the minimum (the maximum percentile), the maximum (the minimum percentile), the mean or the median impact (percentile) for the different subject categories is used. The study is based on a sample of percentile data for three research institutions (n=4,232). Findings – The result of the comparison of citation impact of the three institutions remains very similar for all the calculation methods, but on a different level. Originality/value – It is the first study, which investigates how far it makes a difference in the comparison of the citation impact of three different research institutes whether – with multiple assignments of subject categories to one publication – the minimum, the maximum, the mean or the median inverted percentile is used. An answer to the question is very relevant since different methods are used in practical application. For example, the web-based research evaluation tool InCites uses the minimum percentile.

Ceftazidime dosage regimen in intensive care unit patients: from a population pharmacokinetic approach to clinical practice via Monte Carlo simulations

The large variability in drug pharmacokinetic disposition has already been described in ICU patients leading to important variations in drugs concentrations. The usual recommended dosage of ceftazidime is not adapted for all ICU situations and ceftazidime should be monitored closely. New recommendations have to be given for some specific cases. Our results propose individual therapeutic drug monitoring taking into account: For the patient: the reason of admission in the ICU, the mechanical ventilation status and the creatinine clearance calculated by the modified diet in renal disease (MDRD). • For the antibiotics: the lung distribution, the minimal inhibitory concentration (MIC) of the strain to eradicate and the potential toxicity. AIM To predict the ceftazidime dosage regimen as a function of the glomerular filtration rate expressed by the modification of the diet in renal disease (MDRD), reason for admission and mechanical ventilation in intensive care unit (ICU) patients to treat Pseudomonas aeruginosa pneumonia. A published and qualified population pharmacokinetic model was used to perform Monte Carlo simulations of ceftazidime concentrations. The serum target of 40-100 mg l(-1) was defined based on the minimal inhibitory concentration (MIC), the European break point (EBP), the pulmonary drug diffusion and toxicity. The recommended dosage regimens were based on the maximum percentile of the patients with simulated steady state concentrations reaching the target. Steady-state was reached at 72 h whatever the MDRD. The simulations of serum concentrations generated higher percentiles of the population reaching the target after continuous administration. We recommend a 4 g continuous dose after the usual 2 g loading dose for patients with MDRD from 10 to 30 ml min(-1) , 6 g for MDRD between 40 and 80 ml min(-1) , 8 g for MDRD from 90 to 110 ml min(-1) , 10 g for MDRD from 120 to 190 ml min(-1) and 12 g day(-1) for patients with MDRD higher than 200 ml min(-1) . Our study demonstrated that in ICU patients for a given MDRD, steady-state takes longer to reach in polytrauma patients than in patients with medical or post surgery reasons for admission. Continuous infusion ensures that a higher percentage of patients reaches the target than the same dose given by discontinuous administration and this only depends on MDRD.

SU-DD-A1-04: Dose Reconstruction Using Helical Tomotherapy Detector Data

Purpose: To reconstruct dose delivered to head‐and‐neck patients using detector signals from a helical tomotherapy machine. Methods: Data from five nasopharyngeal cancer patients treated on a Hi‐Art Helical TomoTherapy unit with daily MVCT scans were analyzed. Each patient received a total of 25 treatment fractions. Nine evenly distributed fractions were selected in this analysis. During treatment, X‐rays penetrated the patient and deposited energy on the same CT detectors used for imaging and the signal was back‐projected to entrance fluence by removing attenuation using a dose verification tool provided by TomoTherapy. The fluence was then projected on the patient's CT to calculate delivered radiation dose. The method was first validated on quality assurance (QA) plans delivered on a cylindrical phantom and then applied on patient treatment plans. Results: Point check on the phantom for QA measurement resulted in an average error of 1.36% (range −0.22% to 3.73%). A 2.1% systematic error on patients was observed by comparing the average reconstructed dose and the planned dose. Random errors of PTV mean dose were between −1% and 2.1%. The variation of max doses to optical nerves, chiasm, brain stem, spinal cord, larynx, right eye and lens were within 5% of the planned dose but the reconstructed doses to the left eye and lens was more than 5% higher than the planned dose for 4 out of 5 the patients with a maximum percentile error of 16% and absolute error of 1.5 Gy in the entire treatment. Conclusions: Dose verification using detector data offers comparable accuracy to ion chamber measurement on the phantom plans. The error on patients is larger but still within 5% for most organs. Therefore, dose reconstruction based on MVCT images and detector data can be used to effectively detect any gross deviation from the treatment plan.

Heat stress intensification in the Mediterranean climate change hotspot

We find that elevated greenhouse gas concentrations dramatically increase heat stress risk in the Mediterranean region, with the occurrence of hot extremes increasing by 200 to 500% throughout the region. This heat stress intensification is due to preferential warming of the hot tail of the daily temperature distribution, with 95th percentile maximum and minimum temperature magnitude increasing more than 75th percentile magnitude. This preferential warming of the hot tail is dictated in large part by a surface moisture feedback, with areas of greatest warm‐season drying showing the greatest increases in hot temperature extremes. Fine‐scale topographic and humidity effects help to further dictate the spatial variability of the heat stress response, with increases in dangerous Heat Index magnified in coastal areas. Further, emissions deceleration substantially mitigates heat stress intensification throughout the Mediterranean region, implying that emissions reductions could reduce the risk of increased heat stress in the coming decades.

Non-methane hydrocarbon emissions from vehicle fuel caps

Abstract Vehicles emit non-methane hydrocarbons (NMHCs) from a number of sources, including missing, worn or improperly tightened fuel caps. Inspection and maintenance programs and the On-Board Diagnostic (OBD) system will detect some of these deficiencies, however, even properly tightened caps will emit NMHCs due to permeation, diffusion, cracks and gaps in seals, and failures of pressure-relief mechanisms. These emissions have not been previously quantified. In this study, in-use emissions from fuel caps were measured in 213 tests on vehicles of varying age and condition over several seasons, including cold and warm temperatures. Diffusion/permeation models are presented to complement the experimental work. NMHC emissions from fuel caps were detected from all vehicles, of which benzene constituted 2.5%. Emissions averaged 2.0 mg h −1 (median=0.5 mg h −1 ), and the distribution of emission rates was highly skewed by a small number of vehicles with much higher emissions, e.g., the 90th, 95th and maximum percentile values were 2.7, 5.0, and 62.7 mg h −1 , respectively. Emission rates increased substantially if the fuel cap was loose, in hot weather, and with vehicle age and mileage. Overall, emissions from properly functioning caps are small relative to running and refueling losses, though they may be significant if the gas cap is defective or loose. Further reductions in emissions may be achieved by using new low-torque cap designs, improved elastomers, properly tightening fuel caps, and replacing old caps.

Fish residue valorization: aplication in the quail diet and study of effects in the eggs

The use of fish industrialization residues is small in thesouthern part of Rio Grande do Sul State, Brazil. These residuesare generally: accumulated in tanks without receiving any typeof treatment, discarded in the vicinity of the industries or takenfor low quality fish flour preparation. A simple and cheapalternative for its use is the production of chemical silage. Whenit is produced in good conditions it can be added in the rationof birds, in a percentile maximum of 10%. In this present worktook place a study for the use of the residues under this form,using its solid fraction in the quails ration and evaluating itseffects on the physical-chemical characteristics,microbiological, and the sensorial profile of the fatty acids ofthe eggs. The accomplished analyses were: Haugh Unit; yolkindex; relationship albumen/egg, yolk/egg and peal/egg,according to Souza and Souza (1995) and volume of the yolkand albumen; proximal composition, according to A.O.A.C.(1995), and for proteins Micro Kjeldahl’s method was used, forlipids the acid hidrolises and for ashes and humidity was usedgravimetric method; determination of fatty acid throughgaseous chromatography; sensorial analysis of scent andflavor, through structured scales and evaluation of mesofilosmicrorganism and psicrofilos, according to ABNT (1995), normMB-3462 Novembro/91, using the plaquement method in depthand in surface, respectively; these analyses were accomplishedwith samples stored in different times from 0 up to 30 days.The found results demonstrate that was possible the use ofresidues under the form of chemical silage, that proved to be astable product; in the quail eggs the physical-chemicalcharacteristics are modified along the time, mainly due to thehumidity changes; the sensorial terminology of the scent andflavor of the eggs was gathered in four groups: sweetened,penetrating, oleaginous and farinaceous; the diet hasinfluence in the sensorial characteristics, when the silage didn’tcontain antioxidant; the storage temperature has greatinfluence in the physical properties of the eggs; at the end ofthe 30 days of storage the eggs didn’t show microbiologicalcontamination; there was a change in the profile of the fattyacids of the quails eggs, in function of the modified diets.

Identifying and assessing uncertainty in hydrological pathways: a novel approach to end member mixing in a Scottish agricultural catchment

A hydrograph separation based upon end member mixing was carried out to assess the relative importance of the hydrological pathways providing the main sources of runoff during five storm events in a 14.5 km 2 agricultural catchment in north east Scotland. The method utilised event specific end member chemistries to differentiate three catchment-scale hydrological pathways on the basis of observed Si and NO 3–N concentrations in sampled source waters. These were overland flow (OF) (low Si and intermediate NO 3–N); subsurface storm flow (high Si and high NO 3–N) and groundwater flow (high Si and intermediate NO 3–N). The hydrograph separation explicitly accounted for uncertainty in the spatial and temporal variation in end member chemistry using Bayesian statistical methods which assumed that each end member arose from a bivariate normal distribution whose mean vectors and co-variance matrices could be estimated. Markov Chain–Monte Carlo methods were used to model the average and 95 percentile maximum and minimum contributions that each end member made to stream water samples during storm events. Although there is large uncertainty over the contributions of each end member to specific events, the analysis produced hydrograph separations that were broadly believable on the basis of hydrometric observations in the catchment. Moreover, by using event specific end member compositions, the method appeared sensitive to the unique combination of event characteristics, antecedent conditions and seasonality in terms of producing feasible separations for very different events. It is concluded that OF generally dominates the storm peak and provides the main flow path by which P is transferred to stream channels during storm events, whilst subsurface storm flows usually dominate the storm hydrograph volumetrically and route NO 3-rich soil water into streams. Consequently, nutrient enrichment in such streams is largely mediated by event-based hydrological flow paths, a finding that will need to be considered in any future mitigation strategies.

Letter on the integral frequency‐domain/time‐domain relationship of MTIE (τ, β)

AbstractThe range spanned by a clock time error is a random quantity that can be statistically characterised. The paper considers the case of clock affected by white phase noise and the range probability laws are inferred. From the probability distribution, the percentile Maximum Time Interval Error, MTIE(τ, β), can be estimated and the relationship between frequency domain and time domain are discussed.

The Relative Sizes of Percentiles

In anthropometric design, one usually tries to accommodate the widest range of sizes from small to large. The accelerating ratio of physical size to percentile as one approaches the upper extreme end of the Gaussian distribution, with its generally concomitant cost and/or spatial impacts on the physical system, can often limit the maximum percentile or minimum percentile which can be realistically accommodated. However, what is not generally realized is the relative sizes of the physical dimensions involved because so much work is done with percentiles which are dimensionless quantities. This paper presents several figures which provide some idea of the relative physical sizes of percentiles. The rather small coefficient of variance for anthropometric measurements means that the difference between say the 50th and the 95th percentiles while being a difference of “45 percentile points” is only a matter of a few percent in physical size. Such considerations can help in providing a better understanding of the tradeoff in percentiles and percent and perhaps adds some physical interpretations of percentiles.

Human Factors for Portable Products

The parameter of weight was investigated to determine the correct poundage of a portable product for the 5-95 percentile male and female consumer. The concept of portability appears to be culturally inspired such that men and women do not differ in the amount of weight which they feel is portable. Men, however, tend to use approximately 30% of their maximum isometric arm strength when carrying what they feel to be a portable object, while women utilize slightly over 40% of their maximum isometric arm strength to carry the same weight. For the subject group as a whole the correct maximum portable weight ranges from 4.5 kg. at the fifth percentile to 10.2 kg. at the ninety-fifth percentile. The fiftieth percentile maximum portable weight was found to be 7.3 kg. In addition wrist rotation strengths in the vertical upright position used for one-handed carry were recorded for the 5-95 percentile male and female to aid in the determination of correct handle placement and case balance.