Statistical Prediction of Sea Ice Concentration over Arctic

NASA Astrophysics Data System (ADS)

Kim, Jongho; Jeong, Jee-Hoon; Kim, Baek-Min

2017-04-01

In this study, a statistical method that predict sea ice concentration (SIC) over the Arctic is developed. We first calculate the Season-reliant Empirical Orthogonal Functions (S-EOFs) of monthly Arctic SIC from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, which contain the seasonal cycles (12 months long) of dominant SIC anomaly patterns. Then, the current SIC state index is determined by projecting observed SIC anomalies for latest 12 months to the S-EOFs. Assuming the current SIC anomalies follow the spatio-temporal evolution in the S-EOFs, we project the future (upto 12 months) SIC anomalies by multiplying the SI and the corresponding S-EOF and then taking summation. The predictive skill is assessed by hindcast experiments initialized at all the months for 1980-2010. When comparing predictive skill of SIC predicted by statistical model and NCEP CFS v2, the statistical model shows a higher skill in predicting sea ice concentration and extent.

Predictive Success Factors in Selective Upper Airway Stimulation.

PubMed

Heiser, Clemens; Hofauer, Benedikt

2017-01-01

Obstructive sleep apnea is one of the most common diseases in Western industrialized countries. A variety of conservative and surgical treatment options are available for its treatment. In recent years, selective upper airway stimulation (sUAS) has been shown to be effective and safe. Different biomarkers have been investigated as predictive clinical success factors in a number of clinical trials. Age does not matter in sUAS, as compared to its predictive role in other therapies. Weight seems to play a limited role, depending on drug-induced sleep endoscopy to rule out a complete concentric collapse with an increased body mass index. For surgical success and the related postoperative tongue motions, a nerve integrity monitoring methodology has been developed for predicting correct cuff placement. Postoperative sonography remains a promising method for the future assessment of predictive markers in sUAS. © 2017 S. Karger AG, Basel.

Factors Affecting Tocopherol Concentrations in Soybean Seeds.

PubMed

Carrera, Constanza S; Seguin, Philippe

2016-12-21

Soybean seeds contain several health-beneficial compounds, including tocopherols, which are used by the nutraceutical and functional food industries. Soybean tocopherol concentrations are, however, highly variable. Large differences observed in tocopherol concentrations among soybean genotypes together with the relatively simple biosynthetic pathway involving few genes support the feasibility of selecting for high-tocopherol soybean. Tocopherol concentrations are also highly influenced by environmental factors and field management. Temperature during seed filling and soil moisture appear to be the main factors affecting tocopherol concentrations; other factors such as soil fertility and solar radiation also affect concentrations and composition. Field management decisions including seeding date, row spacing, irrigation, and fertilization also affect tocopherols. Knowledge of factors affecting soybean tocopherols is essential to develop management strategies that will lead to the production of seeds with consistent target concentrations that will meet the needs of the nutraceutical and functional food industries.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

PubMed

Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

2013-04-01

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Predictability Analysis of PM10 Concentrations in Budapest

NASA Astrophysics Data System (ADS)

Ferenczi, Zita

2013-04-01

Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

A simplified building airflow model for agent concentration prediction.

PubMed

Jacques, David R; Smith, David A

2010-11-01

A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

Factors Affecting Radon Concentration in Houses

NASA Astrophysics Data System (ADS)

Al-Sharif, Abdel-Latif; Abdelrahman, Y. S.

2001-03-01

The dangers to the human health upon exposure to radon and its daughter products is the main motivation behind the vast number of studies performed to find the concentration of radon in our living environment, including our houses. The presence of radon and its daughter products in houses are due to various sources including building materials and the soil under the houses. Many factors affect radon concentration in our houses, the elevation above ground level,ventilation, building materials and room usage being among these factors. In our paper, we discuss the effect of elevation above ground level, room usage and ventilation on the Radon concentration in houses. The faculty residences of the Mu'tah University (Jordan) were chosen in our study. Our results showed that the concentration of radon decreases with elevation. Ventilation rate was also found to affect radon concentration, where low concentrations observed for areas with good ventilation.

Jump neural network for real-time prediction of glucose concentration.

PubMed

Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

2015-01-01

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

INTERSPECIES CORRELATION ESTIMATES PREDICT PROTECTIVE ENVIRONMENTAL CONCENTRATIONS

EPA Science Inventory

Environmental risk assessments often use multiple single species toxicity test results and species sensitivity distributions (SSDs) to derive a predicted no-effect concentration in the environment, typically the 5th percentile of the SSD, termed the HC5. The shape and location of...

Meteorological influence on predicting surface SO2 concentration from satellite remote sensing in Shanghai, China.

PubMed

Xue, Dan; Yin, Jingyuan

2014-05-01

In this study, we explored the potential applications of the Ozone Monitoring Instrument (OMI) satellite sensor in air pollution research. The OMI planetary boundary layer sulfur dioxide (SO2_PBL) column density and daily average surface SO2 concentration of Shanghai from 2004 to 2012 were analyzed. After several consecutive years of increase, the surface SO2 concentration finally declined in 2007. It was higher in winter than in other seasons. The coefficient between daily average surface SO2 concentration and SO2_PBL was only 0.316. But SO2_PBL was found to be a highly significant predictor of the surface SO2 concentration using the simple regression model. Five meteorological factors were considered in this study, among them, temperature, dew point, relative humidity, and wind speed were negatively correlated with surface SO2 concentration, while pressure was positively correlated. Furthermore, it was found that dew point was a more effective predictor than temperature. When these meteorological factors were used in multiple regression, the determination coefficient reached 0.379. The relationship of the surface SO2 concentration and meteorological factors was seasonally dependent. In summer and autumn, the regression model performed better than in spring and winter. The surface SO2 concentration predicting method proposed in this study can be easily adapted for other regions, especially most useful for those having no operational air pollution forecasting services or having sparse ground monitoring networks.

Arsenic concentrations, related environmental factors, and the predicted probability of elevated arsenic in groundwater in Pennsylvania

USGS Publications Warehouse

Gross, Eliza L.; Low, Dennis J.

2013-01-01

Logistic regression models were created to predict and map the probability of elevated arsenic concentrations in groundwater statewide in Pennsylvania and in three intrastate regions to further improve predictions for those three regions (glacial aquifer system, Gettysburg Basin, Newark Basin). Although the Pennsylvania and regional predictive models retained some different variables, they have common characteristics that can be grouped by (1) geologic and soils variables describing arsenic sources and mobilizers, (2) geochemical variables describing the geochemical environment of the groundwater, and (3) locally specific variables that are unique to each of the three regions studied and not applicable to statewide analysis. Maps of Pennsylvania and the three intrastate regions were produced that illustrate that areas most at risk are those with geology and soils capable of functioning as an arsenic source or mobilizer and geochemical groundwater conditions able to facilitate redox reactions. The models have limitations because they may not characterize areas that have localized controls on arsenic mobility. The probability maps associated with this report are intended for regional-scale use and may not be accurate for use at the field scale or when considering individual wells.

Factors affecting Escherichia coli concentrations at Lake Erie public bathing beaches

USGS Publications Warehouse

Francy, Donna S.; Darner, Robert A.

1998-01-01

The environmental and water-quality factors that affect concentrations of Escherichia coli (E. coli) in water and sediment were investigated at three public bathing beachesEdgewater Park, Villa Angela, and Sims Parkin the Cleveland, Ohio metropolitan area. This study was done to aid in the determination of safe recreational use and to help water- resource managers assess more quickly and accurately the degradation of recreational water quality. Water and lake-bottom sediments were collected and ancillary environmental data were compiled for 41 days from May through September 1997. Water samples were analyzed for E. coli concentrations, suspended sediment concentrations, and turbidity. Lake- bottom sediment samples from the beach area were analyzed for E. coli concentrations and percent dry weight. Concentrations of E. coli were higher and more variable at Sims Park than at Villa Angela or Edgewater Park; concentrations were lowest at Edgewater Park. Time-series plots showed that short-term storage (less than one week) of E. coli in lake-bottom sediments may have occurred, although no evidence for long-term storage was found during the sampling period. E. coli concentrations in water were found to increase with increasing wave height, but the resuspension of E. coli from lake-bottom sediments by wave action could not be adequately assessed; higherwave heights were often associated with the discharge of sewage containing E. coli during or after a rainfall and wastewater-treatment plant overflow. Multiple linear regression (MLR) was used to develop models to predict recreational water quality at the in water. The related variables included turbidity, antecedent rainfall, antecedent weighted rainfall, volumes of wastewater-treatment plant overflows and metered outfalls (composed of storm-water runoff and combined-sewer overflows), a resuspension index, and wave heights. For the beaches in this study, wind speed, wind direction, water temperature, and the prswimmers

[Predictive factors of anxiety disorders].

PubMed

Domschke, K

2014-10-01

Anxiety disorders are among the most frequent mental disorders in Europe (12-month prevalence 14%) and impose a high socioeconomic burden. The pathogenesis of anxiety disorders is complex with an interaction of biological, environmental and psychosocial factors contributing to the overall disease risk (diathesis-stress model). In this article, risk factors for anxiety disorders will be presented on several levels, e.g. genetic factors, environmental factors, gene-environment interactions, epigenetic mechanisms, neuronal networks ("brain fear circuit"), psychophysiological factors (e.g. startle response and CO2 sensitivity) and dimensional/subclinical phenotypes of anxiety (e.g. anxiety sensitivity and behavioral inhibition), and critically discussed regarding their potential predictive value. The identification of factors predictive of anxiety disorders will possibly allow for effective preventive measures or early treatment interventions, respectively, and reduce the individual patient's suffering as well as the overall socioeconomic burden of anxiety disorders.

Prediction of toxic metals concentration using artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.

2011-12-01

Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.

Prediction of tropospheric ozone concentrations by using the design system approach.

PubMed

Abdul-Wahab, Sabah A; Abdo, Jamil

2007-01-01

Data on the concentrations of non-methane hydrocarbons (NMHC), nitrogen oxide (NO), nitrogen dioxide (NO2), carbon monoxide (CO), and meteorological parameters (air temperature and solar radiation) were used to predict the concentration of tropospheric ozone using the Design-Ease software. These data were collected on hourly basis over a 12-month period. Sampling of the data was conducted automatically. The effect of the NMHC, NO, NO2,CO, temperature and solar radiation variables in predicting ozone concentrations was examined under two scenarios: (i) when NO is included with the absence of NO2; and (ii) when NO2 is addressed with the absence of NO. The results of these two scenarios were validated against ozone actual data. The predicted concentration of ozone in the second scenario (i.e., when NO2 is addressed) was in better agreement with the real observations. In addition, the paper indicated that statistical models of hourly surface ozone concentrations require interactions and non-linear relationships between predictor variables in order to accurately capture the ozone behavior.

Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation.

PubMed

Bengtsson-Palme, Johan; Larsson, D G Joakim

2016-01-01

There are concerns that selection pressure from antibiotics in the environment may accelerate the evolution and dissemination of antibiotic-resistant pathogens. Nevertheless, there is currently no regulatory system that takes such risks into account. In part, this is due to limited knowledge of environmental concentrations that might exert selection for resistant bacteria. To experimentally determine minimal selective concentrations in complex microbial ecosystems for all antibiotics would involve considerable effort. In this work, our aim was to estimate upper boundaries for selective concentrations for all common antibiotics, based on the assumption that selective concentrations a priori need to be lower than those completely inhibiting growth. Data on Minimal Inhibitory Concentrations (MICs) were obtained for 111 antibiotics from the public EUCAST database. The 1% lowest observed MICs were identified, and to compensate for limited species coverage, predicted lowest MICs adjusted for the number of tested species were extrapolated through modeling. Predicted No Effect Concentrations (PNECs) for resistance selection were then assessed using an assessment factor of 10 to account for differences between MICs and minimal selective concentrations. The resulting PNECs ranged from 8 ng/L to 64 μg/L. Furthermore, the link between taxonomic similarity between species and lowest MIC was weak. This work provides estimated upper boundaries for selective concentrations (lowest MICs) and PNECs for resistance selection for all common antibiotics. In most cases, PNECs for selection of resistance were below available PNECs for ecotoxicological effects. The generated PNECs can guide implementation of compound-specific emission limits that take into account risks for resistance promotion. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting herbicide and biocide concentrations in rivers across Switzerland

NASA Astrophysics Data System (ADS)

Wemyss, Devon; Honti, Mark; Stamm, Christian

2014-05-01

Pesticide concentrations vary strongly in space and time. Accordingly, intensive sampling is required to achieve a reliable quantification of pesticide pollution. As this requires substantial resources, loads and concentration ranges in many small and medium streams remain unknown. Here, we propose partially filling the information gap for herbicides and biocides by using a modelling approach that predicts stream concentrations without site-specific calibration simply based on generally available data like land use, discharge and nation-wide consumption data. The simple, conceptual model distinguishes herbicide losses from agricultural fields, private gardens and biocide losses from buildings (facades, roofs). The herbicide model is driven by river discharge and the applied herbicide mass; the biocide model requires precipitation and the footprint area of urban areas containing the biocide. The model approach allows for modelling concentrations across multiple catchments at the daily, or shorter, time scale and for small to medium-sized catchments (1 - 100 km2). Four high resolution sampling campaigns in the Swiss Plateau were used to calibrate the model parameters for six model compounds: atrazine, metolachlor, terbuthylazine, terbutryn, diuron and mecoprop. Five additional sampled catchments across Switzerland were used to directly compare the predicted to the measured concentrations. Analysis of the first results reveals a reasonable simulation of the concentration dynamics for specific rainfall events and across the seasons. Predicted concentration ranges are reasonable even without site-specific calibration. This indicates the transferability of the calibrated model directly to other areas. However, the results also demonstrate systematic biases in that the highest measured peaks were not attained by the model. Probable causes for these deviations are conceptual model limitations and input uncertainty (pesticide use intensity, local precipitation, etc

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds.

PubMed

Winchell, Michael F; Peranginangin, Natalia; Srinivasan, Raghavan; Chen, Wenlin

2018-05-01

Recent national regulatory assessments of potential pesticide exposure of threatened and endangered species in aquatic habitats have led to increased need for watershed-scale predictions of pesticide concentrations in flowing water bodies. This study was conducted to assess the ability of the uncalibrated Soil and Water Assessment Tool (SWAT) to predict annual maximum pesticide concentrations in the flowing water bodies of highly vulnerable small- to medium-sized watersheds. The SWAT was applied to 27 watersheds, largely within the midwest corn belt of the United States, ranging from 20 to 386 km 2 , and evaluated using consistent input data sets and an uncalibrated parameterization approach. The watersheds were selected from the Atrazine Ecological Exposure Monitoring Program and the Heidelberg Tributary Loading Program, both of which contain high temporal resolution atrazine sampling data from watersheds with exceptionally high vulnerability to atrazine exposure. The model performance was assessed based upon predictions of annual maximum atrazine concentrations in 1-d and 60-d durations, predictions critical in pesticide-threatened and endangered species risk assessments when evaluating potential acute and chronic exposure to aquatic organisms. The simulation results showed that for nearly half of the watersheds simulated, the uncalibrated SWAT model was able to predict annual maximum pesticide concentrations within a narrow range of uncertainty resulting from atrazine application timing patterns. An uncalibrated model's predictive performance is essential for the assessment of pesticide exposure in flowing water bodies, the majority of which have insufficient monitoring data for direct calibration, even in data-rich countries. In situations in which SWAT over- or underpredicted the annual maximum concentrations, the magnitude of the over- or underprediction was commonly less than a factor of 2, indicating that the model and uncalibrated parameterization

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks.

PubMed

Solis-Paredes, Mario; Estrada-Gutierrez, Guadalupe; Perichart-Perera, Otilia; Montoya-Estrada, Araceli; Guzmán-Huerta, Mario; Borboa-Olivares, Héctor; Bravo-Flores, Eyerahi; Cardona-Pérez, Arturo; Zaga-Clavellina, Veronica; Garcia-Latorre, Ethel; Gonzalez-Perez, Gabriela; Hernández-Pérez, José Alfredo; Irles, Claudine

2017-12-28

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

USGS Publications Warehouse

Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory E.; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas

2017-01-01

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative

Activated Prothrombin Complex Concentrate Versus 4-Factor Prothrombin Complex Concentrate for Vitamin K-Antagonist Reversal.

PubMed

Rowe, A Shaun; Dietrich, Scott K; Phillips, John W; Foster, Kaci E; Canter, Joshua R

2018-06-01

To compare the international normalized ratio normalization efficacy of activated prothrombin complex concentrates and 4-factor prothrombin complex concentrates and to evaluate the thrombotic complications in patients treated with these products for warfarin-associated hemorrhage. Retrospective, Multicenter Cohort. Large, Community, Teaching Hospital. Patients greater than 18 years old and received either activated prothrombin complex concentrate or 4-factor prothrombin complex concentrate for the treatment of warfarin-associated hemorrhage. We excluded those patients who received either agent for an indication other than warfarin-associated hemorrhage, pregnant, had a baseline international normalized ratio of less than 2, received a massive transfusion as defined by hospital protocol, received plasma for treatment of warfarin-associated hemorrhage, or were treated for an acute warfarin ingestion. Patients in the activated prothrombin complex concentrate group (enrolled from one hospital) with an international normalized ratio of less than 5 received 500 IU and those with an international normalized ratio greater than 5 received 1,000 IU. Patients in the 4-factor prothrombin complex concentrate (enrolled from a separate hospital) group received the Food and Drug Administration approved dosing algorithm. A total of 158 patients were included in the final analysis (activated prothrombin complex concentrate = 118; 4-factor prothrombin complex concentrate = 40). Those in the 4-factor prothrombin complex concentrate group had a higher pretreatment international normalized ratio (2.7 ± 1.8 vs 3.5 ± 2.9; p = 0.0164). However, the posttreatment international normalized ratio was similar between the groups. In addition, even when controlling for differences in the pretreatment international normalized ratio, there was no difference in the ability to achieve a posttreatment international normalized ratio of less than 1.4 (odds ratio, 0.753 [95% CI, 0.637-0.890]; p

Prediction of Particle Concentration using Traffic Emission Model

NASA Astrophysics Data System (ADS)

He, Hong-di; Lu, Jane Wei-zhen

2010-05-01

Vehicle emission is regarded as one of major sources of air pollution in urban area. Much attention has been addressed on it especially at traffic intersection. At intersection, vehicles frequently stop with idling engine during the red time and speed-up rapidly in the green time, which result in a high velocity fluctuation and produce extra pollutants to the surrounding air. To deeply understand such process, a semi-empirical model for predicting the changing effect of traffic flow patterns on particulate concentrations is proposed. The performance of the model is evaluated using the correlation coefficient and other parameters. From the results, the correlation coefficients in morning and afternoon data were found to be 0.86 an 0.73 respectively, which implies that the semi-empirical model for morning and afternoon data are 86% and 73% error free. Due to less affected by possible factors such as traffic volume and movement of pedestrian, the dispersion of the particulate matter in the morning is smaller and then contributes to higher performance than that in the afternoon.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

PubMed

de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.; Crawford, Charles G.

2008-01-01

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize

Comparison of predicted pesticide concentrations in groundwater from SCI-GROW and PRZM-GW models with historical monitoring data.

PubMed

Estes, Tammara L; Pai, Naresh; Winchell, Michael F

2016-06-01

A key factor in the human health risk assessment process for the registration of pesticides by the US Environmental Protection Agency (EPA) is an estimate of pesticide concentrations in groundwater used for drinking water. From 1997 to 2011, these estimates were obtained from the EPA empirical model SCI-GROW. Since 2012, these estimates have been obtained from the EPA deterministic model PRZM-GW, which has resulted in a significant increase in estimated groundwater concentrations for many pesticides. Historical groundwater monitoring data from the National Ambient Water Quality Assessment (NAWQA) Program (1991-2014) were compared with predicted groundwater concentrations from both SCI-GROW (v.2.3) and PRZM-GW (v.1.07) for 66 different pesticides of varying environmental fate properties. The pesticide environmental fate parameters associated with over- and underprediction of groundwater concentrations by the two models were evaluated. In general, SCI-GROW2.3 predicted groundwater concentrations were close to maximum historically observed groundwater concentrations. However, for pesticides with soil organic carbon content values below 1000 L kg(-1) and no simulated hydrolysis, PRZM-GW overpredicted, often by greater than 100 ppb. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Hot flashes are not predictive for serum concentrations of tamoxifen and its metabolites

PubMed Central

2013-01-01

Background Tamoxifen has dramatically reduced the recurrence and mortality rate of estrogen receptor positive breast cancer. However, the efficacy of tamoxifen varies between individuals and 40% of patients will have a recurrence despite adjuvant tamoxifen treatment. Factors that predict tamoxifen efficacy would be helpful for optimizing treatment. Serum concentrations of the active metabolite, endoxifen, may be positively related to treatment outcome. In addition, hot flashes are suggested to be positively associated with tamoxifen treatment outcome. Methods We investigated in a series of 109 patients whether the frequency and severity of hot flashes were related to concentrations of tamoxifen and its metabolites. A serum sample of all patients was analyzed for the concentration of tamoxifen, N-desmethyltamoxifen, endoxifen and 4-hydroxytamoxifen, as well as for estradiol concentrations and several single nucleotide polymorphisms in CYP2D6. Additionally, these patients completed a questionnaire concerning biometric data and treatment side effects. Results We found no evidence supporting an association between concentrations of tamoxifen or metabolites and either the frequency or severity of hot flashes in the covariate unadjusted analyses. However, including interactions with menopausal status and pre-treatment hot flash (PTHF) history indicated that post-menopausal women with PTHF experienced an increasing frequency of hot flashes with increasing serum concentrations of tamoxifen and its metabolites. This finding was not altered when adjusting for potential confounding factors (duration of tamoxifen treatment, CYP2D6 phenotype, estradiol serum concentration, age and body mass index). In addition we observed a positive association between body mass index and both hot flash frequency (p = 0.04) and severity (p < 0.0001). We also observed that patients with lower estradiol levels reported more severe hot flashes (p = 0.02). Conclusions No univariate

Characterization of air pollutant concentrations, fleet emission factors, and dispersion near a North Carolina interstate freeway across two seasons

NASA Astrophysics Data System (ADS)

Saha, Provat K.; Khlystov, Andrey; Snyder, Michelle G.; Grieshop, Andrew P.

2018-03-01

We present field measurement data and modeling of multiple traffic-related air pollutants during two seasons at a site adjoining Interstate 40, near Durham, North Carolina. We analyze spatial-temporal and seasonal trends and fleet-average pollutant emission factors and use our data to evaluate a line source dispersion model. Month-long measurement campaigns were performed in summer 2015 and winter 2016. Data were collected at a fixed near-road site located within 10 m from the highway edge, an upwind background site and, under favorable meteorological conditions, along downwind perpendicular transects. Measurements included the size distribution, chemical composition, and volatility of submicron particles, black carbon (BC), nitrogen oxides (NOx), meteorological conditions and traffic activity data. Results show strong seasonal and diurnal differences in spatial distribution of traffic sourced pollutants. A strong signature of vehicle emissions was observed within 100-150 m from the highway edge with significantly higher concentrations during morning. Substantially higher concentrations and less-sharp near-road gradients were observed in winter for many species. Season-specific fleet-average fuel-based emission factors for NO, NOx, BC, and particle number (PN) were derived based on up- and down-wind roadside measurements. The campaign-average NOx and PN emission factors were 20% and 300% higher in winter than summer, respectively. These results suggest that the combined effect of higher emissions and their slower downwind dispersion in winter dictate the observed higher downwind concentrations and wider highway influence zone in winter for several species. Finally, measurements of traffic data, emission factors, and pollutant concentrations were integrated to evaluate a line source dispersion model (R-LINE). The dispersion model captured the general trends in the spatial and temporal patterns in near-road concentrations. However, there was a tendency for the model

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula.

PubMed

Nowosad, Jakub

2016-06-01

Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

NASA Astrophysics Data System (ADS)

Nowosad, Jakub

2016-06-01

Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

Is a high serum copper concentration a risk factor for implantation failure?

PubMed

Matsubayashi, Hidehiko; Kitaya, Kotaro; Yamaguchi, Kohei; Nishiyama, Rie; Takaya, Yukiko; Ishikawa, Tomomoto

2017-08-10

zinc. When we set the cut-off as 1.59/1.60 for the Cu/Zn ratio, sensitivity, specificity, the positive predictive value, and negative predictive value were 0.98, 0.29, 0.71, and 0.88, respectively. Our single-center retrospective study suggests that high serum copper concentrations (high Cu/Zn ratio) are a risk factor for implantation failure.

Radium concentration factors and their use in health and environmental risk assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meinhold, A.F.; Hamilton, L.D.

1991-12-31

Radium is known to be taken up by aquatic animals, and tends to accumulate in bone, shell and exoskeleton. The most common approach to estimating the uptake of a radionuclide by aquatic animals for use in health and environmental risk assessments is the concentration factor method. The concentration factor method relates the concentration of a contaminant in an organism to the concentration in the surrounding water. Site specific data are not usually available, and generic, default values are often used in risk assessment studies. This paper describes the concentration factor method, summarizes some of the variables which may influence themore » concentration factor for radium, reviews reported concentration factors measured in marine environments and presents concentration factors derived from data collected in a study in coastal Louisiana. The use of generic default values for the concentration factor is also discussed.« less

Radium concentration factors and their use in health and environmental risk assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meinhold, A.F.; Hamilton, L.D.

1991-01-01

Radium is known to be taken up by aquatic animals, and tends to accumulate in bone, shell and exoskeleton. The most common approach to estimating the uptake of a radionuclide by aquatic animals for use in health and environmental risk assessments is the concentration factor method. The concentration factor method relates the concentration of a contaminant in an organism to the concentration in the surrounding water. Site specific data are not usually available, and generic, default values are often used in risk assessment studies. This paper describes the concentration factor method, summarizes some of the variables which may influence themore » concentration factor for radium, reviews reported concentration factors measured in marine environments and presents concentration factors derived from data collected in a study in coastal Louisiana. The use of generic default values for the concentration factor is also discussed.« less

Is the Factor-of-2 Rule Broadly Applicable for Evaluating the Prediction Accuracy of Metal-Toxicity Models?

PubMed

Meyer, Joseph S; Traudt, Elizabeth M; Ranville, James F

2018-01-01

In aquatic toxicology, a toxicity-prediction model is generally deemed acceptable if its predicted median lethal concentrations (LC50 values) or median effect concentrations (EC50 values) are within a factor of 2 of their paired, observed LC50 or EC50 values. However, that rule of thumb is based on results from only two studies: multiple LC50 values for the fathead minnow (Pimephales promelas) exposed to Cu in one type of exposure water, and multiple EC50 values for Daphnia magna exposed to Zn in another type of exposure water. We tested whether the factor-of-2 rule of thumb also is supported in a different dataset in which D. magna were exposed separately to Cd, Cu, Ni, or Zn. Overall, the factor-of-2 rule of thumb appeared to be a good guide to evaluating the acceptability of a toxicity model's underprediction or overprediction of observed LC50 or EC50 values in these acute toxicity tests.

Factors which predict violence victimization in Nigeria

PubMed Central

Fry, Lincoln J.

2014-01-01

Background: Violence is a major public health issue, globally as well as in the African continent. This paper looks at Nigeria and begins the process of identifying the factors that predict interpersonal violence in that country. The purpose is to interpret the implications of the results presented here for violence prevention programmes in Nigeria. Materials and Methods: The study is based on the responses of 2324 Nigerians included in Round Four of the Afrobarometer surveys. The study concentrates on 579 respondents who reported either they or someone else in their family had been the victim of violence, defined as being physically attacked, in the past year. Results: A logistical regression analysis revealed five significant factors that predicted interpersonal violence: being the victim of a property crime, the fear of crime, the respondents faith, whethera police station was in the local area and poverty. The findings revealed that 43.7% of the sample had been victimised within the past year and 18.8% had been the victim of both violent and property crimes. One surprising findingwas the number of respondents who were re-victimised; 75% of violence victims also had been property crime victims. Conclusions: These findings suggest that target hardening should be the basis to plan, implement and evaluate violence prevention programmes in Nigeria. Prevention personnel and/or law enforcement need to respond to reported incidents of property and/or violence victimisation and attempt to prepare victims to protect both their premises and their persons in the future. PMID:24970968

Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwestern United States

USGS Publications Warehouse

Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is conducting a regional analysis of water quality in the principal aquifer systems across the United States. The Southwest Principal Aquifers (SWPA) study is building a better understanding of the susceptibility and vulnerability of basin-fill aquifers in the region to groundwater contamination by synthesizing baseline knowledge of groundwater-quality conditions in 16 basins previously studied by the NAWQA Program. The improved understanding of aquifer susceptibility and vulnerability to contamination is assisting in the development of tools that water managers can use to assess and protect the quality of groundwater resources.Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate con­tamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamina­tion and relate nitrate and arsenic concentrations to explana­tory variables representing local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. The classifiers were unbiased and fit the observed data well, and misclassifications were primarily due to statistical sampling error in the training datasets.The classifiers were designed to predict concentrations to be in one of six classes for nitrate, and one of seven classes for arsenic. Each classification scheme allowed for identification of areas with concentrations that were equal to or exceeding

Effect of antacids on predicted steady-state cimetidine concentrations.

PubMed

Russell, W L; Lopez, L M; Normann, S A; Doering, P L; Guild, R T

1984-05-01

The purpose of this study was to evaluate effects of antacids on predicted steady-state concentrations of cimetidine. Ten healthy volunteers received in random order one week apart, cimetidine and cimetidine and antacid suspension. Blood was obtained at specified times and analyzed for cimetidine. Bioavailability was assessed by comparison of peak concentration, time to peak concentration, area under the curve, and time spent over 0.5 micrograms/ml. Single-dose data were extrapolated to steady-state using computer simulation. Concurrent administration of antacid suspension reduced parameters of bioavailability approximately 30%. When steady-state conditions were simulated, concentrations of cimetidine greater than or equal to 0.5 micrograms/ml were maintained for the entire dosing interval in seven of 10 subjects. These data suggest that temporal separation of cimetidine and antacid suspension may be unnecessary.

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

MLP based models to predict PM10, O3 concentrations, in Sines industrial area

NASA Astrophysics Data System (ADS)

Durao, R.; Pereira, M. J.

2012-04-01

Sines is an important Portuguese industrial area located southwest cost of Portugal with important nearby protected natural areas. The main economical activities are related with this industrial area, the deep-water port, petrochemical and thermo-electric industry. Nevertheless, tourism is also an important economic activity especially in summer time with potential to grow. The aim of this study is to develop prediction models of pollutant concentration categories (e.g. low concentration and high concentration) in order to provide early warnings to the competent authorities who are responsible for the air quality management. The knowledge in advanced of pollutant high concentrations occurrence will allow the implementation of mitigation actions and the release of precautionary alerts to population. The regional air quality monitoring network consists in three monitoring stations where a set of pollutants' concentrations are registered on a continuous basis. From this set stands out the tropospheric ozone (O3) and particulate matter (PM10) due to the high concentrations occurring in the region and their adverse effects on human health. Moreover, the major industrial plants of the region monitor SO2, NO2 and particles emitted flows at the principal chimneys (point sources), also on a continuous basis,. Therefore Artificial neuronal networks (ANN) were the applied methodology to predict next day pollutant concentrations; due to the ANNs structure they have the ability to capture the non-linear relationships between predictor variables. Hence the first step of this study was to apply multivariate exploratory techniques to select the best predictor variables. The classification trees methodology (CART) was revealed to be the most appropriate in this case.. Results shown that pollutants atmospheric concentrations are mainly dependent on industrial emissions and a complex combination of meteorological factors and the time of the year. In the second step, the Multi

Validation of two dilution models to predict chloramine-T concentrations in aquaculture facility effluent

USGS Publications Warehouse

Gaikowski, M.P.; Larson, W.J.; Steuer, J.J.; Gingerich, W.H.

2004-01-01

Accurate estimates of drug concentrations in hatchery effluent are critical to assess the environmental risk of hatchery drug discharge resulting from disease treatment. This study validated two dilution simple n models to estimate chloramine-T environmental introduction concentrations by comparing measured and predicted chloramine-T concentrations using the US Geological Survey's Upper Midwest Environmental Sciences Center aquaculture facility effluent as an example. The hydraulic characteristics of our treated raceway and effluent and the accuracy of our water flow rate measurements were confirmed with the marker dye rhodamine WT. We also used the rhodamine WT data to develop dilution models that would (1) estimate the chloramine-T concentration at a given time and location in the effluent system and (2) estimate the average chloramine-T concentration at a given location over the entire discharge period. To test our models, we predicted the chloramine-T concentration at two sample points based on effluent flow and the maintenance of chloramine-T at 20 mg/l for 60 min in the same raceway used with rhodamine WT. The effluent sample points selected (sample points A and B) represented 47 and 100% of the total effluent flow, respectively. Sample point B is-analogous to the discharge of a hatchery that does not have a detention lagoon, i.e. The sample site was downstream of the last dilution water addition following treatment. We then applied four chloramine-T flow-through treatments at 20mg/l for 60 min and measured the chloramine-T concentration in water samples collected every 15 min for about 180 min from the treated raceway and sample points A and B during and after application. The predicted chloramine-T concentration at each sampling interval was similar to the measured chloramine-T concentration at sample points A and B and was generally bounded by the measured 90% confidence intervals. The predicted aver,age chloramine-T concentrations at sample points A or B

Improvement of PM concentration predictability using WRF-CMAQ-DLM coupled system and its applications

NASA Astrophysics Data System (ADS)

Lee, Soon Hwan; Kim, Ji Sun; Lee, Kang Yeol; Shon, Keon Tae

2017-04-01

Air quality due to increasing Particulate Matter(PM) in Korea in Asia is getting worse. At present, the PM forecast is announced based on the PM concentration predicted from the air quality prediction numerical model. However, forecast accuracy is not as high as expected due to various uncertainties for PM physical and chemical characteristics. The purpose of this study was to develop a numerical-statistically ensemble models to improve the accuracy of prediction of PM10 concentration. Numerical models used in this study are the three dimensional atmospheric model Weather Research and Forecasting(WRF) and the community multiscale air quality model (CMAQ). The target areas for the PM forecast are Seoul, Busan, Daegu, and Daejeon metropolitan areas in Korea. The data used in the model development are PM concentration and CMAQ predictions and the data period is 3 months (March 1 - May 31, 2014). The dynamic-statistical technics for reducing the systematic error of the CMAQ predictions was applied to the dynamic linear model(DLM) based on the Baysian Kalman filter technic. As a result of applying the metrics generated from the dynamic linear model to the forecasting of PM concentrations accuracy was improved. Especially, at the high PM concentration where the damage is relatively large, excellent improvement results are shown.

Postoperative Copeptin Concentration Predicts Diabetes Insipidus After Pituitary Surgery.

PubMed

Winzeler, Bettina; Zweifel, Christian; Nigro, Nicole; Arici, Birsen; Bally, Martina; Schuetz, Philipp; Blum, Claudine Angela; Kelly, Christopher; Berkmann, Sven; Huber, Andreas; Gentili, Fred; Zadeh, Gelareh; Landolt, Hans; Mariani, Luigi; Müller, Beat; Christ-Crain, Mirjam

2015-06-01

Copeptin is a stable surrogate marker of vasopressin release; the peptides are stoichiometrically secreted from the neurohypophysis due to elevated plasma osmolality or nonosmotic stress. We hypothesized that following stress from pituitary surgery, patients with neurohypophyseal damage and eventual diabetes insipidus (DI) would not exhibit the expected pronounced copeptin elevation. The objective was to evaluate copeptin's accuracy to predict DI following pituitary surgery. This was a prospective multicenter observational cohort study. Three Swiss or Canadian referral centers were used. Consecutive pituitary surgery patients were included. Copeptin was measured postoperatively daily until discharge. Logistic regression models and diagnostic performance measures were calculated to assess relationships of postoperative copeptin levels and DI. Of 205 patients, 50 (24.4%) developed postoperative DI. Post-surgically, median [25th-75th percentile] copeptin levels were significantly lower in patients developing DI vs those not showing this complication: 2.9 [1.9-7.9] pmol/L vs 10.8 [5.2-30.4] pmol/L; P < .001. Logistic regression analysis revealed strong association between postoperative copeptin concentrations and DI even after considering known predisposing factors for DI: adjusted odds ratio (95% confidence interval) 1.41 (1.16-1.73). DI was seen in 22/27 patients with copeptin <2.5 pmol/L (positive predictive value, 81%; specificity, 97%), but only 1/40 with copeptin >30 pmol/L (negative predictive value, 95%; sensitivity, 94%) on postoperative day 1. Lack of standardized DI diagnostic criteria; postoperative blood samples for copeptin obtained during everyday care vs at fixed time points. In patients undergoing pituitary procedures, low copeptin levels despite surgical stress reflect postoperative DI, whereas high levels virtually exclude it. Copeptin therefore may become a novel tool for early goal-directed management of postoperative DI.

Strontium-90 concentration factors of lake plankton, macrophytes, and substrates.

PubMed

Kalnina, Z; Polikarpov, G

1969-06-27

The ratio of concentration of strontium-90 in living and inert lake components to that in lake water (concentration factors) was determined for plankton, macrophytes, and substrates in eutrophic, mesotropric-eutrophic, and dystrophic Latgalian lakes. Concentration factors of strontium-90 in aquatic organisms and substrates are higher in a dystrophic lake than in the other types.

Morbidity predicting factors of penetrating colon injuries.

PubMed

Mickevicius, A; Valeikaite, G; Tamelis, A; Saladzinskas, Z; Svagzdys, S; Pavalkis, D

2010-01-01

To analyze patients suffering from penetrating colon injuries management, clinical outcomes and factors, which predict higher morbidity and complications rate. this was a retrospective analysis of prospectively collected data from patients with injured colon from 1995 to 2008. Age, time till operation, systolic blood pressure, part of injured colon, fecal contamination, PATI were registered. Monovariate and multivariate logistic regression was performed to determine higher morbidity predictive factors. 61 patients had penetrating colon injuries. Major fecal contamination of the peritoneal cavity and systolic blood pressure lower than 90 mmHg are independent factors determining the fecal diversion operation. Primary repair group analysis establish that major fecal contamination and systolic blood pressure lower than 90 mmHg OR = 4.2 and 0.96 were significant risk factors, which have contributed to the development of postoperative complications. And systolic blood pressure lower than 90 mmHg and PATI 20 predict OR = 0.05 and 2.61 higher morbidity. Fecal contamination of the peritoneal cavity and hypotension were determined to be crucial in choice of performing fecal diversion or primary repair. But the same criteria and PATI predict higher rate of postoperative complications and higher morbidity.

In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions

PubMed Central

Tomar, Dheeraj S.; Li, Li; Broulidakis, Matthew P.; Luksha, Nicholas G.; Burns, Christopher T.; Singh, Satish K.; Kumar, Sandeep

2017-01-01

ABSTRACT Early stage developability assessments of monoclonal antibody (mAb) candidates can help reduce risks and costs associated with their product development. Forecasting viscosity of highly concentrated mAb solutions is an important aspect of such developability assessments. Reliable predictions of concentration-dependent viscosity behaviors for mAb solutions in platform formulations can help screen or optimize drug candidates for flexible manufacturing and drug delivery options. Here, we present a computational method to predict concentration-dependent viscosity curves for mAbs solely from their sequence—structural attributes. This method was developed using experimental data on 16 different mAbs whose concentration-dependent viscosity curves were experimentally obtained under standardized conditions. Each concentration-dependent viscosity curve was fitted with a straight line, via logarithmic manipulations, and the values for intercept and slope were obtained. Intercept, which relates to antibody diffusivity, was found to be nearly constant. In contrast, slope, the rate of increase in solution viscosity with solute concentration, varied significantly across different mAbs, demonstrating the importance of intermolecular interactions toward viscosity. Next, several molecular descriptors for electrostatic and hydrophobic properties of the 16 mAbs derived using their full-length homology models were examined for potential correlations with the slope. An equation consisting of hydrophobic surface area of full-length antibody and charges on VH, VL, and hinge regions was found to be capable of predicting the concentration-dependent viscosity curves of the antibody solutions. Availability of this computational tool may facilitate material-free high-throughput screening of antibody candidates during early stages of drug discovery and development. PMID:28125318

In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions.

PubMed

Tomar, Dheeraj S; Li, Li; Broulidakis, Matthew P; Luksha, Nicholas G; Burns, Christopher T; Singh, Satish K; Kumar, Sandeep

2017-04-01

Early stage developability assessments of monoclonal antibody (mAb) candidates can help reduce risks and costs associated with their product development. Forecasting viscosity of highly concentrated mAb solutions is an important aspect of such developability assessments. Reliable predictions of concentration-dependent viscosity behaviors for mAb solutions in platform formulations can help screen or optimize drug candidates for flexible manufacturing and drug delivery options. Here, we present a computational method to predict concentration-dependent viscosity curves for mAbs solely from their sequence-structural attributes. This method was developed using experimental data on 16 different mAbs whose concentration-dependent viscosity curves were experimentally obtained under standardized conditions. Each concentration-dependent viscosity curve was fitted with a straight line, via logarithmic manipulations, and the values for intercept and slope were obtained. Intercept, which relates to antibody diffusivity, was found to be nearly constant. In contrast, slope, the rate of increase in solution viscosity with solute concentration, varied significantly across different mAbs, demonstrating the importance of intermolecular interactions toward viscosity. Next, several molecular descriptors for electrostatic and hydrophobic properties of the 16 mAbs derived using their full-length homology models were examined for potential correlations with the slope. An equation consisting of hydrophobic surface area of full-length antibody and charges on V H , V L , and hinge regions was found to be capable of predicting the concentration-dependent viscosity curves of the antibody solutions. Availability of this computational tool may facilitate material-free high-throughput screening of antibody candidates during early stages of drug discovery and development.

An Improved Method of Predicting Extinction Coefficients for the Determination of Protein Concentration.

PubMed

Hilario, Eric C; Stern, Alan; Wang, Charlie H; Vargas, Yenny W; Morgan, Charles J; Swartz, Trevor E; Patapoff, Thomas W

2017-01-01

Concentration determination is an important method of protein characterization required in the development of protein therapeutics. There are many known methods for determining the concentration of a protein solution, but the easiest to implement in a manufacturing setting is absorption spectroscopy in the ultraviolet region. For typical proteins composed of the standard amino acids, absorption at wavelengths near 280 nm is due to the three amino acid chromophores tryptophan, tyrosine, and phenylalanine in addition to a contribution from disulfide bonds. According to the Beer-Lambert law, absorbance is proportional to concentration and path length, with the proportionality constant being the extinction coefficient. Typically the extinction coefficient of proteins is experimentally determined by measuring a solution absorbance then experimentally determining the concentration, a measurement with some inherent variability depending on the method used. In this study, extinction coefficients were calculated based on the measured absorbance of model compounds of the four amino acid chromophores. These calculated values for an unfolded protein were then compared with an experimental concentration determination based on enzymatic digestion of proteins. The experimentally determined extinction coefficient for the native proteins was consistently found to be 1.05 times the calculated value for the unfolded proteins for a wide range of proteins with good accuracy and precision under well-controlled experimental conditions. The value of 1.05 times the calculated value was termed the predicted extinction coefficient. Statistical analysis shows that the differences between predicted and experimentally determined coefficients are scattered randomly, indicating no systematic bias between the values among the proteins measured. The predicted extinction coefficient was found to be accurate and not subject to the inherent variability of experimental methods. We propose the use of a

USING TURBIDITY DATA TO PREDICT SUSPENDED SEDIMENT CONCENTRATIONS: POSSIBILITIES, LIMITATIONS, AND PITFALLS

EPA Science Inventory

This talk will look at the relationships between turbidity and suspended sediment concentrations in a variety of geographic areas, geomorphic river types, and river sizes; and attempt to give guidance on using existing turbidity data to predict suspended sediment concentrations.

Iodine concentration: a new, important characteristic of the spot sign that predicts haematoma expansion.

PubMed

Fu, Fan; Sun, Shengjun; Liu, Liping; Li, Jianying; Su, Yaping; Li, Yingying

2018-04-19

The computed tomography angiography (CTA) spot sign is a validated predictor of haematoma expansion (HE) in spontaneous intracerebral haemorrhage (SICH). We investigated whether defining the iodine concentration (IC) inside the spot sign and the haematoma on Gemstone spectral imaging (GSI) would improve its sensitivity and specificity for predicting HE. From 2014 to 2016, we prospectively enrolled 65 SICH patients who underwent single-phase spectral CTA within 6 h. Logistic regression was performed to assess the risk factors for HE. The predictive performance of individual spot sign characteristics was examined via receiver operating characteristic (ROC) analysis. The spot sign was detected in 46.1% (30/65) of patients. ROC analysis indicated that IC inside the spot sign had the greatest area under the ROC curve for HE (0.858; 95% confidence interval, 0.727-0.989; p = 0.003). Multivariate analysis found that spot sign with higher IC (i.e. IC > 7.82 100 μg/ml) was an independent predictor of HE (odds ratio = 34.27; 95% confidence interval, 5.608-209.41; p < 0.001) with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.81, 0.75, 0.90 and 0.60, respectively; while the spot sign showed sensitivity, specificity, PPV and NPV of 0.81, 0.79, 0.73 and 0.86. Logistic regression analysis indicated that the IC in haematomas was independently associated with HE (odds ratio = 1.525; 95% confidence interval, 1.041-2.235; p = 0.030). ICs in haematoma and in spot sign were all independently associated with HE. IC analysis in spectral imaging may help to identify SICH patients for targeted haemostatic therapy. • Iodine concentration in spot sign and haematoma can predict haematoma expansion • Spectral imaging could measure the IC inside the spot sign and haematoma • IC in spot sign improved the positive predictive value (PPV) cf. CTA.

A Comprehensive Review on the Predictive Performance of the Sheiner-Tozer and Derivative Equations for the Correction of Phenytoin Concentrations.

PubMed

Kiang, Tony K L; Ensom, Mary H H

2016-04-01

In settings where free phenytoin concentrations are not available, the Sheiner-Tozer equation-Corrected total phenytoin concentration = Observed total phenytoin concentration/[(0.2 × Albumin) + 0.1]; phenytoin in µg/mL, albumin in g/dL-and its derivative equations are commonly used to correct for altered phenytoin binding to albumin. The objective of this article was to provide a comprehensive and updated review on the predictive performance of these equations in various patient populations. A literature search of PubMed, EMBASE, and Google Scholar was conducted using combinations of the following terms: Sheiner-Tozer, Winter-Tozer, phenytoin, predictive equation, precision, bias, free fraction. All English-language articles up to November 2015 (excluding abstracts) were evaluated. This review shows the Sheiner-Tozer equation to be biased and imprecise in various critical care, head trauma, and general neurology patient populations. Factors contributing to bias and imprecision include the following: albumin concentration, free phenytoin assay temperature, experimental conditions (eg, timing of concentration sampling, steady-state dosing conditions), renal function, age, concomitant medications, and patient type. Although derivative equations using varying albumin coefficients have improved accuracy (without much improvement in precision) in intensive care and elderly patients, these equations still require further validation. Further experiments are also needed to yield derivative equations with good predictive performance in all populations as well as to validate the equations' impact on actual patient efficacy and toxicity outcomes. More complex, multivariate predictive equations may be required to capture all variables that can potentially affect phenytoin pharmacokinetics and clinical therapeutic outcomes. © The Author(s) 2016.

Environmental Factors Affecting Asthma and Allergies: Predicting and Simulating Downwind Exposure to Airborne Pollen

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey; Estes, Sue; Sprigg, William A.; Nickovic, Slobodan; Huete, Alfredo; Solano, Ramon; Ratana, Piyachat; Jiang, Zhangyan; Flowers, Len; Zelicoff, Alan

2009-01-01

This slide presentation reviews the environmental factors that affect asthma and allergies and work to predict and simulate the downwind exposure to airborne pollen. Using a modification of Dust REgional Atmosphere Model (DREAM) that incorporates phenology (i.e. PREAM) the aim was to predict concentrations of pollen in time and space. The strategy for using the model to simulate downwind pollen dispersal, and evaluate the results. Using MODerate-resolution Imaging Spectroradiometer (MODIS), to get seasonal sampling of Juniper, the pollen chosen for the study, land cover on a near daily basis. The results of the model are reviewed.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Antipsychotic therapeutic drug monitoring: psychiatrists’ attitudes and factors predicting likely future use

PubMed Central

Law, Suzanne; Haddad, Peter M.; Chaudhry, Imran B.; Husain, Nusrat; Drake, Richard J.; Flanagan, Robert J.; David, Anthony S.

2015-01-01

Background: This study aimed to explore predictive factors for future use of therapeutic drug monitoring (TDM) and to further examine psychiatrists’ current prescribing practices and perspectives regarding antipsychotic TDM using plasma concentrations. Method: A cross-sectional study for consultant psychiatrists using a postal questionnaire was conducted in north-west England. Data were combined with those of a previous London-based study and principal axis factor analysis was conducted to identify predictors of future use of TDM. Results: Most of the 181 participants (82.9%, 95% confidence interval 76.7–87.7%) agreed that ‘if TDM for antipsychotics were readily available, I would use it’. Factor analysis identified five factors from the original 35 items regarding TDM. Four of the factors significantly predicted likely future use of antipsychotic TDM and together explained 40% of the variance in a multivariate linear regression model. Likely future use increased with positive attitudes and expectations, and decreased with potential barriers, negative attitudes and negative expectations. Scientific perspectives of TDM and psychiatrist characteristics were not significant predictors. Conclusion: Most senior psychiatrists indicated that they would use antipsychotic TDM if available. However, psychiatrists’ attitudes and expectations and the potential barriers need to be addressed, in addition to the scientific evidence, before widespread use of antipsychotic TDM is likely in clinical practice. PMID:26301077

Qigong exercise with concentration predicts increased health.

PubMed

Jouper, John; Hassmén, Peter; Johansson, Mattias

2006-01-01

Regular physical activity has many positive health effects. Despite this, approximately 50% of all adults are not exercising enough to enjoy better health and may, therefore, need an alternative to vigorous physical exercise. Qigong offers a gentle way to exercise the body. A questionnaire sample of 253 participants was collected and correlations with the variable health-now were analyzed. Results showed that health-now was positively correlated with number of completed qigong courses (p < 0.05), with level of concentration (p < 0.01), session-time (p < 0.01), and years of practice (p < 0.05). Among these variables, concentration predicts an increased feeling of health (R(2) = 0.092). Qigong exercise thereby seems to offer a viable alternative to other more vigorous physical activities when wellness is the primary goal. When interpreted using self-determination theory, qigong seems to satisfy needs related to autonomy, competence and relatedness, thereby, primarily attracting individuals who are intrinsically motivated.

Rational and timely haemostatic interventions following cardiac surgery - coagulation factor concentrates or blood bank products.

PubMed

Tang, Mariann; Fenger-Eriksen, Christian; Wierup, Per; Greisen, Jacob; Ingerslev, Jørgen; Hjortdal, Vibeke; Sørensen, Benny

2017-06-01

Cardiac surgery may cause a serious coagulopathy leading to increased risk of bleeding and transfusion demands. Blood bank products are commonly first line haemostatic intervention, but has been associated with hazardous side effect. Coagulation factor concentrates may be a more efficient, predictable, and potentially a safer treatment, although prospective clinical trials are needed to further explore these hypotheses. This study investigated the haemostatic potential of ex vivo supplementation of coagulation factor concentrates versus blood bank products on blood samples drawn from patients undergoing cardiac surgery. 30 adults were prospectively enrolled (mean age=63.9, females=27%). Ex vivo haemostatic interventions (monotherapy or combinations) were performed in whole blood taken immediately after surgery and two hours postoperatively. Fresh-frozen plasma, platelets, cryoprecipitate, fibrinogen concentrate, prothrombin complex concentrate (PCC), and recombinant FVIIa (rFVIIa) were investigated. The haemostatic effect was evaluated using whole blood thromboelastometry parameters, as well as by thrombin generation. Immediately after surgery the compromised maximum clot firmness was corrected by monotherapy with fibrinogen or platelets or combination therapy with fibrinogen. At two hours postoperatively the coagulation profile was further deranged as illustrated by a prolonged clotting time, a reduced maximum velocity and further diminished maximum clot firmness. The thrombin lagtime was progressively prolonged and both peak thrombin and endogenous thrombin potential were compromised. No monotherapy effectively corrected all haemostatic abnormalities. The most effective combinations were: fibrinogen+rFVIIa or fibrinogen+PCC. Blood bank products were not as effective in the correction of the coagulopathy. Coagulation factor concentrates appear to provide a more optimal haemostasis profile following cardiac surgery compared to blood bank products. Copyright © 2017

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

NASA Astrophysics Data System (ADS)

Ni, X. Y.; Huang, H.; Du, W. P.

2017-02-01

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE PAGES

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

2017-03-06

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Using artificial intelligence to predict the equilibrated postdialysis blood urea concentration.

PubMed

Fernández, E A; Valtuille, R; Willshaw, P; Perazzo, C A

2001-01-01

Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated postdialysis blood urea (eqU) is therefore crucial in order to estimate the equilibrated (true) Kt/V. In this work we propose a supervised neural network to predict the eqU at 60 min after the end of hemodialysis. The use of this model is new in this field and is shown to be better than the currently accepted methods (Smye for eqU and Daugirdas for eqKt/V). With this approach we achieve a mean difference error of 0.22 +/- 7.71 mg/ml (mean % error: 1.88 +/- 13.46) on the eqU prediction and a mean difference error for eqKt/V of -0.01 +/- 0.15 (mean % error: -0.95 +/- 14.73). The equilibrated Kt/V estimated with the eqU calculated using the Smye formula is not appropriate because it showed a great dispersion. The Daugirdas double-pool Kt/V estimation formula appeared to be accurate and in agreement with the results of the HEMO study. Copyright 2001 S. Karger AG, Basel.

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

PubMed

Størset, Elisabet; Holford, Nick; Hennig, Stefanie; Bergmann, Troels K; Bergan, Stein; Bremer, Sara; Åsberg, Anders; Midtvedt, Karsten; Staatz, Christine E

2014-09-01

The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

Use of linear regression models to determine influence factors on the concentration levels of radon in occupied houses

NASA Astrophysics Data System (ADS)

Buermeyer, Jonas; Gundlach, Matthias; Grund, Anna-Lisa; Grimm, Volker; Spizyn, Alexander; Breckow, Joachim

2016-09-01

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO2) was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO2 concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO2 in combination with environmental parameters, as well as the development of advices for ventilation.

Factors affecting corticosteroid concentrations in yellow-bellied marmots.

PubMed

Armitage, K B

1991-01-01

1. Bound and total corticosteroid concentrations of yellow-bellied marmots (Marmota flaviventris) were lowest in May after emergence from hibernation and peaked in August prior to immergence. 2. Total corticosteroids were affected by age but not by sex or reproductive status. 3. There was no consistent relationship between measures of population density and concentrations of corticosteroids; when a significant relationship occurred, only 22-34% of the variation was explained. 4. Social status and social behavior were the major factors affecting corticosteroid concentrations.

Do plasma concentrations of apelin predict prognosis in patients with advanced heart failure?

PubMed

Dalzell, Jonathan R; Jackson, Colette E; Chong, Kwok S; McDonagh, Theresa A; Gardner, Roy S

2014-01-01

Apelin is an endogenous vasodilator and inotrope, plasma concentrations of which are reduced in advanced heart failure (HF). We determined the prognostic significance of plasma concentrations of apelin in advanced HF. Plasma concentrations of apelin were measured in 182 patients with advanced HF secondary to left ventricular systolic dysfunction. The predictive value of apelin for the primary end point of all-cause mortality was assessed over a median follow-up period of 544 (IQR: 196-923) days. In total, 30 patients (17%) reached the primary end point. Of those patients with a plasma apelin concentration above the median, 14 (16%) reached the primary end point compared with 16 (17%) of those with plasma apelin levels below the median (p = NS). NT-proBNP was the most powerful prognostic marker in this population (log rank statistic: 10.37; p = 0.001). Plasma apelin concentrations do not predict medium to long-term prognosis in patients with advanced HF secondary to left ventricular systolic dysfunction.

Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example.

PubMed

Li, Tong; Liu, Shu-Shen; Qu, Rui; Liu, Hai-Ling

2017-10-01

The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC 50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC 30 , and EC 70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs. Copyright © 2017. Published by Elsevier Inc.

Use of a food web model to evaluate the factors responsible for high PCB fish concentrations in Lake Ellasjøen, a high arctic lake.

PubMed

Gewurtz, Sarah B; Gandhi, Nilima; Christensen, Guttorm N; Evenset, Anita; Gregor, Dennis; Diamond, Miriam L

2009-03-01

Lake Ellasjøen, located in the Norwegian high arctic, contains the highest concentrations of polychlorinated biphenyls (PCBs) ever recorded in fish and sediment from high arctic lakes, and concentrations are more than 10 times greater than in nearby Lake Øyangen. These elevated concentrations in Ellasjøen have been previously attributed, in part, to contaminant loadings from seabirds that use Ellasjøen, but not Øyangen, as a resting area. However, other factors, such as food web structure, organism growth rate, weight, lipid content, lake morphology, and nutrient inputs from the seabird guano, also differ between the two systems. The aim of this study is to evaluate the relative influence of these factors as explanatory variables for the higher PCB fish concentrations in Ellasjøen compared with Øyangen, using both a food web model and empirical data. The model is based on previously developed models but parameterized for Lakes Ellasjøen and Øyangen using measured data wherever possible. The model was applied to five representative PCB congeners (PCB 105, 118, 138, 153, and 180) using measured sediment and water concentrations as input data and evaluated with previously collected food web data. Modeled concentrations are within a factor of two of measured concentrations in 60% and 40% of the cases in Lakes Ellasjøen and Øyangen, respectively, and within a factor of 10 in 100% of the cases in both lakes. In many cases, this is comparable to the variability associated with the data as well as the efficacy of the predictions of other food web model applications. We next used the model to quantify the relative importance of five major differences between Ellasjøen and Øyangen by replacing variables representing each of these factors in the Ellasjøen model with those from Øyangen, in separate simulations. The model predicts that the elevated PCB concentrations in Ellasjøen water and sediment account for 49%-58% of differences in modeled fish PCB

Evaluation of measured and predicted environmental concentrations of selected human pharmaceuticals and personal care products.

PubMed

Liebig, Markus; Moltmann, Johann F; Knacker, Thomas

2006-03-01

comparison with calculated PECs. Average MECs(sw) in Germany were < 0.58 ng/L for 17alpha-ethinylestradiol, 454 ng/L for carbamazepine, 126 ng/L for sulfamethoxazole, 1105 ng/L for iopromide and 311 ng/L for tonalide. In comparison to the measured concentrations, PECs calculated with the model proposed by the EMEA in 2001 were in the same range, but slightly higher than the MECs. The EMEA model from 2001 is based on a production/use volume of the PPCPs. The more recent EMEA model (2003/2005) overestimated the PECs by more than one order of magnitude for carbamazepine and sulfamethoxazole, but underestimated the concentration of 17alpha-ethinylestradiol by a factor of almost 5 compared to the MECs. This model is based on maximum daily doses and the assumption that 1% of the population is consuming the pharmaceutical (default value). Calculations with the European Union System for the Evaluation of Substances (EUSES), which is part of the TGD describing the risk assessment of chemicals and biocides, resulted for the investigated pharmaceuticals in almost the same PECs as derived by the older EMEA model (2001). For the PCP tonalide, to which the recent EMEA model (2003/ 2005) cannot be applied, the PEC was overestimated by a factor of 3 with the older EMEA model (2001), but underestimated with EUSES by a factor of 5 compared to the averaged MECsw in Germany. Conclusions. It was shown that PEC calculations with exposure models provided by EMEA and the TGD, resulted in PECs very close to the corresponding MECs in most cases. However, environmental concentrations can be underestimated by models in cases, where, e.g. due to high lipophilicity, sorption to sewage sludge is assumed which does not occur to that extent under real conditions. Thus, it appears that the exposure models do not come up to the complexity of the real environment. However, the main factor with the highest impact on predicted environmental concentrations and a high degree of uncertainty is the production volume

Size distribution and concentrations of heavy metals in atmospheric aerosols originating from industrial emissions as predicted by the HYSPLIT model

NASA Astrophysics Data System (ADS)

Chen, Bing; Stein, Ariel F.; Maldonado, Pabla Guerrero; Sanchez de la Campa, Ana M.; Gonzalez-Castanedo, Yolanda; Castell, Nuria; de la Rosa, Jesus D.

2013-06-01

This study presents a description of the emission, transport, dispersion, and deposition of heavy metals contained in atmospheric aerosols emitted from a large industrial complex in southern Spain using the HYSPLIT model coupled with high- (MM5) and low-resolution (GDAS) meteorological simulations. The dispersion model was configured to simulate eight size fractions (<0.33, 0.66, 1.3, 2.5, 5, 14, 17, and >17 μm) of metals based on direct measurements taken at the industrial emission stacks. Twelve stacks in four plants were studied and the stacks showed considerable differences for both emission fluxes and size ranges of metals. We model the dispersion of six major metals; Cr, Co, Ni, La, Zn, and Mo, which represent 77% of the total mass of the 43 measured elements. The prediction shows that the modeled industrial emissions produce an enrichment of heavy metals by a factor of 2-5 for local receptor sites when compared to urban and rural background areas in Spain. The HYSPLIT predictions based on the meteorological fields from MM5 show reasonable consistence with the temporal evolution of concentrations of Cr, Co, and Ni observed at three sites downwind of the industrial area. The magnitude of concentrations of metals at two receptors was underestimated for both MM5 (by a factor of 2-3) and GDAS (by a factor of 4-5) meteorological runs. The model prediction shows that heavy metal pollution from industrial emissions in this area is dominated by the ultra-fine (<0.66 μm) and fine (<2.5 μm) size fractions.

Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment.

PubMed

James, Katherine A; Meliker, Jaymie R; Buttenfield, Barbara E; Byers, Tim; Zerbe, Gary O; Hokanson, John E; Marshall, Julie A

2014-08-01

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83-0.92 for samples collected from the same well 15-25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.

Sirolimus and everolimus clearance in maintenance kidney and liver transplant recipients: Diagnostic efficiency of the concentration/dose ratio for the prediction of trough steady-state concentrations

PubMed Central

Bouzas, Lorena; Hermida, Jesús

2010-01-01

Objectives Therapeutic monitoring of sirolimus and everolimus is necessary in order to minimize adverse side-effects and to ensure effective immunosuppression. A sirolimus-dosing model using the concentration/dose ratio has been previously proposed for kidney transplant patients, and the aim of our study was the evaluation of this single model for the prediction of trough sirolimus and everolimus concentrations. Methods Trough steady-state sirolimus concentrations were determined in several blood samples from each of 7 kidney and 9 liver maintenance transplant recipients, and everolimus concentrations from 20 kidney, 17 liver, and 3 kidney/liver maintenance transplant recipients. Predicted sirolimus and everolimus concentrations (Css), corresponding to the doses (D), were calculated using the measured concentrations (Css0) and corresponding doses (D0) on starting the study: Css = (Css0)(D)/D0. Results The diagnostic efficiency of the predicting model for the correct classification as subtherapeutic, therapeutic, and supratherapeutic values with respect to the experimentally obtained concentrations was 91.3% for sirolimus and 81.4% for everolimus in the kidney transplant patients. In the liver transplant patients the efficiency was 69.2% for sirolimus and 72.6% for everolimus, and in the kidney/liver transplant recipients the efficiency for everolimus was 67.9%. Conclusions The model has an acceptable diagnostic efficiency (>80%) for the prediction of sirolimus and everolimus concentrations in kidney transplant recipients, but not in liver transplant recipients. However, considering the wide ranges found for the prediction error of sirolimus and everolimus concentrations, the clinical relevance of this dosing model is weak. PMID:19943816

Effects of User Puff Topography, Device Voltage, and Liquid Nicotine Concentration on Electronic Cigarette Nicotine Yield: Measurements and Model Predictions

PubMed Central

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat

2015-01-01

Introduction: Some electronic cigarette (ECIG) users attain tobacco cigarette–like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Methods: Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3–5.2 V or 3.0–7.5 W) and e-liquid nicotine concentration (18–36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Results: Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R 2 = 0.99). Conclusions: Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. PMID:25187061

Predicting the Activity Coefficients of Free-Solvent for Concentrated Globular Protein Solutions Using Independently Determined Physical Parameters

PubMed Central

McBride, Devin W.; Rodgers, Victor G. J.

2013-01-01

The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations. PMID:24324733

Predicting the toxicity of sediment-associated trace metals with simultaneously extracted trace metal: Acid-volatile sulfide concentrations and dry weight-normalized concentrations: A critical comparison

USGS Publications Warehouse

Long, E.R.; MacDonald, D.D.; Cubbage, J.C.; Ingersoll, C.G.

1998-01-01

The relative abilities of sediment concentrations of simultaneously extracted trace metal: acid-volatile sulfide (SEM: AVS) and dry weight-normalized trace metals to correctly predict both toxicity and nontoxicity were compared by analysis of 77 field-collected samples. Relative to the SEM:AVS concentrations, sediment guidelines based upon dry weight-normalized concentrations were equally or slightly more accurate in predicting both nontoxic and toxic results in laboratory tests.

Prediction of ore fluid metal concentrations from solid solution concentrations in ore-stage calcite: Application to the Illinois-Kentucky and Central Tennessee Mississippi Valley-type districts

NASA Astrophysics Data System (ADS)

Smith-Schmitz, Sarah E.; Appold, Martin S.

2018-03-01

Knowledge of the concentrations of Zn and Pb in Mississippi Valley-type (MVT) ore fluids is fundamental to understanding MVT deposit origin. Most previous attempts to quantify the concentrations of Zn and Pb in MVT ore fluids have focused on the analysis of fluid inclusions. However, these attempts have yielded ambiguous results due to possible contamination from secondary fluid inclusions, interferences from Zn and Pb in the host mineral matrix, and uncertainties about whether the measured Zn and Pb signals represent aqueous solute or accidental solid inclusions entrained within the fluid inclusions. The purpose of the present study, therefore, was to try to determine Zn and Pb concentrations in MVT ore fluids using an alternate method that avoids these ambiguities by calculating Zn and Pb concentrations in MVT ore fluids theoretically based on their solid solution concentrations in calcite. This method was applied to the Illinois-Kentucky and Central Tennessee districts, which both contain ore-stage calcite. Experimental partition coefficient (D) values from Rimstidt et al. (1998) and Tsusue and Holland (1966), and theoretical thermodynamic distribution coefficient (KD) values were employed in the present study. Ore fluid concentrations of Zn were likely most accurately predicted by Rimstidt et al. (1998) D values, based on their success in predicting known fluid inclusion concentrations of Mg and Mn, and likely also most accurately predicted ore fluid concentrations of Fe. All four of these elements have a divalent ionic radius smaller than that of Ca2+ and form carbonate minerals with the calcite structure. For both the Illinois-Kentucky and the Central Tennessee district, predicted ore fluid Zn and Fe concentrations were on the order of up to 10's of ppm. Ore fluid concentrations of Pb could only be predicted using Rimstidt et al. (1998) D values. However, these concentrations are unlikely to be reliable, as predicted ore fluid concentrations of Sr and Ba

Prediction of suspended-sediment concentrations at selected sites in the Fountain Creek watershed, Colorado, 2008-09

USGS Publications Warehouse

Stogner, Sr., Robert W.; Nelson, Jonathan M.; McDonald, Richard R.; Kinzel, Paul J.; Mau, David P.

2013-01-01

In 2008, the U.S. Geological Survey (USGS), in cooperation with Pikes Peak Area Council of Governments, Colorado Water Conservation Board, Colorado Springs City Engineering, and the Lower Arkansas Valley Water Conservancy District, began a small-scale pilot study to evaluate the effectiveness of the use of a computational model of streamflow and suspended-sediment transport for predicting suspended-sediment concentrations and loads in the Fountain Creek watershed in Colorado. Increased erosion and sedimentation damage have been identified by the Fountain Creek Watershed Plan as key problems within the watershed. A recommendation in the Fountain Creek Watershed plan for management of the basin is to establish measurable criteria to determine if progress in reducing erosion and sedimentation damage is being made. The major objective of this study was to test a computational method to predict local suspended-sediment loads at two sites with different geomorphic characteristics in order to evaluate the feasibility of using such an approach to predict local suspended-sediment loads throughout the entire watershed. Detailed topographic surveys, particle-size data, and suspended-sediment samples were collected at two gaged sites: Monument Creek above Woodmen Road at Colorado Springs, Colorado (USGS gage 07103970), and Sand Creek above mouth at Colorado Springs, Colorado (USGS gage 07105600). These data were used to construct three-dimensional computational models of relatively short channel reaches at each site. The streamflow component of these models predicted a spatially distributed field of water-surface elevation, water velocity, and bed shear stress for a range of stream discharges. Using the model predictions, along with measured particle sizes, the sediment-transport component of the model predicted the suspended-sediment concentration throughout the reach of interest. These computed concentrations were used with predicted flow patterns and channel morphology to

Interaction and influence of two creeks on Escherichia coli concentrations of nearby beaches: Exploration of predictability and mechanisms

USGS Publications Warehouse

Nevers, M.B.; Whitman, R.L.; Frick, W.E.; Ge, Z.

2007-01-01

The impact of river outfalls on beach water quality depends on numerous interacting factors. The delivery of contaminants by multiple creeks greatly complicates understanding of the source contributions, especially when pollution might originate up- or down-coast of beaches. We studied two beaches along Lake Michigan that are located between two creek outfalls to determine the hydrometeorologic factors influencing near-shore microbiologic water quality and the relative impact of the creeks. The creeks continuously delivered water with high concentrations of Escherichia coli to Lake Michigan, and the direction of transport of these bacteria was affected by current direction. Current direction reversals were associated with elevated E. coli concentrations at Central Avenue beach. Rainfall, barometric pressure, wave height, wave period, and creek specific conductance were significantly related to E. coli concentration at the beaches and were the parameters used in predictive models that best described E. coli variation at the two beaches. Multiple inputs to numerous beaches complicates the analysis and understanding of the relative relationship of sources but affords opportunities for showing how these complex creek inputs might interact to yield collective or individual effects on beach water quality.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

NASA Astrophysics Data System (ADS)

Tesoriero, Anthony J.; Gronberg, Jo Ann; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-08-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

USGS Publications Warehouse

Tesoriero, Anthony J.; Gronberg, Jo Ann M.; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-01-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Can Childhood Factors Predict Workplace Deviance?

PubMed Central

Piquero, Nicole Leeper; Moffitt, Terrie E.

2013-01-01

Compared to the more common focus on street crime, empirical research on workplace deviance has been hampered by highly select samples, cross-sectional research designs, and limited inclusion of relevant predictor variables that bear on important theoretical debates. A key debate concerns the extent to which childhood conduct-problem trajectories influence crime over the life-course, including adults’ workplace crime, whether childhood low self-control is a more important determinant than trajectories, and/or whether each or both of these childhood factors relate to later criminal activity. This paper provides evidence on this debate by examining two types of workplace deviance: production and property deviance separately for males and females. We use data from the Dunedin Multidisciplinary Health and Development Study, a birth cohort followed into adulthood, to examine how childhood factors (conduct-problem trajectories and low self-control) and then adult job characteristics predict workplace deviance at age 32. Analyses revealed that none of the childhood factors matter for predicting female deviance in the workplace but that conduct-problem trajectories did account for male workplace deviance. PMID:24882937

Modifiable factors associated with copeptin concentration: a general population cohort.

PubMed

van Gastel, Maatje D A; Meijer, Esther; Scheven, Lieneke E; Struck, Joachim; Bakker, Stephan J L; Gansevoort, Ron T

2015-05-01

Vasopressin plays an important role in maintaining volume homeostasis. However, recent studies suggest that vasopressin also may play a detrimental role in the progression of chronic kidney disease. It therefore is of interest to identify factors that influence vasopressin concentration, particularly modifiable ones. Cross-sectional analyses. Data used are from participants in a large general-population cohort study (Prevention of Renal and Vascular Endstage Disease [PREVEND]). Patients with a missing copeptin value (n=888), nonfasting blood sample (n=495), missing or assumed incorrect 24-hour urine collection (n=388), or heart failure (n=20) were excluded, leaving 6,801 participants for analysis. Identification of lifestyle- and diet-related factors that are associated with copeptin concentration. Copeptin concentration as surrogate for vasopressin. Copeptin was measured by an immunoluminometric assay as a surrogate for vasopressin. Associations were assessed in uni- and multivariable linear regression analyses. Median copeptin concentration was 4.7 (IQR, 2.9-7.6) pmol/L. When copeptin was studied as a dependent variable, the final stepwise backward model revealed associations with higher copeptin concentrations for lower 24-hour urine volume (P < 0.001), higher sodium excretion (P < 0.001), higher systolic blood pressure (P < 0.001), current smoking (P < 0.001), higher alcohol use (P < 0.001), higher urea excretion (P = 0.003), lower potassium excretion (P = 0.002), use of glucose-lowering drugs (P = 0.02), higher body mass index (P < 0.001), and higher plasma glucose level (P < 0.001). No associations with copeptin concentration were found for C-reactive protein or use of diuretics or nondiuretic antihypertensives. The cross-sectional study design does not allow firm conclusions on cause-effect relationships. Important lifestyle- and diet-related factors associated with copeptin concentration are current smoking, alcohol use, protein and potassium intake, and

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

PubMed

Hidalgo, J Ignacio; Colmenar, J Manuel; Kronberger, Gabriel; Winkler, Stephan M; Garnica, Oscar; Lanchares, Juan

2017-08-08

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

Environmental Factors and Seasonality Affect the Concentration of Rotundone in Vitis vinifera L. cv. Shiraz Wine

PubMed Central

Zhang, Pangzhen; Howell, Kate; Krstic, Mark; Herderich, Markus; Barlow, Edward William R.; Fuentes, Sigfredo

2015-01-01

Rotundone is a sesquiterpene that gives grapes and wine a desirable ‘peppery’ aroma. Previous research has reported that growing grapevines in a cool climate is an important factor that drives rotundone accumulation in grape berries and wine. This study used historical data sets to investigate which weather parameters are mostly influencing rotundone concentration in grape berries and wine. For this purpose, wines produced from 15 vintages from the same Shiraz vineyard (The Old Block, Mount Langi Ghiran, Victoria, Australia) were analysed for rotundone concentration and compared to comprehensive weather data and minimal temperature information. Degree hours were obtained by interpolating available temperature information from the vineyard site using a simple piecewise cubic hermite interpolating polynomial method (PCHIP). Results showed that the highest concentrations of rotundone were consistently found in wines from cool and wet seasons. The Principal Component Analysis (PCA) showed that the concentration of rotundone in wine was negatively correlated with daily solar exposure and grape bunch zone temperature, and positively correlated with vineyard water balance. Finally, models were constructed based on the Gompertz function to describe the dynamics of rotundone concentration in berries through the ripening process according to phenological and thermal times. This characterisation is an important step forward to potentially predict the final quality of the resultant wines based on the evolution of specific compounds in berries according to critical environmental and micrometeorological variables. The modelling techniques described in this paper were able to describe the behaviour of rotundone concentration based on seasonal weather conditions and grapevine phenological stages, and could be potentially used to predict the final rotundone concentration early in future growing seasons. This could enable the adoption of precision irrigation and canopy

Preparation of factor VII concentrate using CNBr-activated Sepharose 4B immunoaffinity chromatography

PubMed Central

Mousavi Hosseini, Kamran; Nasiri, Saleh

2015-01-01

Background: Factor VII concentrates are used in patients with congenital or acquired factor VII deficiency or treatment of hemophilia patients with inhibitors. In this research, immunoaffinity chromatography was used to purify factor VII from prothrombin complex (Prothrombin- Proconvertin-Stuart Factor-Antihemophilic Factor B or PPSB) which contains coagulation factors II, VII, IX and X. The aim of this study was to improve purity, safety and tolerability as a highly purified factor VII concentrate. Methods: PPSB was prepared using DEAE-Sephadex and was used as the starting material for purification of coagulation factor VII. Prothrombin complex was treated by solvent/detergent at 24°C for 6 h with constant stirring. The mixture of PPSB in the PBS buffer was filtered and then chromatographed using CNBr-activated Sepharose 4B coupled with specific antibody. Factors II, IX, VII, X and VIIa were assayed on the fractions. Fractions of 48-50 were pooled and lyophilized as a factor VII concentrate. Agarose gel electrophoresis was performed and Tween 80 was measured in the factor VII concentrate. Results: Specific activity of factor VII concentrate increased from 0.16 to 55.6 with a purificationfold of 347.5 and the amount of activated factor VII (FVIIa) was found higher than PPSB (4.4-fold). Results of electrophoresis on agarose gel indicated higher purity of Factor VII compared to PPSB; these finding revealed that factor VII migrated as alpha-2 proteins. In order to improve viral safety, solvent-detergent treatment was applied prior to further purification and nearly complete elimination of tween 80 (2 μg/ml). Conclusion: It was concluded that immuonoaffinity chromatography using CNBr-activated Sepharose 4B can be a suitable choice for large-scale production of factor VII concentrate with higher purity, safety and activated factor VII. PMID:26034723

Preparation of factor VII concentrate using CNBr-activated Sepharose 4B immunoaffinity chromatography.

PubMed

Mousavi Hosseini, Kamran; Nasiri, Saleh

2015-01-01

Factor VII concentrates are used in patients with congenital or acquired factor VII deficiency or treatment of hemophilia patients with inhibitors. In this research, immunoaffinity chromatography was used to purify factor VII from prothrombin complex (Prothrombin- Proconvertin-Stuart Factor-Antihemophilic Factor B or PPSB) which contains coagulation factors II, VII, IX and X. The aim of this study was to improve purity, safety and tolerability as a highly purified factor VII concentrate. PPSB was prepared using DEAE-Sephadex and was used as the starting material for purification of coagulation factor VII. Prothrombin complex was treated by solvent/detergent at 24°C for 6 h with constant stirring. The mixture of PPSB in the PBS buffer was filtered and then chromatographed using CNBr-activated Sepharose 4B coupled with specific antibody. Factors II, IX, VII, X and VIIa were assayed on the fractions. Fractions of 48-50 were pooled and lyophilized as a factor VII concentrate. Agarose gel electrophoresis was performed and Tween 80 was measured in the factor VII concentrate. Specific activity of factor VII concentrate increased from 0.16 to 55.6 with a purificationfold of 347.5 and the amount of activated factor VII (FVIIa) was found higher than PPSB (4.4-fold). RESULTS of electrophoresis on agarose gel indicated higher purity of Factor VII compared to PPSB; these finding revealed that factor VII migrated as alpha-2 proteins. In order to improve viral safety, solvent-detergent treatment was applied prior to further purification and nearly complete elimination of tween 80 (2 μg/ml). It was concluded that immuonoaffinity chromatography using CNBr-activated Sepharose 4B can be a suitable choice for large-scale production of factor VII concentrate with higher purity, safety and activated factor VII.

Current use of factor concentrates in pediatric cardiac anesthesia.

PubMed

Guzzetta, Nina A; Williams, Glyn D

2017-07-01

Excessive bleeding following pediatric cardiopulmonary bypass is associated with increased morbidity and mortality, both from the effects of hemorrhage and the therapies employed to achieve hemostasis. Neonates and infants are especially at risk because their coagulation systems are immature, surgeries are often complex, and cardiopulmonary bypass technologies are inappropriately matched to patient size and physiology. Consequently, these young children receive substantial amounts of adult-derived blood products to restore adequate hemostasis. Adult and pediatric data demonstrate associations between blood product transfusions and adverse patient outcomes. Thus, efforts to limit bleeding after pediatric cardiopulmonary bypass and minimize allogeneic blood product exposure are warranted. The off-label use of factor concentrates, such as fibrinogen concentrate, recombinant activated factor VII, and prothrombin complex concentrates, is increasing as these hemostatic agents appear to offer several advantages over conventional blood products. However, recognizing that these agents have the potential for both benefit and harm, well-designed studies are needed to enhance our knowledge and to determine the optimal use of these agents. In this review, our primary objective was to examine the evidence regarding the use of factor concentrates to treat bleeding after pediatric CPB and identify where further research is required. PubMed, MEDLINE/OVID, The Cochrane Library and the Cochrane Central Register of Controlled Trials (CENTRAL) were systematically searched to identify existing studies. © 2017 John Wiley & Sons Ltd.

Control of exogenous factors affecting plasma homovanillic acid concentration.

PubMed

Davidson, M; Giordani, A B; Mohs, R C; Mykytyn, V V; Platt, S; Aryan, Z S; Davis, K L

1987-04-01

Measurements of plasma homovanillic acid (pHVA) concentrations appear to be a valid research strategy in psychiatric disorders in which a central dopamine (DA) abnormality has been implicated. This study provides guidance about the control of some of the exogenous factors affecting pHVA concentrations. Fasting for 14 hours eliminates the dietary effects on pHVA in healthy human subjects. Changing position, walking for 30 minutes, or smoking two cigarettes has no effect on pHVA concentrations.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales

USGS Publications Warehouse

Collins, Sarah M.; Oliver, Samantha K.; Lapierre, Jean-Francois; Stanley, Emily H.; Jones, John R.; Wagner, Tyler; Soranno, Patricia A.

2017-01-01

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

PubMed Central

Hadrup, Niels; Taxvig, Camilla; Pedersen, Mikael; Nellemann, Christine; Hass, Ulla; Vinggaard, Anne Marie

2013-01-01

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be

Family Factors Predicting Categories of Suicide Risk

ERIC Educational Resources Information Center

Randell, Brooke P.; Wang, Wen-Ling; Herting, Jerald R.; Eggert, Leona L.

2006-01-01

We compared family risk and protective factors among potential high school dropouts with and without suicide-risk behaviors (SRB) and examined the extent to which these factors predict categories of SRB. Subjects were randomly selected from among potential dropouts in 14 high schools. Based upon suicide-risk status, 1,083 potential high school…

Clinical Factors Predict Atezolizumab Response.

PubMed

2018-04-01

Researchers have presented a new model that uses six readily available clinical factors to predict whether a patient with advanced bladder cancer who has already received platinum chemotherapy will respond to treatment with the PD-L1 inhibitor atezolizumab. The results may help patients and their doctors decide how to proceed with treatment. ©2018 American Association for Cancer Research.

Predictive Factors for Death After Snake Envenomation in Myanmar.

PubMed

Aye, Kyi-Phyu; Thanachartwet, Vipa; Soe, Chit; Desakorn, Varunee; Chamnanchanunt, Supat; Sahassananda, Duangjai; Supaporn, Thanom; Sitprija, Visith

2018-06-01

Factors predictive for death from snake envenomation vary between studies, possibly due to variation in host genetic factors and venom composition. This study aimed to evaluate predictive factors for death from snake envenomation in Myanmar. A prospective study was performed among adult patients with snakebite admitted to tertiary hospitals in Yangon, Myanmar, from May 2015 to August 2016. Data including clinical variables and laboratory parameters, management, and outcomes were evaluated. Multivariate regression analysis was performed to evaluate factors predictive for death at the time of presentation to the hospital. Of the 246 patients with snake envenomation recruited into the study, 225 (92%) survived and 21 (8%) died during hospitalization. The snake species responsible for a bite was identified in 74 (30%) of the patients; the majority of bites were from Russell's vipers (63 patients, 85%). The independent factors predictive for death included 1) duration from bite to arrival at the hospital >1 h (odds ratio [OR]: 9.0, 95% confidence interval [CI]: 1.1-75.2; P=0.04); 2) white blood cell counts >20 ×10 3 cells·μL -1 (OR: 8.9, 95% CI: 2.3-33.7; P=0.001); and 3) the presence of capillary leakage (OR: 3.7, 95% CI: 1.2-11.2; P=0.02). A delay in antivenom administration >4 h increases risk of death (11/21 deaths). Patients who present with these independent predictive factors should be recognized and provided with early appropriate intervention to reduce the mortality rate among adults with snake envenomation in Myanmar. Copyright © 2018 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

PubMed Central

Chang, Howard H.; Hu, Xuefei; Liu, Yang

2014-01-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial–temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003–2005. Via cross-validation experiments, our model had an out-of-sample prediction R2 of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m3 between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial–temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined. PMID:24368510

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling.

PubMed

Chang, Howard H; Hu, Xuefei; Liu, Yang

2014-07-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

[Predictive factors associated with severity of asthma exacerbations].

PubMed

Atiş, Sibel; Kaplan, Eylem Sercan; Ozge, Cengiz; Bayindir, Suzan

2008-01-01

Several factors have been accused for asthma exacerbations, however, very few studies have evaluated whether different factors predict severity of asthma exacerbation. We aimed to determine the predictive factors for severity of asthma exacerbation. Retrospective analysis of data on 93 patients visited our emergency-department because of asthma exacerbation was reviewed. Hospitalization in intensive care unit and/or intubation because of asthma was accepted as the criteria for severe exacerbation. Logistic regression analysis estimated the strength of association of each variable, potentially related to severe asthmatic exacerbation, with severe/very severe as compared to mild/moderate asthmatic exacerbation. Independent variables included in the analysis were age, sex, smoking history, inhaler steroid using, compliance with medication, chronic asthma severity, presence of additional atopic diseases, prick test positivity, provocative factors, number of short-acting beta(2)-agonist using, number of visits to emergency department for asthma over one year period, previous severe exacerbation, pulmonary functions, and blood eosinophil count. 20 were severe/very severe and 73 mild/moderate asthmatic exacerbation. Frequent using of short-acting beta(2)-agonist (OR= 1.5, 95% CI= 1.08-5.3, p= 0.003), noncompliance with medication (OR= 3.6, 95% CI= 1.3-9.9, p= 0.013), previous severe asthmatic exacerbation (OR= 3.8, 95% CI= 1.48-10.01, p= 0.005) and recent admission to hospital (OR= 2.9, 95% CI= 1.07-8.09, p= 0.037) were found to be predictive factors for severe asthmatic exacerbation. Different predictive factors, in particular frequent using of short-acting beta(2)-agonist and noncompliance with medication may be associated with severe asthma exacerbations compared to milder exacerbations. This suggests different mechanisms are responsible for severity of asthma exacerbation.

Fibroblast Growth Factor-23 Concentration in Dogs with Chronic Kidney Disease.

PubMed

Harjes, L M; Parker, V J; Dembek, K; Young, G S; Giovaninni, L H; Kogika, M M; Chew, D J; Toribio, R E

2017-05-01

Chronic kidney disease (CKD) is associated with hyperphosphatemia, decreased vitamin D metabolite concentrations, and hyperparathyroidism. This syndrome is known as CKD-mineral bone disorder (CKD-MBD). Recently, it has been shown that an increase in fibroblast growth factor-23 (FGF-23) concentration is an early biomarker of CKD in people. It is an independent risk factor for both progression of renal disease and survival time in humans and cats with CKD. Information about FGF-23 in healthy dogs and those with CKD is lacking. To measure FGF-23 concentration in dogs with different stages of CKD and determine its association with factors involved in CKD-MBD, including serum phosphorus and parathyroid hormone (PTH) concentrations. A secondary aim was to validate an ELISA for measurement of plasma FGF-23 concentration in dogs. Thirty-two client-owned dogs with naturally occurring CKD and 10 healthy control dogs. Prospective cross-sectional study. An FGF-23 ELISA was used to measure plasma FGF-23 concentration in dogs and their association with serum creatinine, phosphorus, calcium, and PTH concentrations. Plasma FGF-23 concentrations increased with severity of CKD and were significantly different between IRIS stages 1 and 2 versus stages 3 and 4 (P < .0001). Increases in FGF-23 concentrations were more frequent than hyperparathyroidism or hyperphosphatemia in this cohort. Serum creatinine and phosphorus concentrations were the strongest independent predictors of FGF-23 concentration. Plasma FGF-23 concentrations increase in dogs with CKD as disease progresses. Plasma FGF-23 concentrations appear to be useful for further study of the pathophysiology of CKD-MBD in dogs. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

[Serum PTH levels as a predictive factor of hypocalcaemia after total thyroidectomy].

PubMed

Díez Alonso, Manuel; Sánchez López, José Daniel; Sánchez-Seco Peña, María Isabel; Ratia Jiménez, Tomás; Arribas Gómez, Ignacio; Rodríguez Pascual, Angel; Martín-Duce, Antonio; Guadalix Hidalgo, Gregorio; Hernández Domínguez, Sara; Granell Vicent, Javier

2009-02-01

Postoperative parathyroid hormone (PTH) levels as a predictor of hypocalcaemia in patients subjected to total thyroidectomy is analyzed. Prospective study involving 67 patients who underwent total thyroidectomy due to a benign disease. Serum PTH and ionised calcium were measured 20 h after surgery. Sensitivity, specificity and predictive values of PTH and ionised calcium levels were calculated to predict clinical and analytical hypocalcaemia. A total of 42 (62.7%) patients developed hypocalcaemia (ionised calcium<0.95 mmol/l), but only 20 (29.9%) presented with symptoms. PTH concentration the day after surgery was significantly lower in the group that developed symptomatic hypocalcaemia (5.57+/-6.4 pg/ml) than in the asymptomatic (21.5+/-15.3 pg/ml) or normocalcaemic (26.8+/-24.9 pg/ml) groups (p=0.001). Taking the value of 13 pg/ml as a cut-off point of PTH levels, sensitivity, specificity, positive predictive value and negative predictive value were 54%, 72%, 76% and 48%, respectively. On the other hand, sensitivity for predicting symptomatic hypocalcaemia was 95% and specificity was 76%. The test showed a high incidence of false positives (11/30, 36%). Negative predictive value was 97% and positive predictive value was 65%. In multivariate analysis, PTH and ionised calcium were the only perioperative factors that showed an independent predictive value as risk indicators of symptomatic hypocalcaemia. Normal PTH levels 20 h after surgery practically rule out the subsequent appearance of hypocalcaemia symptoms. On the other hand, low PTH levels are not necessarily associated to symptomatic hypocalcaemia due to the high number of false positives.

Use of extant kinetic parameters to predict effluent concentrations of specific organic compounds at full-scale facilities.

PubMed

Ellis, Timothy G; Eliosov, Boris

2004-01-01

To use the results of kinetic tests to predict effluent concentrations of specific contaminants in activated sludge systems, the fraction of the biomass that has an ability to degrade the test compound (i.e., competent biomass) must be estimated. A calibration procedure was developed to assess the competent biomass concentration because the chemical oxygen demand (COD) fraction tended to underestimate the degrading fraction for three of the four test compounds. Acetone, for instance, had a measured influent COD fraction of 0.08%, and the actual competent fraction was estimated to be 2.3%, based on the model calibration. Once the competent biomass fraction in the mixed liquor was determined, the extant kinetic parameters were subsequently used to predict activated sludge system performance. Predicted effluent concentrations were within 2, 5, and 16% of the average measured concentrations for acetone, linear alkylbenzene sulfonate, and furfural, respectively. Day-to-day predictions for these compounds were less accurate, possibly because of the non-steady-state nature of the activated sludge systems studied. The difference between the fraction of the influent COD contributed by the target compounds and the competent biomass fraction in the mixed liquor was found to be more significant when the target compound contributed less than 1% of the influent organic matter. The chemical structure of the target compound and chemical composition of the influent likely had an effect on the resulting competent biomass concentration. The total maximum growth rate, microX, was observed to be independent of the influent concentration of acetone and furfural, thus suggesting that the competent biomass concentration for these compounds was not affected by the changes in their influent concentrations. Consequently, a majority of competent biomass growth resulted from the degradation of other substrates, resulting in a competent biomass concentration significantly higher than predicted

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting

Predictive factors for cosmetic surgery: a hospital-based investigation.

PubMed

Li, Jun; Li, Qian; Zhou, Bei; Gao, Yanli; Ma, Jiehua; Li, Jingyun

2016-01-01

Cosmetic surgery is becoming increasingly popular in China. However, reports on the predictive factors for cosmetic surgery in Chinese individuals are scarce in the literature. We retrospectively analyzed 4550 cosmetic surgeries performed from January 2010 to December 2014 at a single center in China. Data collection included patient demographics and type of cosmetic surgery. Predictive factors were age, sex, marital status, occupational status, educational degree, and having had children. Predictive factors for the three major cosmetic surgeries were determined using a logistic regression analysis. Patients aged 19-34 years accounted for the most popular surgical procedures (76.9 %). The most commonly requested procedures were eye surgery, Botox injection, and nevus removal. Logistic regression analysis showed that higher education level (college, P = 0.01, OR 1.21) was predictive for eye surgery. Age (19-34 years, P = 0.00, OR 33.39; 35-50, P = 0.00, OR 31.34; ≥51, P = 0.00, OR 16.42), female sex (P = 0.00, OR 9.19), employment (service occupations, P = 0.00, OR 2.31; non-service occupations, P = 0.00, OR 1.76), and higher education level (college, P = 0.00, OR 1.39) were independent predictive factors for Botox injection. Married status (P = 0.00, OR 1.57), employment (non-service occupations, P = 0.00, OR 1.50), higher education level (masters, P = 0.00, OR 6.61), and having children (P = 0.00, OR 1.45) were independent predictive factors for nevus removal. The principal three cosmetic surgeries (eye surgery, Botox injection, and nevus removal) were associated with multiple variables. Patients employed in non-service occupations were more inclined to undergo Botox injection and nevus removal. Cohort study, Level III.

Explicit solution of integrated 1 - exp equation for predicting accumulation and decline of concentrations for drugs obeying nonlinear saturation kinetics.

PubMed

Keller, Frieder; Hartmann, Bertram; Czock, David

2009-12-01

To describe nonlinear, saturable pharmacokinetics, the Michaelis-Menten equation is frequently used. However, the Michaelis-Menten equation has no integrated solution for concentrations but only for the time factor. Application of the Lambert W function was proposed recently to obtain an integrated solution of the Michaelis-Menten equation. As an alternative to the Michaelis-Menten equation, a 1 - exp equation has been used to describe saturable kinetics, with the advantage that the integrated 1 - exp equation has an explicit solution for concentrations. We used the integrated 1 - exp equation to predict the accumulation kinetics and the nonlinear concentration decline for a proposed fictive drug. In agreement with the recently proposed method, we found that for the integrated 1 - exp equation no steady state is obtained if the maximum rate of change in concentrations (Vmax) within interval (Tau) is less than the difference between peak and trough concentrations (Vmax x Tau < C peak - C trough).

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Effects of user puff topography, device voltage, and liquid nicotine concentration on electronic cigarette nicotine yield: measurements and model predictions.

PubMed

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat; Shihadeh, Alan

2015-02-01

Some electronic cigarette (ECIG) users attain tobacco cigarette-like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3-5.2 V or 3.0-7.5 W) and e-liquid nicotine concentration (18-36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R (2) = 0.99). Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Towards a better prediction of peak concentration, volume of distribution and half-life after oral drug administration in man, using allometry.

PubMed

Sinha, Vikash K; Vaarties, Karin; De Buck, Stefan S; Fenu, Luca A; Nijsen, Marjoleen; Gilissen, Ron A H J; Sanderson, Wendy; Van Uytsel, Kelly; Hoeben, Eva; Van Peer, Achiel; Mackie, Claire E; Smit, Johan W

2011-05-01

It is imperative that new drugs demonstrate adequate pharmacokinetic properties, allowing an optimal safety margin and convenient dosing regimens in clinical practice, which then lead to better patient compliance. Such pharmacokinetic properties include suitable peak (maximum) plasma drug concentration (C(max)), area under the plasma concentration-time curve (AUC) and a suitable half-life (t(½)). The C(max) and t(½) following oral drug administration are functions of the oral clearance (CL/F) and apparent volume of distribution during the terminal phase by the oral route (V(z)/F), each of which may be predicted and combined to estimate C(max) and t(½). Allometric scaling is a widely used methodology in the pharmaceutical industry to predict human pharmacokinetic parameters such as clearance and volume of distribution. In our previous published work, we have evaluated the use of allometry for prediction of CL/F and AUC. In this paper we describe the evaluation of different allometric scaling approaches for the prediction of C(max), V(z)/F and t(½) after oral drug administration in man. Twenty-nine compounds developed at Janssen Research and Development (a division of Janssen Pharmaceutica NV), covering a wide range of physicochemical and pharmacokinetic properties, were selected. The C(max) following oral dosing of a compound was predicted using (i) simple allometry alone; (ii) simple allometry along with correction factors such as plasma protein binding (PPB), maximum life-span potential or brain weight (reverse rule of exponents, unbound C(max) approach); and (iii) an indirect approach using allometrically predicted CL/F and V(z)/F and absorption rate constant (k(a)). The k(a) was estimated from (i) in vivo pharmacokinetic experiments in preclinical species; and (ii) predicted effective permeability in man (P(eff)), using a Caco-2 permeability assay. The V(z)/F was predicted using allometric scaling with or without PPB correction. The t(½) was estimated from

Factors Affecting Lactoferrin Concentration in Human Milk: How Much Do We Know?

PubMed Central

Villavicencio, Aasith; Rueda, Maria S.; Turin, Christie G.; Ochoa, Theresa J.

2017-01-01

Lactoferrin (LF) is a breast milk glycoprotein with antimicrobial and anti-inflammatory effects. Its beneficial properties in infants, especially in those born preterm, are currently being studied in clinical trials. However, the maternal and nursing infant factors that may affect the concentration of LF in breast milk are still not clear. We conducted a systematic review to investigate the factors that may affect LF concentration. We used a 2-step approach to identify the eligible studies according to inclusion/exclusion criteria and to determine which studies would be considered. We included 70 qualified articles from 29 countries with publication dates ranging from 1976 to 2015. We described the correlation between LF concentration in breast milk and lactation stage; 10 maternal factors, such as race, parity, among others; and 2 infant factors, infections and prematurity. Colostrum has the highest LF levels, but they decrease with days postpartum. No other factor has been consistently associated with LF concentration. A major limitation of the majority of the published studies is the small sample size and the different methods used to measure LF concentration. Therefore, there is a need for large, multicenter studies with standardized study design, sample collection, and LF measurement methods to identify clinically significant factors associated with LF expression in breast milk, which will help promote exclusive breastfeeding in preterm infants. PMID:28075610

Variability of the soil-to-plant radiocaesium transfer factor for Japanese soils predicted with soil and plant properties.

PubMed

Uematsu, Shinichiro; Vandenhove, Hildegarde; Sweeck, Lieve; Van Hees, May; Wannijn, Jean; Smolders, Erik

2016-03-01

Food chain contamination with radiocaesium (RCs) in the aftermath of the Fukushima accident calls for an analysis of the specific factors that control the RCs transfer. Here, soil-to-plant transfer factors (TF) of RCs for grass were predicted from the potassium concentration in soil solution (mK) and the Radiocaesium Interception Potential (RIP) of the soil using existing mechanistic models. The mK and RIP were (a) either measured for 37 topsoils collected from the Fukushima accident affected area or (b) predicted from the soil clay content and the soil exchangeable potassium content using the models that had been calibrated for European soils. An average ammonium concentration was used throughout in the prediction. The measured RIP ranged 14-fold and measured mK varied 37-fold among the soils. The measured RIP was lower than the RIP predicted from the soil clay content likely due to the lower content of weathered micas in the clay fraction of Japanese soils. Also the measured mK was lower than that predicted. As a result, the predicted TFs relying on the measured RIP and mK were, on average, about 22-fold larger than the TFs predicted using the European calibrated models. The geometric mean of the measured TFs for grass in the affected area (N = 82) was in the middle of both. The TFs were poorly related to soil classification classes, likely because soil fertility (mK) was obscuring the effects of the soil classification related to the soil mineralogy (RIP). This study suggests that, on average, Japanese soils are more vulnerable than European soils at equal soil clay and exchangeable K content. The affected regions will be targeted for refined model validation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predictive factors of bacterial meningitis in the patients seen in emergency departments.

PubMed

Morales-Casado, María Isabel; Julián-Jiménez, Agustín; Lobato-Casado, Paula; Cámara-Marín, Belén; Pérez-Matos, Julio Alberto; Martínez-Maroto, Tamara

2017-04-01

To analyse and compare predictive factors of bacterial meningitis in the patients seen in the Emergency Departments (ED) due to an episode of acute meningitis (AM). A prospective, observational study was carried out in patients aged 15 years and older seen in ED due to AM between August 2009 and November 2015. Thirty-two variables for predicting bacterial meningitis were assessed. They covered epidemiological, comorbidity, clinical and analytical factors. Multivariate logistic regression analysis was performed. The study included 154 patients. The diagnosis was bacterial meningitis in 53 (34.4%) patients. Four variables were significantly associated with bacterial aetiology: cerebrospinal fluid (CSF) lactate concentration ≥33mg/dl (odds ratio [OR] 50.84; 95% confidence interval [CI]: 21.63-119.47, P<.001), serum procalcitonin (PCT) ≥0.8ng/ml (OR 46.34; 95%CI: 19.71-108.89; P<.001), CSF glucose <60% of blood value (OR 20.82; 95%CI: 8.86-48.96; P=.001), CSF polymorphonuclears greater than 50% (OR 20.19; 95%CI: 8.31-49.09; P=.002]. The area under the curve for the model serum PCT≥0.8ng/ml plus CSF lactate ≥33mg/dl was 0.992 (95%CI: 0.979-1; P<.001), and achieved 99% sensitivity and 98% specificity for predicting bacterial meningitis. Serum PCT with CSF lactate, CSF glucose and CSF polymorphonuclears evaluated in an initial assessment in the ED for patients with AM, achieved an excellent diagnostic usefulness for predicting bacterial meningitis. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

The influence of biological and environmental factors on metallothionein concentration in the blood.

PubMed

Kowalska, Katarzyna; Bizoń, Anna; Zalewska, Marta; Milnerowicz, Halina

2015-01-01

The concentration of metallothionein (MT), a low-molecular-weight protein, is regulated by many factors, primarily metals (zinc, cadmium, copper), cytokines, glucocorticoides and free radicals. These factors are determined by such aspects of human biology as gender, pregnancy and age, as well as by environmental factors including the use of oral contraceptives and cigarette smoking, all which may affect MT levels in the body. The aim of this study was to investigate the influence of these biological and environmental factors on MT concentrations in erythrocyte lysate and in plasma. MT concentrations were determined by a two-step direct enzyme-linked immunosorbent assay. Evaluation of exposure to cigarette smoking was performed by checking cotinine levels in the plasma of subjects. The studies showed higher MT concentrations in both the erythrocyte lysate and plasma of women when compared to men. Furthermore, pregnancy causes an increase of MT concentration in plasma, while oral contraceptives cause an elevated concentration of MT in erythrocyte lysate. Age impacts plasma MT concentrations in men, whereas it does not affect concentrations of MT in erythrocyte lysate. Copyright © 2014 Elsevier GmbH. All rights reserved.

Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

PubMed Central

Zhang, Jiangshe; Ding, Weifu

2017-01-01

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R2 increased and root mean square error values decreased respectively. PMID:28125034

Gene expression analysis in zebrafish embryos: a potential approach to predict effect concentrations in the fish early life stage test.

PubMed

Weil, Mirco; Scholz, Stefan; Zimmer, Michaela; Sacher, Frank; Duis, Karen

2009-09-01

Based on the hypothesis that analysis of gene expression could be used to predict chronic fish toxicity, the zebrafish (Danio rerio) embryo test (DarT), developed as a replacement method for the acute fish test, was expanded to a gene expression D. rerio embryo test (Gene-DarT). The effects of 14 substances on lethal and sublethal endpoints of the DarT and on expression of potential marker genes were investigated: the aryl hydrocarbon receptor 2, cytochrome P450 1A (cypla), heat shock protein 70, fizzy-related protein 1, the transcription factors v-maf musculoaponeurotic fibrosarcoma oncogene family protein g (avian) 1 and NF-E2-p45-related factor, and heme oxygenase 1 (hmox1). After exposure of zebrafish embryos for 48 h, differential gene expression was evaluated using reverse transcriptase-polymerase chain reaction, gel electrophoresis, and densitometric analysis of the gels. All tested compounds significantly affected the expression of at least one potential marker gene, with cyp1a and hmox1 being most sensitive. Lowest-observed-effect concentrations (LOECs) for gene expression were below concentrations resulting in 10% lethal effects in the DarT. For 10 (3,4- and 3,5-dichloroaniline, 1,4-dichlorobenzene, 2,4-dinitrophenol, atrazine, parathion-ethyl, chlorotoluron, genistein, 4-nitroquinoline-1-oxide, and cadmium) out of the 14 tested substances, LOEC values derived with the Gene-DarT differ by a factor of less than 10 from LOEC values of fish early life stage tests with zebrafish. For pentachloroaniline and pentachlorobenzene, the Gene-DarT showed a 23- and 153-fold higher sensitivity, respectively, while for lindane, it showed a 13-fold lower sensitivity. For ivermectin, the Gene-DarT was by a factor of more than 1,000 less sensitive than the acute fish test. The results of the present study indicate that gene expression analysis in zebrafish embryos could principally be used to predict effect concentrations in the fish early life stage test.

Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

PubMed

Collins, Sarah M; Oliver, Samantha K; Lapierre, Jean-Francois; Stanley, Emily H; Jones, John R; Wagner, Tyler; Soranno, Patricia A

2017-07-01

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

[Application of artificial neural networks on the prediction of surface ozone concentrations].

PubMed

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.

Decreased Plasma Brain-Derived Neurotrophic Factor and Vascular Endothelial Growth Factor Concentrations during Military Training

PubMed Central

Nibuya, Masashi; Ishida, Toru; Yamamoto, Tetsuo; Mukai, Yasuo; Mitani, Keiji; Tsumatori, Gentaro; Scott, Daniel; Shimizu, Kunio

2014-01-01

Decreased concentrations of plasma brain-derived neurotrophic factor (BDNF) and serum BDNF have been proposed to be a state marker of depression and a biological indicator of loaded psychosocial stress. Stress evaluations of participants in military mission are critically important and appropriate objective biological parameters that evaluate stress are needed. In military circumstances, there are several problems to adopt plasma BDNF concentration as a stress biomarker. First, in addition to psychosocial stress, military missions inevitably involve physical exercise that increases plasma BDNF concentrations. Second, most participants in the mission do not have adequate quality or quantity of sleep, and sleep deprivation has also been reported to increase plasma BDNF concentration. We evaluated plasma BDNF concentrations in 52 participants on a 9-week military mission. The present study revealed that plasma BDNF concentration significantly decreased despite elevated serum enzymes that escaped from muscle and decreased quantity and quality of sleep, as detected by a wearable watch-type sensor. In addition, we observed a significant decrease in plasma vascular endothelial growth factor (VEGF) during the mission. VEGF is also neurotrophic and its expression in the brain has been reported to be up-regulated by antidepressive treatments and down-regulated by stress. This is the first report of decreased plasma VEGF concentrations by stress. We conclude that decreased plasma concentrations of neurotrophins can be candidates for mental stress indicators in actual stressful environments that include physical exercise and limited sleep. PMID:24586790

Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

USGS Publications Warehouse

Francy, Donna S.; Graham, Jennifer L.; Stelzer, Erin A.; Ecker, Christopher D.; Brady, Amie M. G.; Pam Struffolino,; Loftin, Keith A.

2015-11-06

The results of this study showed that water-quality and environmental variables are promising for use in site-specific daily or long-term predictive models. In order to develop more accurate models to predict toxin concentrations at freshwater lake sites, data need to be collected more frequently and for consecutive days in future studies.

Concentration of platelets and growth factors in platelet-rich plasma from Goettingen minipigs.

PubMed

Jungbluth, Pascal; Grassmann, Jan-Peter; Thelen, Simon; Wild, Michael; Sager, Martin; Windolf, Joachim; Hakimi, Mohssen

2014-01-01

In minipigs little is known about the concentration of growth factors in plasma, despite their major role in several patho-physiological processes such as healing of fractures. This prompted us to study the concentration of platelets and selected growth factors in plasma and platelet-rich plasma (PRP) preparation of sixteen Goettingen minipigs. Platelet concentrations increased significantly in PRP in comparison to native blood plasma. Generally, significant increase in the concentration of all growth factors tested was observed in the PRP in comparison to the corresponding plasma or serum. Five of the plasma samples examined contained detectable levels of bone morphogenic protein 2 (BMP-2) whereas eleven of the plasma or serum samples contained minimal amounts of vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF-bb) respectively. On the other hand variable concentrations of bone morphogenic protein 7 (BMP-7) and transforming growth factor β1 (TGF-β1) were measured in all plasma samples. In contrast, all PRP samples contained significantly increased amounts of growth factors. The level of BMP-2, BMP-7, TGF-β1, VEGF and PDGF-bb increased by 17.6, 1.5, 7.1, 7.2 and 103.3 fold, in comparison to the corresponding non-enriched preparations. Moreover significant positive correlations were found between platelet count and the concentrations of BMP-2 (r=0.62, p<0.001), TGF-β1 (r=0.85, p<0.001), VEGF (r=0.46, p<0.01) and PDGF-bb (r=0.9, p<0.001). Our results demonstrate that selected growth factors are present in the platelet-rich plasma of minipigs which might thus serve as a source of autologous growth factors.

Cerebrospinal fluid and plasma oxytocin concentrations are positively correlated and negatively predict anxiety in children.

PubMed

Carson, D S; Berquist, S W; Trujillo, T H; Garner, J P; Hannah, S L; Hyde, S A; Sumiyoshi, R D; Jackson, L P; Moss, J K; Strehlow, M C; Cheshier, S H; Partap, S; Hardan, A Y; Parker, K J

2015-09-01

The neuropeptide oxytocin (OXT) exerts anxiolytic and prosocial effects in the central nervous system of rodents. A number of recent studies have attempted to translate these findings by investigating the relationships between peripheral (e.g., blood, urinary and salivary) OXT concentrations and behavioral functioning in humans. Although peripheral samples are easy to obtain in humans, whether peripheral OXT measures are functionally related to central OXT activity remains unclear. To investigate a possible relationship, we quantified OXT concentrations in concomitantly collected cerebrospinal fluid (CSF) and blood samples from child and adult patients undergoing clinically indicated lumbar punctures or other CSF-related procedures. Anxiety scores were obtained in a subset of child participants whose parents completed psychometric assessments. Findings from this study indicate that plasma OXT concentrations significantly and positively predict CSF OXT concentrations (r=0.56, P=0.0064, N=27). Moreover, both plasma (r=-0.92, P=0.0262, N=10) and CSF (r=-0.91, P=0.0335, N=10) OXT concentrations significantly and negatively predicted trait anxiety scores, consistent with the preclinical literature. Importantly, plasma OXT concentrations significantly and positively (r=0.96, P=0.0115, N=10) predicted CSF OXT concentrations in the subset of child participants who provided behavioral data. This study provides the first empirical support for the use of blood measures of OXT as a surrogate for central OXT activity, validated in the context of behavioral functioning. These preliminary findings also suggest that impaired OXT signaling may be a biomarker of anxiety in humans, and a potential target for therapeutic development in individuals with anxiety disorders.

Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

NASA Astrophysics Data System (ADS)

Zhan, Yu; Luo, Yuzhou; Deng, Xunfei; Chen, Huajin; Grieneisen, Michael L.; Shen, Xueyou; Zhu, Lizhong; Zhang, Minghua

2017-04-01

A high degree of uncertainty associated with the emission inventory for China tends to degrade the performance of chemical transport models in predicting PM2.5 concentrations especially on a daily basis. In this study a novel machine learning algorithm, Geographically-Weighted Gradient Boosting Machine (GW-GBM), was developed by improving GBM through building spatial smoothing kernels to weigh the loss function. This modification addressed the spatial nonstationarity of the relationships between PM2.5 concentrations and predictor variables such as aerosol optical depth (AOD) and meteorological conditions. GW-GBM also overcame the estimation bias of PM2.5 concentrations due to missing AOD retrievals, and thus potentially improved subsequent exposure analyses. GW-GBM showed good performance in predicting daily PM2.5 concentrations (R2 = 0.76, RMSE = 23.0 μg/m3) even with partially missing AOD data, which was better than the original GBM model (R2 = 0.71, RMSE = 25.3 μg/m3). On the basis of the continuous spatiotemporal prediction of PM2.5 concentrations, it was predicted that 95% of the population lived in areas where the estimated annual mean PM2.5 concentration was higher than 35 μg/m3, and 45% of the population was exposed to PM2.5 >75 μg/m3 for over 100 days in 2014. GW-GBM accurately predicted continuous daily PM2.5 concentrations in China for assessing acute human health effects.

Limited sampling strategies to predict the area under the concentration-time curve for rifampicin.

PubMed

Medellín-Garibay, Susanna E; Correa-López, Tania; Romero-Méndez, Carmen; Milán-Segovia, Rosa C; Romano-Moreno, Silvia

2014-12-01

Rifampicin (RMP) is the most effective first-line antituberculosis drug. One of the most critical aspects of using it in fixed-drug combination formulations is to ensure it reaches therapeutic levels in blood. The determination of the area under the concentration-time curve (AUC) and appropriate dose adjustment of this drug may contribute to optimization of therapy. Even when the maximal concentration (Cmax) of RMP also predicts its sterilizing effect, the time to reach it (Tmax) takes 40 minutes to 6 hours. The aim of this study was to develop a limited sampling strategy (LSS) for therapeutic drug monitoring assistance for RMP. Full concentration-time curves were obtained from 58 patients with tuberculosis (TB) after the oral administration of RMP in fixed-drug combination formulation. A validated high-performance liquid chromatographic method was used. Pharmacokinetic parameters were estimated with a noncompartmental model. Generalized linear models were obtained by forward steps, and bootstrapping was performed to develop LSS to predict AUC curve from time 0 to the last measured at 24 hours postdose (AUC0-24). The predictive performance of the proposed models was assessed using RMP profiles from 25 other TB patients by comparing predicted and observed AUC0-24. The mean AUC0-24 in the current study was 91.46 ± 36.7 mg·h·L, and the most convenient sampling time points to predict it were 2, 4 and 12 hours postdose (slope [m] = 0.955 ± 0.06; r = 0.92). The mean prediction error was -0.355%, and the root mean square error was 5.6% in the validation group. Alternate LSSs are proposed with 2 of these sampling time points, which also provide good predictions when the 3 most convenient are not feasible. The AUC0-24 for RMP in TB patients can be predicted with acceptable precision through a 2- or 3-point sampling strategy, despite wide interindividual variability. These LSSs could be applied in clinical practice to optimize anti-TB therapy based on therapeutic drug

Concentration-dependent polyparameter linear free energy relationships to predict organic compound sorption on carbon nanotubes

PubMed Central

Zhao, Qing; Yang, Kun; Li, Wei; Xing, Baoshan

2014-01-01

Adsorption of organic compounds on carbon nanotubes (CNTs), governed by interactions between molecules and CNTs surfaces, is critical for their fate, transport, bioavailability and toxicity in the environment. Here, we report a promising concentration-dependent polyparameter linear free energy relationships (pp-LFERs) model to describe the compound-CNTs interactions and to predict sorption behavior of chemicals on CNTs in a wide range of concentrations (over five orders of magnitude). The developed pp-LFERs are able to capture the dependence of the ki on equilibrium concentration. The pp-LFERs indexes [r, p, a, b, v] representing different interactions are found to have a good relationship with the aqueous equilibrium concentrations of compounds. This modified model can successfully interpret the relative contribution of each interaction at a given concentration and reliably predict sorption of various chemicals on CNTs. This approach is expected to help develop a better environmental fate and risk assessment model. PMID:24463462

[Predictive factors of mortality in extremely preterm infants].

PubMed

Lin, L; Fang, M C; Jiang, H; Zhu, M L; Chen, S Q; Lin, Z L

2018-04-02

Objective: To investigate the predictive factors of mortality in extremely preterm infants. Methods: The retrospective case-control study was accomplished in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. A total of 268 extremely preterm infants seen from January 1, 1999 to December 31, 2015 were divided into survival group (192 cases) and death group (76 cases). The potential predictive factors of mortality were identified by univariate analysis, and then analyzed by multivariate unconditional Logistic regression analysis. The mortality and predictive factors were also compared between two time periods, which were January 1, 1999 to December 31, 2007 (65 cases) and January 1, 2008 to December 31, 2015 (203 cases). Results: The median gestational age (GA) of extremely preterm infants was 27 weeks (23 +3 -27 +6 weeks). The mortality was higher in infants with GA of 25-<26 weeks ( OR= 2.659, 95% CI: 1.211-5.840) and<25 weeks ( OR= 10.029, 95% CI: 3.266-30.792) compared to that in infants with GA> 26 weeks. From January 1, 2008 to December 31, 2015, the number of extremely preterm infants was increased significantly compared to the previous 9 years, while the mortality decreased significantly ( OR= 0.490, 95% CI: 0.272-0.884). Multivariate unconditional Logistic regression analysis showed that GA below 25 weeks ( OR= 6.033, 95% CI: 1.393-26.133), lower birth weight ( OR= 0.997, 95% CI: 0.995-1.000), stage Ⅲ necrotizing enterocolitis (NEC) ( OR= 15.907, 95% CI: 3.613-70.033), grade Ⅰ and Ⅱ intraventricular hemorrhage (IVH) ( OR= 0.260, 95% CI: 0.117-0.575) and dependence on invasive mechanical ventilation ( OR= 3.630, 95% CI: 1.111-11.867) were predictive factors of mortality in extremely preterm infants. Conclusions: GA below 25 weeks, lower birth weight, stage Ⅲ NEC and dependence on invasive mechanical ventilation are risk factors of mortality in extremely preterm infants. But grade ⅠandⅡ IVH is protective

Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance

USGS Publications Warehouse

Gutreuter, S.; Boogaard, M.A.

2007-01-01

Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2???,5???-dichloro-4???- nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4- nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. ?? 2007 SETAC.

Predicting indoor pollutant concentrations, and applications to air quality management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lorenzetti, David M.

Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptomsmore » such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.« less

Examining Factors Predicting Students' Digital Competence

ERIC Educational Resources Information Center

Hatlevik, Ove Edvard; Guðmundsdóttir, Gréta Björk; Loi, Massimo

2015-01-01

The purpose of this study was to examine factors predicting lower secondary school students' digital competence and to explore differences between students when it comes to digital competence. Results from a digital competence test and survey in lower secondary school will be presented. It is important to learn more about and investigate what…

Diet and metabolic state are the main factors determining concentrations of perfluoroalkyl substances in female polar bears from Svalbard.

PubMed

Tartu, Sabrina; Bourgeon, Sophie; Aars, Jon; Andersen, Magnus; Lone, Karen; Jenssen, Bjørn Munro; Polder, Anuschka; Thiemann, Gregory W; Torget, Vidar; Welker, Jeffrey M; Routti, Heli

2017-10-01

Perfluoroalkyl substances (PFASs) have been detected in organisms worldwide, including Polar Regions. The polar bear (Ursus maritimus), the top predator of Arctic marine ecosystems, accumulates high concentrations of PFASs, which may be harmful to their health. The aim of this study was to investigate which factors (habitat quality, season, year, diet, metabolic state [i.e. feeding/fasting], breeding status and age) predict PFAS concentrations in female polar bears captured on Svalbard (Norway). We analysed two perfluoroalkyl sulfonates (PFSAs: PFHxS and PFOS) and C 8 -C 13 perfluoroalkyl carboxylates (PFCAs) in 112 plasma samples obtained in April and September 2012-2013. Nitrogen and carbon stable isotope ratios (δ 15 N, δ 13 C) in red blood cells and plasma, and fatty acid profiles in adipose tissue were used as proxies for diet. We determined habitat quality based on movement patterns, capture position and resource selection functions, which are models that predict the probability of use of a resource unit. Plasma urea to creatinine ratios were used as proxies for metabolic state (i.e. feeding or fasting state). Results were obtained from a conditional model averaging of 42 general linear mixed models. Diet was the most important predictor of PFAS concentrations. PFAS concentrations were positively related to trophic level and marine diet input. High PFAS concentrations in females feeding on the eastern part of Svalbard, where the habitat quality was higher than on the western coast, were likely related to diet and possibly to abiotic factors. Concentrations of PFSAs and C 8 -C 10 PFCAs were higher in fasting than in feeding polar bears and PFOS was higher in females with cubs of the year than in solitary females. Our findings suggest that female polar bears that are exposed to the highest levels of PFAS are those 1) feeding on high trophic level sea ice-associated prey, 2) fasting and 3) with small cubs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting heavy metal concentrations in soils and plants using field spectrophotometry

NASA Astrophysics Data System (ADS)

Muradyan, V.; Tepanosyan, G.; Asmaryan, Sh.; Sahakyan, L.; Saghatelyan, A.; Warner, T. A.

2017-09-01

Aim of this study is to predict heavy metal (HM) concentrations in soils and plants using field remote sensing methods. The studied sites were an industrial town of Kajaran and city of Yerevan. The research also included sampling of soils and leaves of two tree species exposed to different pollution levels and determination of contents of HM in lab conditions. The obtained spectral values were then collated with contents of HM in Kajaran soils and the tree leaves sampled in Yerevan, and statistical analysis was done. Consequently, Zn and Pb have a negative correlation coefficient (p <0.01) in a 2498 nm spectral range for soils. Pb has a significantly higher correlation at red edge for plants. A regression models and artificial neural network (ANN) for HM prediction were developed. Good results were obtained for the best stress sensitive spectral band ANN (R2 0.9, RPD 2.0), Simple Linear Regression (SLR) and Partial Least Squares Regression (PLSR) (R2 0.7, RPD 1.4) models. Multiple Linear Regression (MLR) model was not applicable to predict Pb and Zn concentrations in soils in this research. Almost all full spectrum PLS models provide good calibration and validation results (RPD>1.4). Full spectrum ANN models are characterized by excellent calibration R2, rRMSE and RPD (0.9; 0.1 and >2.5 respectively). For prediction of Pb and Ni contents in plants SLR and PLS models were used. The latter provide almost the same results. Our findings indicate that it is possible to make coarse direct estimation of HM content in soils and plants using rapid and economic reflectance spectroscopy.

Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

PubMed Central

Sorbe, Bengt

2012-01-01

Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

Predictive factors for work capacity in patients with musculoskeletal disorders.

PubMed

Lydell, Marie; Baigi, Amir; Marklund, Bertil; Månsson, Jörgen

2005-09-01

To identify predictive factors for work capacity in patients with musculoskeletal disorders. A descriptive, evaluative, quantitative study. The study was based on 385 patients who participated in a rehabilitation programme. Patients were divided into 2 groups depending on their ability to work. The groups were compared with each other with regard to sociodemographic factors, diagnoses, disability pension and number of sick days. The patient's level of exercise habits, ability to undertake activities, physical capacity, pain and quality of life were compared further using logistic regression analysis. Predictive factors for work capacity, such as ability to undertake activities, quality of life and fitness on exercise, were identified as important independent factors. Other well-known factors, i.e. gender, age, education, pain and earlier sickness certification periods, were also identified. Factors that were not significantly different between the groups were employment status, profession, diagnosis and levels of exercise habits. Identifying predictors for ability to return to work is an essential task for deciding on suitable individual rehabilitation. This study identified new predictive factors, such as ability to undertake activities, quality of life and fitness on exercise.

Genetic factors affecting statin concentrations and subsequent myopathy: a HuGENet systematic review

PubMed Central

Canestaro, William J.; Austin, Melissa A.; Thummel, Kenneth E.

2015-01-01

Statins, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitors, have proven efficacy in both lowering low-density-lipoprotein levels and preventing major coronary events, making them one of the most commonly prescribed drugs in the United States. Statins exhibit a class-wide side effect of muscle toxicity and weakness, which has led regulators to impose both dosage limitations and a recall. This review focuses on the best-characterized genetic factors associated with increased statin muscle concentrations, including the genes encoding cytochrome P450 enzymes (CYP2D6, CYP3A4, and CYP3A5), a mitochondrial enzyme (GATM), an influx transporter (SLCO1B1), and efflux transporters (ABCB1 and ABCG2). A systematic literature review was conducted to identify relevant research evaluating the significance of genetic variants predictive of altered statin concentrations and subsequent statin-related myopathy. Studies eligible for inclusion must have incorporated genotype information and must have associated it with some measure of myopathy, either creatine kinase levels or self-reported muscle aches and pains. After an initial review, focus was placed on seven genes that were adequately characterized to provide a substantive review: CYP2D6, CYP3A4, CYP3A5, GATM, SLCO1B1, ABCB1, and ABCG2. All statins were included in this review. Among the genetic factors evaluated, statin-related myopathy appears to be most strongly associated with variants in SLCO1B1. PMID:24810685

Predictive factors for intrauterine growth restriction.

PubMed

Albu, A R; Anca, A F; Horhoianu, V V; Horhoianu, I A

2014-06-15

Reduced fetal growth is seen in about 10% of the pregnancies but only a minority has a pathological background and is known as intrauterine growth restriction or fetal growth restriction (IUGR / FGR). Increased fetal and neonatal mortality and morbidity as well as adult pathologic conditions are often associated to IUGR. Risk factors for IUGR are easy to assess but have poor predictive value. For the diagnostic purpose, biochemical serum markers, ultrasound and Doppler study of uterine and spiral arteries, placental volume and vascularization, first trimester growth pattern are object of assessment today. Modern evaluations propose combined algorithms using these strategies, all with the goal of a better prediction of risk pregnancies.

Compilation of selected marine radioecological data for the US Subseabed Program: Summaries of available radioecological concentration factors and biological half-lives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gomez, L.S.; Marietta, M.G.; Jackson, D.W.

1987-04-01

The US Subseabed Disposal Program has compiled an extensive concentration factor and biological half-life data base from the international marine radioecological literature. A microcomputer-based data management system has been implemented to provide statistical and graphic summaries of these data. The data base is constructed in a manner which allows subsets to be sorted using a number of interstudy variables such as organism category, tissue/organ category, geographic location (for in situ studies), and several laboratory-related conditions (e.g., exposure time and exposure concentration). This report updates earlier reviews and provides summaries of the tabulated data. In addition to the concentration factor/biological half-lifemore » data base, we provide an outline of other published marine radioecological works. Our goal is to present these data in a form that enables those concerned with predictive assessment of radiation dose in the marine environment to make a more judicious selection of data for a given application. 555 refs., 19 figs., 7 tabs.« less

Prediction of local concentration statistics in variably saturated soils: Influence of observation scale and comparison with field data

NASA Astrophysics Data System (ADS)

Graham, Wendy; Destouni, Georgia; Demmy, George; Foussereau, Xavier

1998-07-01

The methodology developed in Destouni and Graham [Destouni, G., Graham, W.D., 1997. The influence of observation method on local concentration statistics in the subsurface. Water Resour. Res. 33 (4) 663-676.] for predicting locally measured concentration statistics for solute transport in heterogeneous porous media under saturated flow conditions is applied to the prediction of conservative nonreactive solute transport in the vadose zone where observations are obtained by soil coring. Exact analytical solutions are developed for both the mean and variance of solute concentrations measured in discrete soil cores using a simplified physical model for vadose-zone flow and solute transport. Theoretical results show that while the ensemble mean concentration is relatively insensitive to the length-scale of the measurement, predictions of the concentration variance are significantly impacted by the sampling interval. Results also show that accounting for vertical heterogeneity in the soil profile results in significantly less spreading in the mean and variance of the measured solute breakthrough curves, indicating that it is important to account for vertical heterogeneity even for relatively small travel distances. Model predictions for both the mean and variance of locally measured solute concentration, based on independently estimated model parameters, agree well with data from a field tracer test conducted in Manatee County, Florida.

Predictive factors for intrauterine growth restriction

PubMed Central

Albu, AR; Anca, AF; Horhoianu, VV; Horhoianu, IA

2014-01-01

Abstract Reduced fetal growth is seen in about 10% of the pregnancies but only a minority has a pathological background and is known as intrauterine growth restriction or fetal growth restriction (IUGR / FGR). Increased fetal and neonatal mortality and morbidity as well as adult pathologic conditions are often associated to IUGR. Risk factors for IUGR are easy to assess but have poor predictive value. For the diagnostic purpose, biochemical serum markers, ultrasound and Doppler study of uterine and spiral arteries, placental volume and vascularization, first trimester growth pattern are object of assessment today. Modern evaluations propose combined algorithms using these strategies, all with the goal of a better prediction of risk pregnancies. Abbreviations: SGA = small for gestational age; IUGR = intrauterine growth restriction; FGR = fetal growth restriction; IUFD = intrauterine fetal demise; HIV = human immunodeficiency virus; PAPP-A = pregnancy associated plasmatic protein A; β-hCG = beta human chorionic gonadotropin; MoM = multiple of median; ADAM-12 = A-disintegrin and metalloprotease 12; PP-13 = placental protein 13; VEGF = vascular endothelial growth factor; PlGF = placental growth factor; sFlt-1 = soluble fms-like tyrosine kinase-1; UAD = uterine arteries Doppler ultrasound; RI = resistence index; PI = pulsatility index; VOCAL = Virtual Organ Computer–Aided Analysis software; VI = vascularization index; FI = flow index; VFI = vascularization flow index; PQ = placental quotient PMID:25408721

Predictive Factors of Anxiety and Depression in Patients with Acute Coronary Syndrome.

PubMed

Altino, Denise Meira; Nogueira-Martins, Luiz Antônio; de Barros, Alba Lucia Bottura Leite; Lopes, Juliana de Lima

2017-12-01

To identify the predictive factors of anxiety and depression in patients with acute coronary syndrome. Cross-sectional and retrospective study conducted with 120 patients hospitalized with acute coronary syndrome. Factors interfering with anxiety and depression were assessed. Anxiety was related to sex, stress, years of education, and depression, while depression was related to sex, diabetes mellitus, obesity, years of education, and trait-anxiety. Obesity and anxiety were considered predictive factors for depression, while depression and fewer years of education were considered predictive factors for anxiety. Copyright © 2017. Published by Elsevier Inc.

Predicting the probability of elevated nitrate concentrations in the Puget Sound Basin: Implications for aquifer susceptibility and vulnerability

USGS Publications Warehouse

Tesoriero, A.J.; Voss, F.D.

1997-01-01

The occurrence and distribution of elevated nitrate concentrations (≥ 3 mg/l) in ground water in the Puget Sound Basin, Washington, were determined by examining existing data from more than 3000 wells. Models that estimate the probability that a well has an elevated nitrate concentration were constructed by relating the occurrence of elevated nitrate concentrations to both natural and anthropogenic variables using logistic regression. The variables that best explain the occurrence of elevated nitrate concentrations were well depth, surficial geology, and the percentage of urban and agricultural land within a radius of 3.2 kilometers of the well. From these relations, logistic regression models were developed to assess aquifer susceptibility (relative ease with which contaminants will reach aquifer) and ground-water vulnerability (relative ease with which contaminants will reach aquifer for a given set of land-use practices). Both models performed well at predicting the probability of elevated nitrate concentrations in an independent data set. This approach to assessing aquifer susceptibility and ground-water vulnerability has the advantages of having both model variables and coefficient values determined on the basis of existing water quality information and does not depend on the assignment of variables and weighting factors based on qualitative criteria.

Prediction of Intensity Change Subsequent to Concentric Eyewall Events

NASA Astrophysics Data System (ADS)

Mauk, Rachel Grant

Concentric eyewall events have been documented numerous times in intense tropical cyclones over the last two decades. During a concentric eyewall event, an outer (secondary) eyewall forms around the inner (primary) eyewall. Improved instrumentation on aircraft and satellites greatly increases the likelihood of detecting an event. Despite the increased ability to detect such events, forecasts of intensity changes during and after these events remain poor. When concentric eyewall events occur near land, accurate intensity change predictions are especially critical to ensure proper emergency preparations and staging of recovery assets. A nineteen-year (1997-2015) database of concentric eyewall events is developed by analyzing microwave satellite imagery, aircraft- and land-based radar, and other published documents. Events are identified in both the North Atlantic and eastern North Pacific basins. TCs are categorized as single (1 event), serial (>= 2 events) and super-serial (>= 3 events). Key findings here include distinct spatial patterns for single and serial Atlantic TCs, a broad seasonal distribution for eastern North Pacific TCs, and apparent ENSO-related variability in both basins. The intensity change subsequent to the concentric eyewall event is calculated from the HURDAT2 database at time points relative to the start and to the end of the event. Intensity change is then categorized as Weaken (≤ -10 kt), Maintain (+/- 5 kt), and Strengthen (≥ 10 kt). Environmental conditions in which each event occurred are analyzed based on the SHIPS diagnostic files. Oceanic, dynamic, thermodynamic, and TC status predictors are selected for testing in a multiple discriminant analysis procedure to determine which variables successfully discriminate the intensity change category and the occurrence of additional concentric eyewall events. Intensity models are created for 12 h, 24 h, 36 h, and 48 h after the concentric eyewall events end. Leave-one-out cross validation is

Prediction and Informative Risk Factor Selection of Bone Diseases.

PubMed

Li, Hui; Li, Xiaoyi; Ramanathan, Murali; Zhang, Aidong

2015-01-01

With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

Predictive factors for complications in children with esophageal atresia and tracheoesophageal fistula.

PubMed

Shah, R; Varjavandi, V; Krishnan, U

2015-04-01

The objective of this study was to describe the incidence of complications in children with esophageal atresia (EA) with or without tracheoesophageal fistula (TEF) at a tertiary pediatric hospital and to identify predictive factors for their occurrence. A retrospective chart review of 110 patients born in or transferred to Sydney Children's Hospital with EA/TEF between January 1999 and December 2010 was done. Univariate and multivariate regression analyses were performed to identify predictive factors for the occurrence of complications in these children. From univariate analysis, early esophageal stricture formation was more likely in children with 'long-gap' EA (odds ratio [OR] = 16.32). Patients with early strictures were more likely to develop chest infections (OR = 3.33). Patients with severe tracheomalacia were more likely to experience 'cyanotic/dying' (OR = 180) and undergo aortopexy (OR = 549). Patients who had gastroesophageal reflux disease were significantly more likely to require fundoplication (OR = 10.83) and undergo aortopexy (OR = 6.417). From multivariate analysis, 'long-gap' EA was a significant predictive factor for late esophageal stricture formation (P = 0.007) and for gastrostomy insertion (P = 0.001). Reflux was a significant predictive factor for requiring fundoplication (P = 0.007) and gastrostomy (P = 0.002). Gastrostomy insertion (P = 0.000) was a significant predictive factor for undergoing fundoplication. Having a prior fundoplication (P = 0.001) was a significant predictive factor for undergoing a subsequent aortopexy. Predictive factors for the occurrence of complications post EA/TEF repair were identified in this large single centre pediatric study. © 2014 International Society for Diseases of the Esophagus.

Molecular factor computing for predictive spectroscopy.

PubMed

Dai, Bin; Urbas, Aaron; Douglas, Craig C; Lodder, Robert A

2007-08-01

The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. In engineering simulations, four MFC filters gave an adequate calibration model (r2 = 0.995, RMSEC = 0.229%, RMSECV = 0.339%, p = 0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2 = 0.993, RMSEC = 0.359%, RMSECV = 0.551%, p = 0.05 by f test). The first actual MFC prototype gave an RMSECV = 0.735%. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Predictive factors for somatization in a trauma sample

PubMed Central

2009-01-01

Background Unexplained somatic symptoms are common among trauma survivors. The relationship between trauma and somatization appears to be mediated by posttraumatic stress disorder (PTSD). However, only few studies have focused on what other psychological risk factors may predispose a trauma victim towards developing somatoform symptoms. Methods The present paper examines the predictive value of PTSD severity, dissociation, negative affectivity, depression, anxiety, and feeling incompetent on somatization in a Danish sample of 169 adult men and women who were affected by a series of explosions in a firework factory settled in a residential area. Results Negative affectivity and feelings of incompetence significantly predicted somatization, explaining 42% of the variance. PTSD was significant until negative affectivity was controlled for. Conclusion Negative affectivity and feelings of incompetence significantly predicted somatization in the trauma sample whereas dissociation, depression, and anxiety were not associated with degree of somatization. PTSD as a risk factor was mediated by negative affectivity. PMID:19126224

Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth.

PubMed

Zhang, Xueying; Chu, Yiyi; Wang, Yuxuan; Zhang, Kai

2018-08-01

The regulatory monitoring data of particulate matter with an aerodynamic diameter <2.5μm (PM 2.5 ) in Texas have limited spatial and temporal coverage. The purpose of this study is to estimate the ground-level PM 2.5 concentrations on a daily basis using satellite-retrieved Aerosol Optical Depth (AOD) in the state of Texas. We obtained the AOD values at 1-km resolution generated through the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm based on the images retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. We then developed mixed-effects models based on AODs, land use features, geographic characteristics, and weather conditions, and the day-specific as well as site-specific random effects to estimate the PM 2.5 concentrations (μg/m 3 ) in the state of Texas during the period 2008-2013. The mixed-effects models' performance was evaluated using the coefficient of determination (R 2 ) and square root of the mean squared prediction error (RMSPE) from ten-fold cross-validation, which randomly selected 90% of the observations for training purpose and 10% of the observations for assessing the models' true prediction ability. Mixed-effects regression models showed good prediction performance (R 2 values from 10-fold cross validation: 0.63-0.69). The model performance varied by regions and study years, and the East region of Texas, and year of 2009 presented relatively higher prediction precision (R 2 : 0.62 for the East region; R 2 : 0.69 for the year of 2009). The PM 2.5 concentrations generated through our developed models at 1-km grid cells in the state of Texas showed a decreasing trend from 2008 to 2013 and a higher reduction of predicted PM 2.5 in more polluted areas. Our findings suggest that mixed-effects regression models developed based on MAIAC AOD are a feasible approach to predict ground-level PM 2.5 in Texas. Predicted PM 2.5 concentrations at the 1-km resolution on a daily basis can be used for

Predicting age at menopause from serum antimüllerian hormone concentration.

PubMed

Tehrani, Fahimeh Ramezani; Shakeri, Nezhat; Solaymani-Dodaran, Masoud; Azizi, Fereidoun

2011-07-01

We aimed to estimate age at menopause using serum antimüllerian hormone (AMH) concentration. We randomly selected 266 study participants from a pool of 1,265 eligible women in the Tehran Lipid and Glucose Study cohort. We measured AMH levels three times at about 3-year intervals. There were 63 occurrences of menopause in our participants over an average of 6-year follow-up. We built an accelerated failure time model using serum AMH level at the start of follow-up to estimate age at menopause. The goodness of fit for the model was tested using Cox-Snell residuals and the Bland-Altman plot. We estimated ages at menopause for different levels of serum AMH concentration among women aged 20 to 49 years. For those who reached menopause, serum AMH concentrations about 6 years before the event provided fairly accurate estimates of the age at menopause. The Bland-Altman plot showed an acceptable agreement between predicted and observed values. Serum AMH concentrations can reasonably forecast the age at menopause for individual women.

PAMPA--critical factors for better predictions of absorption.

PubMed

Avdeef, Alex; Bendels, Stefanie; Di, Li; Faller, Bernard; Kansy, Manfred; Sugano, Kiyohiko; Yamauchi, Yukinori

2007-11-01

PAMPA, log P(OCT), and Caco-2 are useful tools in drug discovery for the prediction of oral absorption, brain penetration and for the development of structure-permeability relationships. Each approach has its advantages and limitations. Selection criteria for methods are based on many different factors: predictability, throughput, cost and personal preferences (people factor). The PAMPA concerns raised by Galinis-Luciani et al. (Galinis-Luciani et al., 2007, J Pharm Sci, this issue) are answered by experienced PAMPA practitioners, inventors and developers from diverse research organizations. Guidelines on how to use PAMPA are discussed. PAMPA and PAMPA-BBB have much better predictivity for oral absorption and brain penetration than log P(OCT) for real-world drug discovery compounds. PAMPA and Caco-2 have similar predictivity for passive oral absorption. However, it is not advisable to use PAMPA to predict absorption involving transporter-mediated processes, such as active uptake or efflux. Measurement of PAMPA is much more rapid and cost effective than Caco-2 and log P(OCT). PAMPA assay conditions are critical in order to generate high quality and relevant data, including permeation time, assay pH, stirring, use of cosolvents and selection of detection techniques. The success of using PAMPA in drug discovery depends on careful data interpretation, use of optimal assay conditions, implementation and integration strategies, and education of users. Copyright 2007 Wiley-Liss, Inc.

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

LARGE-SCALE PREDICTIONS OF MOBILE SOURCE CONTRIBUTIONS TO CONCENTRATIONS OF TOXIC AIR POLLUTANTS

EPA Science Inventory

This presentation shows concentrations and deposition of toxic air pollutants predicted by a 3-D air quality model, the Community Multi Scale Air Quality (CMAQ) modeling system. Contributions from both on-road and non-road mobile sources are analyzed.

Factors affecting water strider (Hemiptera: Gerridae) mercury concentrations in lotic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jardine, T.D.; Kidd, K.A.; Cunjak, R.A.

2009-07-15

Water striders (Hemiptera: Gerridae) have been considered as a potential sentinel for mercury (Hg) contamination of freshwater ecosystems, yet little is known about factors that control Hg concentrations in this invertebrate. Striders were collected from 80 streams and rivers in New Brunswick, Canada, in August and September of 2004 through 2007 to assess the influence of factors such as diet, water chemistry, and proximity to point sources on Hg concentrations in this organism. Higher than average Hg concentrations were observed in the southwest and Grand Lake regions of the province, the latter being the location of a coal-fired power plantmore » that is a source of Hg (similar to 100 kg annually), with elevated Hg concentrations in the lichen Old Man's Beard (Usnea spp.) in its immediate vicinity. Across all streams, pH and total organic carbon of water were relatively weak predictors of strider Hg concentrations. Female striders that were larger in body size than males had significantly lower Hg concentrations within sites, suggestive of growth dilution. There was no relationship between percent aquatic carbon in the diet and Hg concentrations in striders. For those striders feeding solely on terrestrial carbon, Hg concentrations were higher in animals occupying a higher trophic level. Mercury concentrations were highly variable in striders collected monthly over two growing seasons, suggesting short-term changes in Hg availability. These measurements highlight the importance of considering both deposition and postdepositional processes in assessing Hg bioaccumulation in this species.« less

Predicting organic matter, nitrogen, and phosphorus concentrations in runoff from peat extraction sites using partial least squares regression

NASA Astrophysics Data System (ADS)

Tuukkanen, T.; Marttila, H.; Kløve, B.

2017-07-01

Organic matter and nutrient export from drained peatlands is affected by complex hydrological and biogeochemical interactions. Here partial least squares regression (PLSR) was used to relate various soil and catchment characteristics to variations in chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) concentrations in runoff. Peat core samples and water quality data were collected from 15 peat extraction sites in Finland. PLSR models constructed by cross-validation and variable selection routines predicted 92, 88, and 95% of the variation in mean COD, TN, and TP concentration in runoff, respectively. The results showed that variations in COD were mainly related to net production (temperature and water-extractable dissolved organic carbon (DOC)), hydrology (topographical relief), and solubility of dissolved organic matter (peat sulfur (S) and calcium (Ca) concentrations). Negative correlations for peat S and runoff COD indicated that acidity from oxidation of organic S stored in peat may be an important mechanism suppressing organic matter leaching. Moreover, runoff COD was associated with peat aluminum (Al), P, and sodium (Na) concentrations. Hydrological controls on TN and COD were similar (i.e., related to topography), whereas degree of humification, bulk density, and water-extractable COD and Al provided additional explanations for TN concentration. Variations in runoff TP concentration were attributed to erosion of particulate P, as indicated by a positive correlation with suspended sediment concentration (SSC), and factors associated with metal-humic complexation and P adsorption (peat Al, water-extractable P, and water-extractable iron (Fe)).

A method for testing whether model predictions fall within a prescribed factor of true values, with an application to pesticide leaching

USGS Publications Warehouse

Parrish, Rudolph S.; Smith, Charles N.

1990-01-01

A quantitative method is described for testing whether model predictions fall within a specified factor of true values. The technique is based on classical theory for confidence regions on unknown population parameters and can be related to hypothesis testing in both univariate and multivariate situations. A capability index is defined that can be used as a measure of predictive capability of a model, and its properties are discussed. The testing approach and the capability index should facilitate model validation efforts and permit comparisons among competing models. An example is given for a pesticide leaching model that predicts chemical concentrations in the soil profile.

Modeling the rheological behavior of thermosonic extracted guava, pomelo, and soursop juice concentrates at different concentration and temperature using a new combination model

PubMed Central

Abdullah, Norazlin; Yusof, Yus A.; Talib, Rosnita A.

2017-01-01

Abstract This study has modeled the rheological behavior of thermosonic extracted pink‐fleshed guava, pink‐fleshed pomelo, and soursop juice concentrates at different concentrations and temperatures. The effects of concentration on consistency coefficient (K) and flow behavior index (n) of the fruit juice concentrates was modeled using a master curve which utilized the concentration‐temperature shifting to allow a general prediction of rheological behaviors covering a wide concentration. For modeling the effects of temperature on K and n, the integration of two functions from the Arrhenius and logistic sigmoidal growth equations has provided a new model which gave better description of the properties. It also alleviated the problems of negative region when using the Arrhenius model alone. The fitted regression using this new model has improved coefficient of determination, R 2 values above 0.9792 as compared to using the Arrhenius and logistic sigmoidal models alone, which presented minimum R 2 of 0.6243 and 0.9440, respectively. Practical applications In general, juice concentrate is a better form of food for transportation, preservation, and ingredient. Models are necessary to predict the effects of processing factors such as concentration and temperature on the rheological behavior of juice concentrates. The modeling approach allows prediction of behaviors and determination of processing parameters. The master curve model introduced in this study simplifies and generalized rheological behavior of juice concentrates over a wide range of concentration when temperature factor is insignificant. The proposed new mathematical model from the combination of the Arrhenius and logistic sigmoidal growth models has improved and extended description of rheological properties of fruit juice concentrates. It also solved problems of negative values of consistency coefficient and flow behavior index prediction using existing model, the Arrhenius equation. These rheological

Predictive Modeling of Risk Factors and Complications of Cataract Surgery

PubMed Central

Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

2016-01-01

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

Calculating flux to predict future cave radon concentrations.

PubMed

Rowberry, Matt D; Martí, Xavi; Frontera, Carlos; Van De Wiel, Marco J; Briestenský, Miloš

2016-06-01

Cave radon concentration measurements reflect the outcome of a perpetual competition which pitches flux against ventilation and radioactive decay. The mass balance equations used to model changes in radon concentration through time routinely treat flux as a constant. This mathematical simplification is acceptable as a first order approximation despite the fact that it sidesteps an intrinsic geological problem: the majority of radon entering a cavity is exhaled as a result of advection along crustal discontinuities whose motions are inhomogeneous in both time and space. In this paper the dynamic nature of flux is investigated and the results are used to predict cave radon concentration for successive iterations. The first part of our numerical modelling procedure focuses on calculating cave air flow velocity while the second part isolates flux in a mass balance equation to simulate real time dependence among the variables. It is then possible to use this information to deliver an expression for computing cave radon concentration for successive iterations. The dynamic variables in the numerical model are represented by the outer temperature, the inner temperature, and the radon concentration while the static variables are represented by the radioactive decay constant and a range of parameters related to geometry of the cavity. Input data were recorded at Driny Cave in the Little Carpathians Mountains of western Slovakia. Here the cave passages have developed along splays of the NE-SW striking Smolenice Fault and a series of transverse faults striking NW-SE. Independent experimental observations of fault slip are provided by three permanently installed mechanical extensometers. Our numerical modelling has revealed four important flux anomalies between January 2010 and August 2011. Each of these flux anomalies was preceded by conspicuous fault slip anomalies. The mathematical procedure outlined in this paper will help to improve our understanding of radon migration

Meta-analysis of the predictive factors of postpartum fatigue.

PubMed

Badr, Hanan A; Zauszniewski, Jaclene A

2017-08-01

Nearly 64% of new mothers are affected by fatigue during the postpartum period, making it the most common problem that a woman faces as she adapts to motherhood. Postpartum fatigue can lead to serious negative effects on the mother's health and the newborn's development and interfere with mother-infant interaction. The aim of this meta-analysis was to identify predictive factors of postpartum fatigue and to document the magnitude of their effects using effect sizes. We used two search engines, PubMed and Google Scholar, to identify studies that met three inclusion criteria: (a) the article was written in English, (b) the article studied the predictive factors of postpartum fatigue, and (c) the article included information about the validity and reliability of the instruments used in the research. Nine articles met these inclusion criteria. The direction and strength of correlation coefficients between predictive factors and postpartum fatigue were examined across the studies to determine their effect sizes. Measurement of predictor variables occurred from 3days to 6months postpartum. Correlations reported between predictive factors and postpartum fatigue were as follows: small effect size (r range =0.10 to 0.29) for education level, age, postpartum hemorrhage, infection, and child care difficulties; medium effect size (r range =0.30 to 0.49) for physiological illness, low ferritin level, low hemoglobin level, sleeping problems, stress and anxiety, and breastfeeding problems; and large effect size (r range =0.50+) for depression. Postpartum fatigue is a common condition that can lead to serious health problems for a new mother and her newborn. Therefore, increased knowledge concerning factors that influence the onset of postpartum fatigue is needed for early identification of new mothers who may be at risk. Appropriate treatments, interventions, information, and support can then be initiated to prevent or minimize the postpartum fatigue. Copyright © 2017 Elsevier

Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes

PubMed Central

Lin, Chao-Yuan; Chiang, Mon-Ling; Lin, Cheng-Yu

2016-01-01

Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents. PMID:27271642

Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes.

PubMed

Lin, Chao-Yuan; Chiang, Mon-Ling; Lin, Cheng-Yu

2016-06-02

Around the estuary of the Zhuo-Shui River in Taiwan, the waters recede during the winter, causing an increase in bare land area and exposing a large amount of fine earth and sand particles that were deposited on the riverbed. Observations at the site revealed that when northeastern monsoons blow over bare land without vegetation or water cover, the fine particles are readily lifted by the wind, forming river dust, which greatly endangers the health of nearby residents. Therefore, determining which factors affect river dust and constructing a model to predict river dust concentration are extremely important in the research and development of a prototype warning system for areas at risk of river dust emissions. In this study, the region around the estuary of the Zhuo-Shui River (from the Zi-Qiang Bridge to the Xi-Bin Bridge) was selected as the research area. Data from a nearby air quality monitoring station were used to screen for days with river dust episodes. The relationships between PM10 concentration and meteorological factors or bare land area were analyzed at different temporal scales to explore the factors that affect river dust emissions. Study results showed that no single factor alone had adequate power to explain daily average or daily maximum PM10 concentration. Stepwise regression analysis of multiple factors showed that the model could not effectively predict daily average PM10 concentration, but daily maximum PM10 concentration could be predicted by a combination of wind velocity, temperature, and bare land area; the coefficient of determination for this model was 0.67. It was inferred that river dust episodes are caused by the combined effect of multiple factors. In addition, research data also showed a time lag effect between meteorological factors and hourly PM10 concentration. This characteristic was applied to the construction of a prediction model, and can be used in an early warning system for local residents.

The music of clash: predictions on the concentration-mass relation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meneghetti, M.; Rasia, E.; Vega, J.

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies andmore » masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ∼11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.« less

The MUSIC of CLASH: Predictions on the Concentration-Mass Relation

NASA Astrophysics Data System (ADS)

Meneghetti, M.; Rasia, E.; Vega, J.; Merten, J.; Postman, M.; Yepes, G.; Sembolini, F.; Donahue, M.; Ettori, S.; Umetsu, K.; Balestra, I.; Bartelmann, M.; Benítez, N.; Biviano, A.; Bouwens, R.; Bradley, L.; Broadhurst, T.; Coe, D.; Czakon, N.; De Petris, M.; Ford, H.; Giocoli, C.; Gottlöber, S.; Grillo, C.; Infante, L.; Jouvel, S.; Kelson, D.; Koekemoer, A.; Lahav, O.; Lemze, D.; Medezinski, E.; Melchior, P.; Mercurio, A.; Molino, A.; Moscardini, L.; Monna, A.; Moustakas, J.; Moustakas, L. A.; Nonino, M.; Rhodes, J.; Rosati, P.; Sayers, J.; Seitz, S.; Zheng, W.; Zitrin, A.

2014-12-01

We present an analysis of the MUSIC-2 N-body/hydrodynamical simulations aimed at estimating the expected concentration-mass relation for the CLASH (Cluster Lensing and Supernova Survey with Hubble) cluster sample. We study nearly 1,400 halos simulated at high spatial and mass resolution. We study the shape of both their density and surface-density profiles and fit them with a variety of radial functions, including the Navarro-Frenk-White (NFW), the generalized NFW, and the Einasto density profiles. We derive concentrations and masses from these fits. We produce simulated Chandra observations of the halos, and we use them to identify objects resembling the X-ray morphologies and masses of the clusters in the CLASH X-ray-selected sample. We also derive a concentration-mass relation for strong-lensing clusters. We find that the sample of simulated halos that resembles the X-ray morphology of the CLASH clusters is composed mainly of relaxed halos, but it also contains a significant fraction of unrelaxed systems. For such a heterogeneous sample we measure an average two-dimensional concentration that is ~11% higher than is found for the full sample of simulated halos. After accounting for projection and selection effects, the average NFW concentrations of CLASH clusters are expected to be intermediate between those predicted in three dimensions for relaxed and super-relaxed halos. Matching the simulations to the individual CLASH clusters on the basis of the X-ray morphology, we expect that the NFW concentrations recovered from the lensing analysis of the CLASH clusters are in the range [3-6], with an average value of 3.87 and a standard deviation of 0.61.

Measured and predicted environmental concentrations of carbamazepine, diclofenac, and metoprolol in small and medium rivers in northern Germany.

PubMed

Meyer, Wibke; Reich, Margrit; Beier, Silvio; Behrendt, Joachim; Gulyas, Holger; Otterpohl, Ralf

2016-08-01

This study evaluated the impact of secondary municipal effluent discharge on carbamazepine, diclofenac, and metoprolol concentrations in small and medium rivers in northern Germany and compared the measured environmental concentrations (MECs) to the predicted environmental concentrations (PECs) calculated with four well-established models. During a 1-year sampling period, secondary effluent grab samples were collected at four wastewater treatment plants (WWTPs) together with grab samples from the receiving waters upstream and downstream from the wastewater discharge points. The carbamazepine, diclofenac, and metoprolol concentrations were analyzed with high-performance liquid chromatography-tandem mass spectrometry (HPLC/MS-MS) after solid phase extraction. In the secondary effluents, 84-790 ng/L carbamazepine, 395-2100 ng/L diclofenac, and 745-5000 ng/L metoprolol were detected. The carbamazepine, diclofenac, and metoprolol concentrations analyzed in the rivers downstream from the secondary effluent discharge sites ranged from <5 to 68, 370, and 520 ng/L, respectively. Most of the downstream pharmaceutical concentrations were markedly higher than the corresponding upstream concentrations. The impact of wastewater discharge on the MECs in rivers downstream from the WWTPs was clearly demonstrated, but the correlations of the MECs with dilution factors were poor. The smallest rivers exhibited the largest maximum MECs and the widest ranges of MECs downstream from the wastewater discharge point. Three of the four tested models were conservative, as they showed higher PECs than the MECs in the rivers downstream from the WWTPs. However, the most detailed model underestimated the diclofenac concentrations.

Factors Predicting Post-High School Employment for Young Adults with Visual Impairments

ERIC Educational Resources Information Center

McDonnall, Michele Capella

2010-01-01

Although low levels of employment among transition-age youth with visual impairments (VI) have long been a concern, empirical research in this area is very limited. The purpose of this study was to identify factors that predict future employment for this population and to compare these factors to the factors that predict employment for the general…

Group size and visitor numbers predict faecal glucocorticoid concentrations in zoo meerkats

PubMed Central

Scott, Katy; Heistermann, Michael

2017-01-01

Measures of physiological stress in zoo animals can give important insights into how they are affected by aspects of their captive environment. We analysed the factors influencing variation in glucocorticoid metabolites in faeces (fGCs) from zoo meerkats as a proxy for blood cortisol concentration, high levels of which are associated with a stress response. Levels of fGCs in captive meerkats declined with increasing group size. In the wild, very small groups of meerkats are at a higher risk of predation, while in larger groups, there is increased competition for resources. Indeed, group sizes in captivity resemble those seen in unstable coalitions in the wild, which may represent a stressful condition and predispose meerkats to chronic stress, even in the absence of natural predators. Individuals in large enclosures showed lower levels of stress, but meerkat density had no effect on the stress measures. In contrast with data from wild meerkats, neither sex, age nor dominance status predicted stress levels, which may reflect less food stress owing to more equal access to resources in captivity versus wild. The median number of visitors at the enclosure was positively correlated with fGC concentrations on the following day, with variation in the visitor numbers having the opposite effect. Our results are consistent with the hypothesis that there is an optimum group size which minimizes physiological stress in meerkats, and that zoo meerkats at most risk of physiological stress are those kept in small groups and small enclosures and are exposed to consistently high numbers of visitors. PMID:28484620

Group size and visitor numbers predict faecal glucocorticoid concentrations in zoo meerkats.

PubMed

Scott, Katy; Heistermann, Michael; Cant, Michael A; Vitikainen, Emma I K

2017-04-01

Measures of physiological stress in zoo animals can give important insights into how they are affected by aspects of their captive environment. We analysed the factors influencing variation in glucocorticoid metabolites in faeces (fGCs) from zoo meerkats as a proxy for blood cortisol concentration, high levels of which are associated with a stress response. Levels of fGCs in captive meerkats declined with increasing group size. In the wild, very small groups of meerkats are at a higher risk of predation, while in larger groups, there is increased competition for resources. Indeed, group sizes in captivity resemble those seen in unstable coalitions in the wild, which may represent a stressful condition and predispose meerkats to chronic stress, even in the absence of natural predators. Individuals in large enclosures showed lower levels of stress, but meerkat density had no effect on the stress measures. In contrast with data from wild meerkats, neither sex, age nor dominance status predicted stress levels, which may reflect less food stress owing to more equal access to resources in captivity versus wild. The median number of visitors at the enclosure was positively correlated with fGC concentrations on the following day, with variation in the visitor numbers having the opposite effect. Our results are consistent with the hypothesis that there is an optimum group size which minimizes physiological stress in meerkats, and that zoo meerkats at most risk of physiological stress are those kept in small groups and small enclosures and are exposed to consistently high numbers of visitors.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

Nazif, Amina; Mohammed, Nurul Izma; Malakahmad, Amirhossein; Abualqumboz, Motasem S

2018-01-01

The devastating health effects of particulate matter (PM 10 ) exposure by susceptible populace has made it necessary to evaluate PM 10 pollution. Meteorological parameters and seasonal variation increases PM 10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM 10 concentration levels. The analyses were carried out using daily average PM 10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM 10 concentration levels having coefficient of determination (R 2 ) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Antepartal insulin-like growth factor 1 and insulin-like growth factor binding protein 2 concentrations are indicative of ketosis in dairy cows.

PubMed

Piechotta, M; Mysegades, W; Ligges, U; Lilienthal, J; Hoeflich, A; Miyamoto, A; Bollwein, H

2015-05-01

A study involving a small number of cows found that the concentrations of insulin-like growth hormone 1 (IGF1) may be a useful predictor of metabolic disease. Further, IGF1 may provide also a pathophysiological link to metabolic diseases such as ketosis. The objective of the current study was to test whether the low antepartal total IGF1 or IGF1 binding protein (IGFBP) concentrations might predict ketosis under field conditions. Clinical examinations and blood sampling were performed antepartum (262-270 d after artificial insemination) on 377 pluriparous pregnant Holstein Friesian cows. The presence of postpartum diseases were recorded (ketosis, fatty liver, displacement of the abomasum, hypocalcemia, mastitis, retention of fetal membranes, and clinical metritis or endometritis), and the concentrations of IGF1, IGFBP2, IGFBP3, and nonesterified fatty acids were measured. Cows with postpartum clinical ketosis had lower IGF1 concentrations antepartum than healthy cows. The sensitivity of antepartal IGF1 as a marker for postpartum ketosis was 0.87, and the specificity was 0.43; a positive predictive value of 0.91 and a negative predictive value of 0.35 were calculated. The cows with ketosis and retained fetal membranes had lower IGFBP2 concentrations compared with the healthy cows. It can be speculated that lower IGF1 production in the liver during late pregnancy may increase growth hormone secretions and lipolysis, thereby increasing the risk of ketosis. Lower IGFBP2 concentrations may reflect the suppression of IGFBP2 levels through higher growth hormone secretion. In conclusion, compared with nonesterified fatty acids as a predictive parameter, IGF1 and IGFBP2 may represent earlier biomarkers of inadequate metabolic adaptation to the high energy demand required postpartum. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Analysis of factors predicting speed of hematologic recovery after transplantation with 4-hydroperoxycyclophosphamide-purged autologous bone marrow grafts.

PubMed

Rowley, S D; Piantadosi, S; Marcellus, D C; Jones, R J; Davidson, N E; Davis, J M; Kennedy, J; Wiley, J M; Wingard, J R; Yeager, A M

1991-03-01

We previously described the predictive value of graft colony-forming units granulocyte macrophage (CFU-GM) content after 4-hydroperoxycyclophosphamide (4-HC) purging for the duration of aplasia after autologous bone marrow transplantation. Despite the uniform 4-HC concentration, we observed heterogeneity in CFU-GM survival and the kinetics of engraftment. We have now analysed patient and graft characteristics for 154 patients undergoing autologous transplantation with 4-HC purged grafts to further define this heterogeneity. Patients transplanted for the treatment of malignant lymphoma reached a peripheral blood granulocyte count of greater than 0.5 x 10(9)/l (median, 20 versus 40 days; p less than 0.001) and platelet transfusion independence (median, 30 versus 70 days; p less than 0.001) significantly faster than patients transplanted for acute non-lymphoblastic leukemia. Other diagnostic groups were intermediate. These differences were independent of graft CFU-GM content. Multiple other patient and graft factors including patient age, peripheral blood counts on day of harvest, and amounts of other hematopoietic progenitors also predicted the kinetics of engraftment in univariate and multivariate analysis. Cytomegalovirus infection during the aplastic period predicted a delay in granulocyte (p = 0.024) but not platelet recovery (p = 0.174). This analysis demonstrates that multiple patient, graft, and post-transplant factors predict the engraftment capacity of autografts, and the kinetics of engraftment with 4-HC purged grafts. The multiple predictive factors explain a significant portion of the variability in engraftment kinetics observed after transplantation with 4-HC purged autografts.

Investigating Factors that Affect Dissolved Oxygen Concentration in Water

ERIC Educational Resources Information Center

Jantzen, Paul G.

1978-01-01

Describes activities that demonstrate the effects of factors such as wind velocity, water temperature, convection currents, intensity of light, rate of photosynthesis, atmospheric pressure, humidity, numbers of decomposers, presence of oxidizable ions, and respiration by plants and animals on the dissolved oxygen concentration in water. (MA)

[Application of predictive model to estimate concentrations of chemical substances in the work environment].

PubMed

Kupczewska-Dobecka, Małgorzata; Czerczak, Sławomir; Jakubowski, Marek; Maciaszek, Piotr; Janasik, Beata

2010-01-01

Based on the Estimation and Assessment of Substance Exposure (EASE) predictive model implemented into the European Union System for the Evaluation of Substances (EUSES 2.1.), the exposure to three chosen organic solvents: toluene, ethyl acetate and acetone was estimated and compared with the results of measurements in workplaces. Prior to validation, the EASE model was pretested using three exposure scenarios. The scenarios differed in the decision tree of pattern of use. Five substances were chosen for the test: 1,4-dioxane tert-methyl-butyl ether, diethylamine, 1,1,1-trichloroethane and bisphenol A. After testing the EASE model, the next step was the validation by estimating the exposure level and comparing it with the results of measurements in the workplace. We used the results of measurements of toluene, ethyl acetate and acetone concentrations in the work environment of a paint and lacquer factory, a shoe factory and a refinery. Three types of exposure scenarios, adaptable to the description of working conditions were chosen to estimate inhalation exposure. Comparison of calculated exposure to toluene, ethyl acetate and acetone with measurements in workplaces showed that model predictions are comparable with the measurement results. Only for low concentration ranges, the measured concentrations were higher than those predicted. EASE is a clear, consistent system, which can be successfully used as an additional component of inhalation exposure estimation. If the measurement data are available, they should be preferred to values estimated from models. In addition to inhalation exposure estimation, the EASE model makes it possible not only to assess exposure-related risk but also to predict workers' dermal exposure.

Associations between toenail arsenic concentration and dietary factors in a New Hampshire population.

PubMed

Gruber, Joann F; Karagas, Margaret R; Gilbert-Diamond, Diane; Bagley, Pamela J; Zens, M Scot; Sayarath, Vicki; Punshon, Tracy; Morris, J Steven; Cottingham, Kathryn L

2012-06-29

Dietary factors such as folate, vitamin B12, protein, and methionine are important for the excretion of arsenic via one-carbon metabolism in undernourished populations exposed to high levels of arsenic via drinking water. However, the effects of dietary factors on toenail arsenic concentrations in well-nourished populations exposed to relatively low levels of water arsenic are unknown. As part of a population-based case-control study of skin and bladder cancer from the USA, we evaluated relationships between consumption of dietary factors and arsenic concentrations in toenail clippings. Consumption of each dietary factor was determined from a validated food frequency questionnaire. We used general linear models to examine the associations between toenail arsenic and each dietary factor, taking into account potentially confounding effects. As expected, we found an inverse association between ln-transformed toenail arsenic and consumption of vitamin B12 (excluding supplements) and animal protein. Unexpectedly, there were also inverse associations with numerous dietary lipids (e.g., total fat, total animal fat, total vegetable fat, total monounsaturated fat, total polyunsaturated fat, and total saturated fat). Finally, increased toenail arsenic concentrations were associated with increased consumption of long chain n-3 fatty acids. In a relatively well-nourished population exposed to relatively low levels of arsenic via water, consumption of certain dietary lipids may decrease toenail arsenic concentration, while long chain n-3 fatty acids may increase toenail arsenic concentration, possibly due to their association with arsenolipids in fish tissue.

Associations between toenail arsenic concentration and dietary factors in a New Hampshire population

PubMed Central

2012-01-01

Background Dietary factors such as folate, vitamin B12, protein, and methionine are important for the excretion of arsenic via one-carbon metabolism in undernourished populations exposed to high levels of arsenic via drinking water. However, the effects of dietary factors on toenail arsenic concentrations in well-nourished populations exposed to relatively low levels of water arsenic are unknown. Methods As part of a population-based case–control study of skin and bladder cancer from the USA, we evaluated relationships between consumption of dietary factors and arsenic concentrations in toenail clippings. Consumption of each dietary factor was determined from a validated food frequency questionnaire. We used general linear models to examine the associations between toenail arsenic and each dietary factor, taking into account potentially confounding effects. Results As expected, we found an inverse association between ln-transformed toenail arsenic and consumption of vitamin B12 (excluding supplements) and animal protein. Unexpectedly, there were also inverse associations with numerous dietary lipids (e.g., total fat, total animal fat, total vegetable fat, total monounsaturated fat, total polyunsaturated fat, and total saturated fat). Finally, increased toenail arsenic concentrations were associated with increased consumption of long chain n-3 fatty acids. Conclusion In a relatively well-nourished population exposed to relatively low levels of arsenic via water, consumption of certain dietary lipids may decrease toenail arsenic concentration, while long chain n-3 fatty acids may increase toenail arsenic concentration, possibly due to their association with arsenolipids in fish tissue. PMID:22747713

Concentrated Growth Factor Enhanced Fat Graft Survival: A Comparative Study.

PubMed

Hu, Yun; Jiang, Yichen; Wang, Muyao; Tian, Weidong; Wang, Hang

2018-06-08

Concentrated growth factors (CGFs) belong to a new generation biomaterials that concentrate large number of growth factors and CD34 stem cells in small volume of plasma. The purpose of this study was to evaluate the impact of the new technique, CGF, on fat graft survival, which compared with platelet-rich plasma (PRP) and platelet-rich fibrin (PRF). Nude mice received fat graft were divided into PRP group, PRF group, CGF group, and saline. The grafts were volumetrically and histologically evaluated at 4, 8, and 12 weeks after fat grafting. In vitro growth factor levels in PRP, PRF, and CGF were compared using enzyme-linked immunoassay method. Cell count and real-time polymerase chain reaction were used to evaluate the impact of CGF in medium on human adipose-derived stem cell (hADSC) proliferation and vascular differentiation, respectively. Fat graft weight was significantly higher in the CGF group than those in the other groups, and histologic evaluation revealed greater vascularity, fewer cysts, and less fibrosis. Adding CGF to the medium maximally promoted hADSC proliferation and expressing vascular endothelial growth factor and PECAM-1. In this preliminary study, CGF treatment improved the survival and quality of fat grafts.

Using Emotional and Social Factors To Predict Student Success.

ERIC Educational Resources Information Center

Pritchard, Mary E.; Wilson, Gregory S.

2003-01-01

College academic success and retention have traditionally been predicted using demographic and academic variables. This study moved beyond traditional predictors. A survey of 218 undergraduate students revealed that emotional and social factors (e.g., stress, frequency of alcohol consumption) related to GPA and emotional factors (e.g.,…

Predicting risk for childhood asthma by pre-pregnancy, perinatal, and postnatal factors.

PubMed

Wen, Hui-Ju; Chiang, Tung-Liang; Lin, Shio-Jean; Guo, Yue Leon

2015-05-01

Symptoms of atopic disease start early in human life. Predicting risk for childhood asthma by early-life exposure would contribute to disease prevention. A birth cohort study was conducted to investigate early-life risk factors for childhood asthma and to develop a predictive model for the development of asthma. National representative samples of newborn babies were obtained by multistage stratified systematic sampling from the 2005 Taiwan Birth Registry. Information on potential risk factors and children's health was collected by home interview when babies were 6 months old and 5 yr old, respectively. Backward stepwise regression analysis was used to identify the risk factors of childhood asthma for predictive models that were used to calculate the probability of childhood asthma. A total of 19,192 children completed the study satisfactorily. Physician-diagnosed asthma was reported in 6.6% of 5-yr-old children. Pre-pregnancy factors (parental atopy and socioeconomic status), perinatal factors (place of residence, exposure to indoor mold and painting/renovations during pregnancy), and postnatal factors (maternal postpartum depression and the presence of atopic dermatitis before 6 months of age) were chosen for the predictive models, and the highest predicted probability of asthma in 5-yr-old children was 68.1% in boys and 78.1% in girls; the lowest probability in boys and girls was 4.1% and 3.2%, respectively. This investigation provides a technique for predicting risk of childhood asthma that can be used to developing a preventive strategy against asthma. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparison of growth factor and platelet concentration from commercial platelet-rich plasma separation systems.

PubMed

Castillo, Tiffany N; Pouliot, Michael A; Kim, Hyeon Joo; Dragoo, Jason L

2011-02-01

Clinical studies claim that platelet-rich plasma (PRP) shortens recovery times because of its high concentration of growth factors that may enhance the tissue repair process. Most of these studies obtained PRP using different separation systems, and few analyzed the content of the PRP used as treatment. This study characterized the composition of single-donor PRP produced by 3 commercially available PRP separation systems. Controlled laboratory study. Five healthy humans donated 100 mL of blood, which was processed to produce PRP using 3 PRP concentration systems (MTF Cascade, Arteriocyte Magellan, Biomet GPS III). Platelet, white blood cell (WBC), red blood cell, and fibrinogen concentrations were analyzed by automated systems in a clinical laboratory, whereas ELISA determined the concentrations of platelet-derived growth factor αβ and ββ (PDGF-αβ, PDGF-ββ), transforming growth factor β1 (TGF-β1), and vascular endothelial growth factor (VEGF). There was no significant difference in mean PRP platelet, red blood cell, active TGF-β1, or fibrinogen concentrations among PRP separation systems. There was a significant difference in platelet capture efficiency. The highest platelet capture efficiency was obtained with Cascade, which was comparable with Magellan but significantly higher than GPS III. There was a significant difference among all systems in the concentrations of WBC, PDGF-αβ, PDGF-ββ, and VEGF. The Cascade system concentrated leukocyte-poor PRP, compared with leukocyte-rich PRP from the GPS III and Magellan systems. The GPS III and Magellan concentrate leukocyte-rich PRP, which results in increased concentrations of WBCs, PDGF-αβ, PDGF-ββ, and VEGF as compared with the leukocyte-poor PRP from Cascade. Overall, there was no significant difference among systems in the platelet concentration, red blood cell, active TGF-β1, or fibrinogen levels. Products from commercially available PRP separation systems produce differing concentrations of

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2001-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated with Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2000-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating fife based on a concentration dependent failure criterion (e.g., surface solute content drops to two percent). The computer code, written in an extension of FORTRAN 77, employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Predicting Gender-Role Attitudes in Adolescent Females: Ability, Agency, and Parental Factors.

ERIC Educational Resources Information Center

Ahrens, Julia A.; O'Brien, Karen M.

1996-01-01

Investigated the contribution of ability, agency, and parental factors to the prediction of gender-role attitudes of 409 adolescent females in a private, college-preparatory high school. Findings indicate that ability and agency were predictive of gender-role attitudes, whereas parental factors were not significant contributors. Recommendations…

The effect of pasteurization on transforming growth factor alpha and transforming growth factor beta 2 concentrations in human milk.

PubMed

McPherson, R J; Wagner, C L

2001-01-01

Transforming growth factor alpha (TGF-alpha) and beta 2 (TGF-beta2) are present in human milk and are involved in growth differentiation and repair of neonatal intestinal epithelia. Heat treatment at 56 degrees C has been shown effective for providing safe banked donor milk, with good retention of other biologically active factors. The purpose of our study was to determine the effect of heat sterilization on TGF-alpha and TGF-beta2 concentrations in human milk. Twenty milk samples were collected from 20 lactating mothers in polypropylene containers and frozen at -20 degrees C for transport or storage. Before heat treatment by holder pasteurization, the frozen milk was thawed and divided into 1-mL aliquots. All samples were heated in an accurately regulated water bath until a holding temperature was achieved, then held for 30 minutes using constant agitation. Holding temperature ranged from 56.5 degrees C to 56.9 degrees C. The milk was then stored at 4 degrees C overnight for analysis the following day. The concentration of TGF-alpha was measured by radioimmunoassay. Mean concentration +/- SD of TGF-alpha in raw milk samples was 119+/-50 pg/mL, range 57 to 234. The mean concentration +/- SD of TGF-alpha in heat treated samples was 113+/-50 pg/mL, range 51 to 227. TGF-alpha concentration was minimally affected by pasteurization, with an overall loss of 6.1%. Of 19 samples, 4 had increased and 15 had decreased concentrations after pasteurization (mean percent SEM: 94%+/-7% of raw milk, range 72%+/-107%). The concentration of acid-activated TGF-beta2 was measured by enzyme-linked immunosorbent assay. Mean concentration +/- SD of TGF-beta2 in raw milk samples was 5624+/-5038 pg/mL, range 195 to 15480. The mean concentration +/- SD of TGF-beta2 in heat-treated samples was 5073+/-4646 pg/mL, range 181 to 15140. TGF-beta2 survived with relatively little loss (0.6%): of 18 samples, 11 had increased and 7 had decreased concentrations after pasteurization (mean percent

Towards automating measurements and predictions of Escherichia coli concentrations in the Cuyahoga River, Cuyahoga Valley National Park, Ohio, 2012–14

USGS Publications Warehouse

Brady, Amie M. G.; Meg B. Plona,

2015-07-30

A computer program was developed to manage the nowcasts by running the predictive models and posting the results to a publicly accessible Web site daily by 9 a.m. The nowcasts were able to correctly predict E. coli concentrations above or below the water-quality standard at Jaite for 79 percent of the samples compared with the measured concentrations. In comparison, the persistence model (using the previous day’s sample concentration) correctly predicted concentrations above or below the water-quality standard in only 68 percent of the samples. To determine if the Jaite nowcast could be used for the stretch of the river between Lock 29 and Jaite, the model predictions for Jaite were compared with the measured concentrations at Lock 29. The Jaite nowcast provided correct responses for 77 percent of the Lock 29 samples, which was a greater percentage than the percentage of correct responses (58 percent) from the persistence model at Lock 29.

Ratio of urine and blood urea nitrogen concentration predicts the response of tolvaptan in congestive heart failure.

PubMed

Shimizu, Keisuke; Doi, Kent; Imamura, Teruhiko; Noiri, Eisei; Yahagi, Naoki; Nangaku, Masaomi; Kinugawa, Koichiro

2015-06-01

This study was conducted to evaluate the performance of the ratio of urine and blood urea nitrogen concentration (UUN/BUN) as a new predictive factor for the response of an arginine vasopressin receptor 2 antagonist tolvaptan (TLV) in decompensated heart failure patients. This study enrolled 70 decompensated heart failure patients who were administered TLV at University of Tokyo Hospital. We collected the data of clinical parameters including UUN/BUN before administering TLV. Two different outcomes were defined as follows: having over 300 mL increase in urine volume on the first day (immediate urine output response) and having any decrease in body weight within one week after starting TLV treatment (subsequent clinical response). Among the 70 enrolled patients, 37 patients (52.9%) showed immediate urine output response; 51 patients (72.9%) showed a subsequent clinical response of body weight decrease. Receiver operating characteristics (ROC) analysis showed good prediction by UUN/BUN for the immediate response (AUC-ROC 0.86 [0.75-0.93]) and a significantly better prediction by UUN/BUN for the subsequent clinical response compared with urinary osmolality (AUC-ROC 0.78 [0.63-0.88] vs. 0.68 [0.52-0.80], P < 0.05). We demonstrated that a clinical parameter of UUN/BUN can predict the response of TLV even when measured before TLV administration. UUN/BUN might enable identification of good responders for this new drug. © 2015 Asian Pacific Society of Nephrology.

Deriving Predicted No-Effect Concentrations in Diverse Geographies for use in eco-TTC Estimations

EPA Science Inventory

cological Thresholds for Toxicologic Concern (eco-TTC) employs an assessment of distributions of Predicted No-Effect Concentrations (PNECs) for compounds following chemical grouping. Grouping can be by mode of action, structural fragments, or by chemical functional use. Thus, eco...

Psychological factors that predict reaction to abortion.

PubMed

Moseley, D T; Follingstad, D R; Harley, H; Heckel, R V

1981-04-01

Investigated demographic and psychological factors related to positive or negative reactions to legal abortions performed during the first trimester of pregnancy in 62 females in an urban southern community. Results suggest that the social context and the degree of support from a series of significant persons rather than demographic variables were most predictive of a positive reaction.

Cardiometabolic risk factors are associated with high urinary enterolactone concentration, independent of urinary enterodiol concentration and dietary fiber intake in adults.

PubMed

Frankenfeld, Cara L

2014-09-01

The study objective was to evaluate independent and interactive associations of dietary fiber intake and high urinary enterolignans with cardiometabolic risk factors. The analysis included 2260 adults (≥20 y of age) from the 2003-2010 NHANES. Logistic regression models were used to evaluate obesity and clinically defined cardiometabolic risk factors in relation to dietary fiber intake and urinary enterolignan concentrations. Three sets of models were created: 1) independent associations, 2) mutually adjusted associations, and 3) interactions. Models were adjusted for age, gender, race/ethnicity, education, smoking status, and energy intake. High concentrations were considered to be above the 90th percentile of urinary enterolignan concentrations. Increasing dietary fiber intake was associated with high blood pressure (P = 0.02) and low serum HDL cholesterol (P-trend = 0.03). High urinary enterodiol concentration was not associated with obesity or cardiometabolic risk factors. High urinary enterolactone concentration was inversely associated with obesity (OR: 0.44; 95% CI: 0.29, 0.66), abdominal obesity (OR: 0.58; 95% CI: 0.39, 0.87), high serum C-reactive protein (CRP; OR: 0.52; 95% CI: 0.37, 0.74), high serum triglycerides (OR: 0.39; 95% CI: 0.23, 0.61), low serum HDL cholesterol (OR: 0.37; 95% CI: 0.23, 0.61), and metabolic syndrome (OR: 0.47; 95% CI: 0.30, 0.74). In mutually adjusted models, enterolactone associations observed in independent models remained similar, but associations for dietary fiber intake were attenuated, with the exception of blood pressure. In interaction models, there were 2 significant interactions: between high urinary enterodiol concentration and dietary fiber intake for high serum CRP (P = 0.04) and high plasma glucose (P = 0.04). Overall, being in the highest 10% of urinary enterolactone concentration was associated with cardiometabolic risk factors, independent of dietary fiber intake and enterodiol concentration. Future studies are

Factors controlling particle number concentration and size at metro stations

NASA Astrophysics Data System (ADS)

Reche, C.; Moreno, T.; Martins, V.; Minguillón, M. C.; Jones, T.; de Miguel, E.; Capdevila, M.; Centelles, S.; Querol, X.

2017-05-01

An extensive air quality campaign was performed at differently designed station platforms in the Barcelona metro system, aiming to investigate the factors governing airborne particle number (N) concentrations and their size distributions. The study of the daily trends of N concentrations by different size ranges shows that concentrations of N0.3-10 are closely related with the schedule of the metro service. Conversely, the hourly variation of N0.007-10 (mainly composed of ultrafine particles) could be partly governed by the entrance of particles from outdoor emissions through mechanical ventilation. Measurements under different ventilation settings at three metro platforms reveal that the effect on air quality linked to changes in the tunnel ventilation depends on the station design. Night-time maintenance works in tunnels are frequent activities in the metro system; and after intense prolonged works, these can result in higher N concentrations at platforms during the following metro operating hours (by up to 30%), this being especially evident for N1-10. Due to the complex mixture of factors controlling N, together with the differences in trends recorded for particles within different size ranges, developing an air quality strategy at metro systems is a great challenge. When compared to street-level urban particles concentrations, the priority in metro air quality should be dealing with particles coarser than 0.3 μm. In fact, the results suggest that at narrow platforms served by single-track tunnels the current forced tunnel ventilation during operating hours is less efficient in reducing coarse particles compared to fine.

Factors that predict adolescent motivation for substance abuse treatment.

PubMed

Battjes, Robert J; Gordon, Michael S; O'Grady, Kevin E; Kinlock, Timothy W; Carswell, Melissa A

2003-04-01

Many adolescent substance abusers enter treatment because of external pressures and thus lack motivation to change their behavior and engage in treatment. Because an understanding of adolescent motivation may contribute to improved treatment, an investigation of factors that predict motivation was undertaken with youth admitted to an adolescent outpatient substance abuse treatment program (N=196). At admission, these subjects received a comprehensive biopsychosocial assessment. Using multiple regression analysis, factors considered to potentially predict motivation were assessed. Of the factors examined, those that involved experiencing various negative consequences of substance use emerged as important predictors of motivation, whereas severity of substance use did not. Diminished awareness of negative consequences of use was consonant with lower motivation, suggesting the importance of interventions to help youth recognize negative consequences of their substance use. Interventions to enhance motivation are likely to become more important as the juvenile justice system increasingly refers troubled youth to treatment.

Development of a modeling approach to estimate indoor-to-outdoor sulfur ratios and predict indoor PM2.5 and black carbon concentrations for Eastern Massachusetts households

PubMed Central

Tang, Chia Hsi; Garshick, Eric; Grady, Stephanie; Coull, Brent; Schwartz, Joel; Koutrakis, Petros

2018-01-01

The effects of indoor air pollution on human health have drawn increasing attention among the scientific community as individuals spend most of their time indoors. However, indoor air sampling is labor-intensive and costly, which limits the ability to study the adverse health effects related to indoor air pollutants. To overcome this challenge, many researchers have attempted to predict indoor exposures based on outdoor pollutant concentrations, home characteristics, and weather parameters. Typically, these models require knowledge of the infiltration factor, which indicates the fraction of ambient particles that penetrates indoors. For estimating indoor fine particulate matter (PM2.5) exposure, a common approach is to use the indoor-to-outdoor sulfur ratio (Sindoor/Soutdoor) as a proxy of the infiltration factor. The objective of this study was to develop a robust model that estimates Sindoor/Soutdoor for individual households that can be incorporated into models to predict indoor PM2.5 and black carbon (BC) concentrations. Overall, our model adequately estimated Sindoor/Soutdoor with an out-of-sample by home-season R2 of 0.89. Estimated Sindoor/Soutdoor reflected behaviors that influence particle infiltration, including window opening, use of forced air heating, and air purifier. Sulfur ratio-adjusted models predicted indoor PM2.5 and BC with high precision, with out-of-sample R2 values of 0.79 and 0.76, respectively. PMID:29064481

Shoulder dystocia: risk factors, predictability, and preventability.

PubMed

Mehta, Shobha H; Sokol, Robert J

2014-06-01

Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

Pregnancy urinary phthalate metabolite concentrations and gestational diabetes risk factors.

PubMed

James-Todd, Tamarra M; Meeker, John D; Huang, Tianyi; Hauser, Russ; Ferguson, Kelly K; Rich-Edwards, Janet W; McElrath, Thomas F; Seely, Ellen W

2016-11-01

Epidemiologic studies suggest phthalate metabolite concentrations are associated with type 2 diabetes. GDM is a strong risk factor for type 2 diabetes. Little is known about phthalates and GDM risk factors (i.e. 1st trimester body mass index (BMI), gestational weight gain (GWG), and 2nd trimester glucose levels). A total of 350 women participating in Lifecodes pregnancy cohort (Boston, MA), delivered at term and had pregnancy urinary phthalate metabolite concentrations. Nine specific gravity-adjusted urinary phthalate metabolites were evaluated. General linear regression was used to assess associations between quartiles of phthalate metabolites and continuous 1st trimester BMI and late 2nd trimester blood glucose. Linear mixed models were used for total GWG. Multivariable logistic regression was used for phthalate concentrations and categorized GWG and impaired glucose tolerance defined as glucose≥140mg/dL based on a 50-gram glucose load test. Models were adjusted for potential confounders. There were no associations between 1st trimester urinary phthalate metabolite concentrations and 1st trimester BMI. Mono-ethyl phthalate concentrations averaged across pregnancy were associated with a 2.17 increased odds of excessive GWG (95% CI: 0.98, 4.79). Second trimester mono-ethyl phthalate was associated with increased odds of impaired glucose tolerance (adj. OR: 7.18; 95% CI: 1.97, 26.15). A summary measure of di-2-ethylhexyl phthalate metabolite concentrations were inversely associated with impaired glucose tolerance (adj. OR: 0.25; adj. 95% CI: 0.08, 0.85). Higher exposure to mono-ethyl phthalate, a metabolite of the parent compound of di-ethyl phthalate, may be associated with excessive GWG and impaired glucose tolerance; higher di-2-ethylhexyl phthalate was associated with reduced odds of impaired glucose tolerance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pregnancy urinary phthalate metabolite concentrations and gestational diabetes risk factors

PubMed Central

James-Todd, TM; Meeker, JD; Huang, T; Hauser, R; Ferguson, KK; Rich-Edwards, JW; McElrath, TF; Seely, EW

2016-01-01

Background Epidemiologic studies suggest phthalate metabolite concentrations are associated with type 2 diabetes. GDM is a strong risk factor for type 2 diabetes. Little is known about phthalates and GDM risk factors (i.e. 1st trimester body mass index (BMI), gestational weight gain (GWG), and 2nd trimester glucose levels). Methods A total of 350 women participating in Lifecodes pregnancy cohort (Boston, MA), delivered at term and had pregnancy urinary phthalate metabolite concentrations. Nine specific gravity-adjusted urinary phthalate metabolites were evaluated. General linear regression was used to assess associations between quartiles of phthalate metabolites and continuous 1st trimester BMI and late 2nd trimester blood glucose. Linear mixed models were used for total GWG. Multivariable logistic regression was used for phthalate concentrations and categorized GWG and impaired glucose tolerance defined as glucose ≥ 140mg/dL based on a 50-gram glucose load test. Models were adjusted for potential confounders. Results There were no associations between 1st trimester urinary phthalate metabolite concentrations and 1st trimester BMI. Mono-ethyl phthalate (MEP) concentrations averaged across pregnancy were associated with a 2.17 increased odds of excessive GWG (95% CI: 0.98, 4.79). Second trimester MEP was associated with an increased odds of impaired glucose tolerance (adj. OR: 7.18; 95% CI: 1.97, 26.15). Di-2-ethylhexyl phthalate metabolite concentrations were inversely associated with impaired glucose tolerance (adj. OR: 0.25; adj. 95% CI: 0.08, 0.85). Conclusions Higher exposure to di-ethyl phthalate, the parent compound of MEP, may be associated with excessive GWG and impaired glucose tolerance; higher di-2-ethylhexyl phthalate was associated with reduced odds of impaired glucose tolerance. PMID:27649471

Manifestation of cryptic fibroblast tissue factor occurs at detergent concentrations which dissolve the plasma membrane.

PubMed

Carson, S D

1996-04-01

Cultured fibroblasts treated with increasing concentrations of detergents expressed only encrypted levels of tissue factor activity (measured by fX activation in the presence of fVIIa), characteristic of undamaged cells, until each detergent reached a critical concentration at which the cryptic tissue factor activity was manifested. Beyond the narrow ranges of concentrations over which the detergents stimulated tissue factor activity, the detergents were inhibitory. Studies with Triton X-100 and octyl glucoside revealed that manifestation of tissue factor activity coincided with breakdown of the plasma membrane. The magnitude of the increased tissue factor activity differed among detergents, with octyl glucoside giving the largest response. The tissue factor that was active after Triton X-100 treatment remained mostly associated with the insoluble cell residue, whereas the concentration of octyl glucoside which stimulated activity released tissue factor activity into the supernatant. Radiolabeled antibody against human tissue factor was used to show that a small percentage of the total accessible tissue factor remained in the insoluble fraction after treatment with either non-ionic detergent. Chromatographic analysis of lipids extracted from cells treated with detergents and dansyl chloride showed dansyl-reactivity of phosphatidylserine on intact cells, and solubilization of membrane lipids at sublytic concentrations of detergents. These findings reveal that there is a critical level of detergent-induced membrane damage at which tissue factor activity is maximally expressed, in essentially an all-or-none manner. The results are consistent with a major role for phospholipid asymmetry in regulation of tissue factor specific activity, but require either maintenance of asymmetry during sublytic detergent perturbation of the plasma membrane or additional control mechanisms.

Delayed neuropsychological sequelae after carbon monoxide poisoning: predictive risk factors in the Emergency Department. A retrospective study.

PubMed

Pepe, Giuseppe; Castelli, Matteo; Nazerian, Peiman; Vanni, Simone; Del Panta, Massimo; Gambassi, Francesco; Botti, Primo; Missanelli, Andrea; Grifoni, Stefano

2011-03-17

Delayed neuropsychological sequelae (DNS) commonly occur after recovery from acute carbon monoxide (CO) poisoning. The preventive role and the indications for hyperbaric oxygen therapy in the acute setting are still controversial. Early identification of patients at risk in the Emergency Department might permit an improvement in quality of care. We conducted a retrospective study to identify predictive risk factors for DNS development in the Emergency Department. We retrospectively considered all CO-poisoned patients admitted to the Emergency Department of Careggi University General Hospital (Florence, Italy) from 1992 to 2007. Patients were invited to participate in three follow-up visits at one, six and twelve months from hospital discharge. Clinical and biohumoral data were collected; univariate and multivariate analysis were performed to identify predictive risk factors for DNS. Three hundred forty seven patients were admitted to the Emergency Department for acute CO poisoning from 1992 to 2007; 141/347 patients participated in the follow-up visit at one month from hospital discharge. Thirty four/141 patients were diagnosed with DNS (24.1%). Five/34 patients previously diagnosed as having DNS presented to the follow-up visit at six months, reporting a complete recovery. The following variables (collected before or upon Emergency Department admission) were associated to DNS development at one month from hospital discharge in the univariate analysis: CO exposure duration >6 hours, a Glasgow Coma Scale (GCS) score <9, seizures, systolic blood pressure <90 mmHg, elevated creatine phosphokinase concentration and leukocytosis. There was no significant correlation with age, sex, voluntary exposure, headache, transient loss of consciousness, GCS between 14 and 9, arterial lactate and carboxyhemoglobin concentration. The multivariate analysis confirmed as independent prognostic factors GCS <9 (OR 7.15; CI 95%: 1.04-48.8) and leukocytosis (OR 3.31; CI 95%: 1

Delayed neuropsychological sequelae after carbon monoxide poisoning: predictive risk factors in the Emergency Department. A retrospective study

PubMed Central

2011-01-01

Background Delayed neuropsychological sequelae (DNS) commonly occur after recovery from acute carbon monoxide (CO) poisoning. The preventive role and the indications for hyperbaric oxygen therapy in the acute setting are still controversial. Early identification of patients at risk in the Emergency Department might permit an improvement in quality of care. We conducted a retrospective study to identify predictive risk factors for DNS development in the Emergency Department. Methods We retrospectively considered all CO-poisoned patients admitted to the Emergency Department of Careggi University General Hospital (Florence, Italy) from 1992 to 2007. Patients were invited to participate in three follow-up visits at one, six and twelve months from hospital discharge. Clinical and biohumoral data were collected; univariate and multivariate analysis were performed to identify predictive risk factors for DNS. Results Three hundred forty seven patients were admitted to the Emergency Department for acute CO poisoning from 1992 to 2007; 141/347 patients participated in the follow-up visit at one month from hospital discharge. Thirty four/141 patients were diagnosed with DNS (24.1%). Five/34 patients previously diagnosed as having DNS presented to the follow-up visit at six months, reporting a complete recovery. The following variables (collected before or upon Emergency Department admission) were associated to DNS development at one month from hospital discharge in the univariate analysis: CO exposure duration >6 hours, a Glasgow Coma Scale (GCS) score <9, seizures, systolic blood pressure <90 mmHg, elevated creatine phosphokinase concentration and leukocytosis. There was no significant correlation with age, sex, voluntary exposure, headache, transient loss of consciousness, GCS between 14 and 9, arterial lactate and carboxyhemoglobin concentration. The multivariate analysis confirmed as independent prognostic factors GCS <9 (OR 7.15; CI 95%: 1.04-48.8) and leukocytosis (OR 3

Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation.

PubMed

Zhang, Nan; Huang, Hong; Duan, Xiaoli; Zhao, Jinlong; Su, Boni

2018-06-21

Rapid urbanization is causing serious PM 2.5 (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM 2.5 concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM 2.5 concentration based on more than 1 million PM 2.5 recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM 2.5 concentration, and obtained the 10 primary influencing factors. Data of PM 2.5 concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM 2.5 concentration, while nuclear power generation is the most positive factor in decreasing PM 2.5 concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM 2.5 concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).

Factors associated with blood lead concentrations of children in Jamaica

PubMed Central

RAHBAR, MOHAMMAD H.; SAMMS-VAUGHAN, MAUREEN; DICKERSON, AISHA S.; LOVELAND, KATHERINE A.; ARDJOMAND-HESSABI, MANOUCHEHR; BRESSLER, JAN; SHAKESPEARE-PELLINGTON, SYDONNIE; GROVE, MEGAN L.; BOERWINKLE, ERIC

2015-01-01

Lead is a heavy metal known to be detrimental to neurologic, physiologic, and behavioral health of children. Previous studies from Jamaica reported that mean lead levels in soil are four times that of lead levels in some other parts of the world. Other studies detected lead levels in fruits and root vegetables, which were grown in areas with lead contaminated soil. In this study, we investigate environmental factors associated with blood lead concentrations in Jamaican children. The participants in this study comprised 125 typically developing (TD) children (ages 2–8 years) who served as controls in an age- and sex-matched case-control study that enrolled children from 2009 – 2012 in Jamaica. We administered a questionnaire to assess demographic and socioeconomic information as well as potential exposures to lead through food. Using General Linear Models (GLMs), we identified factors associated with blood lead concentrations in Jamaican children. The geometric mean blood lead concentration (GMBLC) in the sample of children in this study was 2.80 μg/dL. In univariable GLM analyses, GMBLC was higher for children whose parents did not have education beyond high school compared to those whose parents had attained this level (3.00 μg/dL vs. 2.31 μg/dL; P = 0.05), children living near a high traffic road compared to those who did not (3.43 μg/dL vs. 2.52 μg/dL; P < 0.01), and children who reported eating ackee compared to those who did not eat this fruit (2.89 μg/dL vs. 1.65 μg/dL; P < 0.05). In multivariable analysis, living near a high traffic road was identified as an independent risk factor for higher adjusted GMBLC (3.05 μg/dL vs. 2.19 μg/dL; P = 0.01). While our findings indicate that GMBLC in Jamaican children has dropped by at least 62% during the past two decades, children living in Jamaica still have GMBLC that is twice that of children in more developed countries. In addition, we have identified significant risk factors for higher blood lead

Factors predicting survival following noninvasive ventilation in amyotrophic lateral sclerosis.

PubMed

Peysson, S; Vandenberghe, N; Philit, F; Vial, C; Petitjean, T; Bouhour, F; Bayle, J Y; Broussolle, E

2008-01-01

The involvement of respiratory muscles is a major predicting factor for survival in amyotrophic lateral sclerosis (ALS). Recent studies show that noninvasive ventilation (NIV) can relieve symptoms of alveolar hypoventilation. However, factors predicting survival in ALS patients when treated with NIV need to be clarified. We conducted a retrospective study of 33 consecutive ALS patients receiving NIV. Ten patients had bulbar onset. We determined the median survivals from onset, diagnosis and initiation of NIV and factors predicting survival. Statistical analysis was performed using the Kaplan-Meier test and Cox proportional hazard models. The median initial and maximal total uses of NIV were 10 and 14 h/24h. The overall median survival from ALS onset was 34.2 months and worsened with increasing age and bulbar onset of the disease. The median survival from initiation of NIV was 8.4 months and was significantly poorer in patients with advanced age or with airway mucus accumulation. Survival from initiation of NIV was not influenced by respiratory parameters or bulbar symptoms. Advanced age at diagnosis and airway mucus accumulation represent poorer prognostic factors of ALS patients treated with NIV. NIV is a helpful treatment of sleep-disordered breathing, including patients with bulbar involvement. Copyright 2008 S. Karger AG, Basel.

Prediction of beef carcass and meat quality traits from factors characterising the rearing management system applied during the whole life of heifers.

PubMed

Soulat, J; Picard, B; Léger, S; Monteils, V

2018-06-01

In this study, four prediction models were developed by logistic regression using individual data from 96 heifers. Carcass and sensory rectus abdominis quality clusters were identified then predicted using the rearing factors data. The obtained models from rearing factors applied during the fattening period were compared to those characterising the heifers' whole life. The highest prediction power of carcass and meat quality clusters were obtained from the models considering the whole life, with success rates of 62.8% and 54.9%, respectively. Rearing factors applied during both pre-weaning and fattening periods influenced carcass and meat quality. According to models, carcass traits were improved when heifer's mother was older for first calving, calves ingested concentrates during pasture preceding weaning and heifers were slaughtered older. Meat traits were improved by the genetic of heifers' parents (i.e., calving ease and early muscularity) and when heifers were slaughtered older. A management of carcass and meat quality traits is possible at different periods of the heifers' life. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ultrasonication as a potential tool to predict solute crystallization in freeze-concentrates.

PubMed

Ragoonanan, Vishard; Suryanarayanan, Raj

2014-06-01

We hypothesize that ultrasonication can accelerate solute crystallization in freeze-concentrates. Our objective is to demonstrate ultrasonication as a potential predictive tool for evaluating physical stability of excipients in frozen solutions. The crystallization tendencies of lyoprotectants (trehalose, sucrose), carboxylic acid buffers (citric, tartaric, malic, and acetic) and an amino acid buffer (histidine HCl) were studied. Aqueous solutions of buffers, lyoprotectants and mixtures of the two were cooled from room temperature to -20°C and sonicated to induce solute crystallization. The crystallized phases were identified by X-ray diffractometry (laboratory or synchrotron source). Sonication accelerated crystallization of trehalose dihydrate in frozen trehalose solutions. Sonication also enhanced solute crystallization in tartaric (200 mM; pH 5), citric (200 mM pH 4) and malic (200 mM; pH 4) acid buffers. At lower buffer concentrations, longer annealing times following sonication were required to facilitate solute crystallization. The time for crystallization of histidine HCl progressively increased as a function of sucrose concentration. The insonation period required to effect crystallization also increased with sucrose concentration. Sonication can substantially accelerate solute crystallization in the freeze-concentrate. Ultrasonication may be useful in assessing the crystallization tendency of formulation constituents used in long term frozen storage and freeze-drying.

Prediction the concentration of graphite direct exfoliation by liquid solution with solubility parameters map

NASA Astrophysics Data System (ADS)

Liang, Ko-Yuan; Yang, Wein-Duo

2018-01-01

This study is to discuss solvent selection with graphene dispersion concentration of directly exfoliation graphite. That limiting boundaries of fractional cohesion parameters will be draw on the triangular diagram to prediction and estimate. It is based on the literature of data and check with experimental or other literature results, include organic solution, aqueous solution and ionic liquid. In this work, we found that estimated the graphene dispersion concentration by distance (Ra) of Hansen solubility parameters (HSP) between graphene and solvent, the lower Ra; the higher concentration, some case the lower Ra; the lower dispersion concentration (such as acetone). It is compatible with the graphene dispersion concentration on the Hansen space or Triangular fractional cohesion parameters dispersion diagram. From Triangular fractional cohesion parameters dispersion diagram, 2D maps are more convenient for researchers than 3D maps of Hansen space and quickly to find the appropriate combination of solvents for different application.

Hierarchical modeling of indoor radon concentration: how much do geology and building factors matter?

PubMed

Borgoni, Riccardo; De Francesco, Davide; De Bartolo, Daniela; Tzavidis, Nikos

2014-12-01

Radon is a natural gas known to be the main contributor to natural background radiation exposure and only second to smoking as major leading cause of lung cancer. The main concern is in indoor environments where the gas tends to accumulate and can reach high concentrations. The primary contributor of this gas into the building is from the soil although architectonic characteristics, such as building materials, can largely affect concentration values. Understanding the factors affecting the concentration in dwellings and workplaces is important both in prevention, when the construction of a new building is being planned, and in mitigation when the amount of Radon detected inside a building is too high. In this paper we investigate how several factors, such as geologic typologies of the soil and a range of building characteristics, impact on indoor concentration focusing, in particular, on how concentration changes as a function of the floor level. Adopting a mixed effects model to account for the hierarchical nature of the data, we also quantify the extent to which such measurable factors manage to explain the variability of indoor radon concentration. Copyright © 2014 Elsevier Ltd. All rights reserved.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

PubMed

Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

2010-08-01

We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error <3 mg/dL) for prediction horizons of up to 25 min; models based on pairs of bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

Radionuclide concentration processes in marine organisms: A comprehensive review.

PubMed

Carvalho, Fernando P

2018-06-01

The first measurements made of artificial radionuclides released into the marine environment did reveal that radionuclides are concentrated by marine biological species. The need to report radionuclide accumulation in biota in different conditions and geographical areas prompted the use of concentration factors as a convenient way to describe the accumulation of radionuclides in biota relative to radionuclide concentrations in seawater. Later, concentration factors became a tool in modelling radionuclide distribution and transfer in aquatic environments and to predicting radioactivity in organisms. Many environmental parameters can modify the biokinetics of accumulation and elimination of radionuclides in marine biota, but concentration factors remained a convenient way to describe concentration processes of radioactive and stable isotopes in aquatic organisms. Revision of CF values is periodically undertaken by international organizations, such as the International Atomic Energy Agency (IAEA), to make updated information available to the international community. A brief commented review of radionuclide concentration processes and concentration factors in marine organisms is presented for key groups of radionuclides such as fission products, activation products, transuranium elements, and naturally-occurring radionuclides. Copyright © 2017 Elsevier Ltd. All rights reserved.

Factors predicting labor induction success: a critical analysis.

PubMed

Crane, Joan M G

2006-09-01

Because of the risk of failed induction of labor, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labor induction success. Certain characteristics of the woman (including parity, age, weight, height and body mass index), and of the fetus (including birth weight and gestational age) are associated with the success of labor induction; with parous, young women who are taller and lower weight having a higher rate of induction success. Fetuses with a lower birth weight or increased gestational age are also associated with increased induction success. The condition of the cervix at the start of induction is an important predictor, with the modified Bishop score being a widely used scoring system. The most important element of the Bishop score is dilatation. Other predictors, including transvaginal ultrasound (TVUS) and biochemical markers [including fetal fibronectin (fFN)] have been suggested. Meta-analyses of studies identified from MEDLINE, PubMed, and EMBASE and published from 1990 to October 2005 were performed evaluating the use of TVUS and fFN in predicting labor induction success in women at term with singleton gestations. Both TVUS and Bishop score predicted successful induction [likelihood ratio (LR)=1.82, 95% confidence interval (CI)=1.51-2.20 and LR=2.10, 95%CI=1.67-2.64, respectively]. As well, fFN and Bishop score predicted successful induction (LR=1.49, 95%CI=1.20-1.85, and LR=2.62, 95%CI=1.88-3.64, respectively). Although TVUS and fFN predicted successful labor induction, neither has been shown to be superior to Bishop score. Further research is needed to evaluate these potential predictors and insulin-like growth factor binding protein-1 (IGFBP-1), another potential biochemical marker.

Carbide factor predicts rolling-element bearing fatigue life

NASA Technical Reports Server (NTRS)

Chevalier, J. L.; Zaretsky, E. V.

1973-01-01

Analysis was made to determine correlation between number and size of carbide particles and rolling-element fatigue. Correlation was established, and carbide factor was derived that can be used to predict fatigue life more effectively than such variables as heat treatment, chemical composition, and hardening mechanism.

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

Use of watershed factors to predict consumer surfactant risk, water quality, and habitat quality in the upper Trinity River, Texas.

PubMed

Atkinson, S F; Johnson, D R; Venables, B J; Slye, J L; Kennedy, J R; Dyer, S D; Price, B B; Ciarlo, M; Stanton, K; Sanderson, H; Nielsen, A

2009-06-15

Surfactants are high production volume chemicals that are used in a wide assortment of "down-the-drain" consumer products. Wastewater treatment plants (WWTPs) generally remove 85 to more than 99% of all surfactants from influents, but residual concentrations are discharged into receiving waters via wastewater treatment plant effluents. The Trinity River that flows through the Dallas-Fort Worth metropolitan area, Texas, is an ideal study site for surfactants due to the high ratio of wastewater treatment plant effluent to river flow (>95%) during late summer months, providing an interesting scenario for surfactant loading into the environment. The objective of this project was to determine whether surfactant concentrations, expressed as toxic units, in-stream water quality, and aquatic habitat in the upper Trinity River could be predicted based on easily accessible watershed characteristics. Surface water and pore water samples were collected in late summer 2005 at 11 sites on the Trinity River in and around the Dallas-Fort Worth metropolitan area. Effluents of 4 major waste water treatment plants that discharge effluents into the Trinity River were also sampled. General chemistries and individual surfactant concentrations were determined, and total surfactant toxic units were calculated. GIS models of geospatial, anthropogenic factors (e.g., population density) and natural factors (e.g., soil organic matter) were collected and analyzed according to subwatersheds. Multiple regression analyses using the stepwise maximum R(2) improvement method were performed to develop prediction models of surfactant risk, water quality, and aquatic habitat (dependent variables) using the geospatial parameters (independent variables) that characterized the upper Trinity River watershed. We show that GIS modeling has the potential to be a reliable and inexpensive method of predicting water and habitat quality in the upper Trinity River watershed and perhaps other highly urbanized

Elevated CSF Corticotropin-Releasing Factor Concentrations in Posttraumatic Stress Disorder

PubMed Central

Bremner, J. Douglas; Licinio, Julio; Darnell, Adam; Krystal, John H.; Owens, Michael J.; Southwick, Steven M.; Nemeroff, Charles B.; Charney, Dennis S.

2011-01-01

Objective Corticotropin-releasing factor (CRF) and somatostatin both play important roles in mediating responses to acute and chronic stress. The purpose of this study was to measure CSF concentrations of CRF and somatostatin in patients with chronic combat-related post-traumatic stress disorder (PTSD) and comparison subjects. Method Lumbar punctures for collection of CSF were performed in Vietnam combat veterans with PTSD (N=11) and comparison subjects (N=17). CSF concentrations of CRF and somatostatin were compared between the two groups. Results CSF concentrations of CRF were higher in the PTSD patients than in the comparison subjects (mean=29.0 pg/ml, SD=7.8, versus mean=21.9 pg/ml, SD=6.0). This group difference remained significant after covariance for age. CSF somatostatin concentrations in PTSD patients were higher than those of the comparison subjects (mean=19.9 pg/ml, SD=5.4, versus mean=13.7 pg/ml, SD=8.0). However, covarying for age reduced the level of significance. Conclusions Higher CSF CRF concentrations in patients with PTSD may reflect alterations in stress-related neurotransmitter systems. The higher CSF CRF concentrations may play a role in disturbances of arousal in patients with PTSD. PMID:9137116

Early Recurrence After Hepatectomy for Colorectal Liver Metastases: What Optimal Definition and What Predictive Factors?

PubMed Central

Imai, Katsunori; Allard, Marc-Antoine; Benitez, Carlos Castro; Vibert, Eric; Sa Cunha, Antonio; Cherqui, Daniel; Castaing, Denis; Bismuth, Henri; Baba, Hideo

2016-01-01

. Five factors, including age, number of preoperative chemotherapy lines, response to last-line chemotherapy, number of tumors, and carbohydrate antigen 19-9 concentrations, were identified as predictors of early recurrence. Salvage surgery for recurrence significantly improved survival, even in patients with early recurrence. For better selection of patients who could truly benefit from surgery and should also receive strong postoperative chemotherapy, the accurate preoperative prediction of early recurrence is crucial. PMID:27125753

Effects of fertilization, crop year, variety, and provenance factors on mineral concentrations in onions.

PubMed

Ariyama, Kaoru; Nishida, Tadashi; Noda, Tomoaki; Kadokura, Masashi; Yasui, Akemi

2006-05-03

Mineral concentrations of onions (Allium cepa L.) grown under various conditions, including factors (fertilization, crop year, variety, and provenance), were investigated to clarify how much each factor contributes to the variation of their concentrations. This was because the mineral concentrations might be affected by various factors. The ultimate goal of this study was to develop a technique to determine the geographic origins of onions by mineral composition. Samples were onions grown under various conditions at 52 fields in 18 farms in Hokkaido, Japan. Twenty-six elements (Li, Na, Mg, Al, P, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Mo, Cd, Cs, Ba, La, Ce, Nd, Gd, W, and Tl) in these samples were determined by inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry. Fertilization conditions and crop years of onions caused variations of P, Ni, Cu, Rb, Sr, Mo, Cs, and Tl concentrations in onions; different onion varieties also showed variations in numerous element concentrations. However, the variations of mineral compositions of onions by these factors were smaller than the differences between production places with a few exceptions. Furthermore, Na, Rb, and Cs in group IA of the periodic table, Ca, Sr, and Ba in group IIA, and Zn and Cd in group IIB showed similar concentration patterns by group; this result demonstrated that elements in the same periodic groups behaved similarly in terms of their absorption in onions.

Spatial Prediction and Optimized Sampling Design for Sodium Concentration in Groundwater

PubMed Central

Shabbir, Javid; M. AbdEl-Salam, Nasser; Hussain, Tajammal

2016-01-01

Sodium is an integral part of water, and its excessive amount in drinking water causes high blood pressure and hypertension. In the present paper, spatial distribution of sodium concentration in drinking water is modeled and optimized sampling designs for selecting sampling locations is calculated for three divisions in Punjab, Pakistan. Universal kriging and Bayesian universal kriging are used to predict the sodium concentrations. Spatial simulated annealing is used to generate optimized sampling designs. Different estimation methods (i.e., maximum likelihood, restricted maximum likelihood, ordinary least squares, and weighted least squares) are used to estimate the parameters of the variogram model (i.e, exponential, Gaussian, spherical and cubic). It is concluded that Bayesian universal kriging fits better than universal kriging. It is also observed that the universal kriging predictor provides minimum mean universal kriging variance for both adding and deleting locations during sampling design. PMID:27683016

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2

Prediction of Protein Aggregation in High Concentration Protein Solutions Utilizing Protein-Protein Interactions Determined by Low Volume Static Light Scattering.

PubMed

Hofmann, Melanie; Winzer, Matthias; Weber, Christian; Gieseler, Henning

2016-06-01

The development of highly concentrated protein formulations is more demanding than for conventional concentrations due to an elevated protein aggregation tendency. Predictive protein-protein interaction parameters, such as the second virial coefficient B22 or the interaction parameter kD, have already been used to predict aggregation tendency and optimize protein formulations. However, these parameters can only be determined in diluted solutions, up to 20 mg/mL. And their validity at high concentrations is currently controversially discussed. This work presents a μ-scale screening approach which has been adapted to early industrial project needs. The procedure is based on static light scattering to directly determine protein-protein interactions at concentrations up to 100 mg/mL. Three different therapeutic molecules were formulated, varying in pH, salt content, and addition of excipients (e.g., sugars, amino acids, polysorbates, or other macromolecules). Validity of the predicted aggregation tendency was confirmed by stability data of selected formulations. Based on the results obtained, the new prediction method is a promising screening tool for fast and easy formulation development of highly concentrated protein solutions, consuming only microliter of sample volumes. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Prevalence, Incidence, and Factor Concentrate Usage Trends of Hemophiliacs in Taiwan

PubMed Central

Tu, Tsu-Chiang; Liou, Wen-Shyong; Chou, Tsui-Yun; Lin, Tsung-Kun; Lee, Chuan-Fang; Chen, Jye-Daa; Cham, Thau-Ming

2013-01-01

Purpose Hemophilia A and B (HA, HB) are the most common X-linked inherited bleeding disorders. The introduction of factor concentrates has allowed for control of the lifelong chronic disease. However, no studies have been published regarding the epidemiology of hemophilia in Taiwan. Our aim was to determine the prevalence, incidence, and mortality rate, as well as trends in the use of factor concentrates, in individuals with hemophilia in Taiwan. Materials and Methods A retrospective study was conducted using the National Health Insurance Research Database between 1997 and 2007. Results We identified 988 males with hemophilia (HA : HB ratio=5.4 : 1). The mean prevalence per 100000 males was 6.7±0.1 for HA and 1.2±0.1 for HB. The estimated mean annual incidence per live male birth was 1 in 10752 for HA and 1 in 47619 for HB. Standardized mortality ratios for males with hemophilia (all severities) or severe hemophilia were 1.3- and 2.1-fold higher than that of the general male population, respectively. Mean factor VIII (FVIII) and factor IX (FIX) usage was 1.5003±0.4029 and 0.3126±0.0904 international units (IUs) per capita, respectively. Mean FVIII and FIX usage per patient with hemophilia (all severities) or severe hemophilia was 44027±11532 and 72341±17298, respectively, and 49407±13015 and 74369±18411 IUs per person with HA or HB, respectively. Conclusion Our data revealed epidemiologic and factor concentrate usage trends in males with hemophilia in Taiwan, highlighting a need for improvements in the mandatory National Health Insurance registry. A better-designed, patient-centered registry system would enable more detailed patient information collection and analysis, improving subsequent care. PMID:23225801

Predictive factors of dropout from inpatient treatment for anorexia nervosa.

PubMed

Roux, H; Ali, A; Lambert, S; Radon, L; Huas, C; Curt, F; Berthoz, S; Godart, Nathalie

2016-09-30

Patients with severe Anorexia Nervosa (AN) whose condition is life-threatening or who are not receiving adequate ambulatory care are hospitalized. However, 40 % of these patients leave the hospital prematurely, without reaching the target weight set in the treatment plan, and this can compromise outcome. This study set out to explore factors predictive of dropout from hospital treatment among patients with AN, in the hope of identifying relevant therapeutic targets. From 2009 to 2011, 180 women hospitalized for AN (DSM-IV diagnosis) in 10 centres across France were divided into two groups: those under 18 years (when the decision to discharge belongs to the parents) and those aged 18 years and over (when the patient can legally decide to leave the hospital). Both groups underwent clinical assessment using the Morgan & Russell Global Outcome State questionnaire and the Eating Disorders Examination Questionnaire (EDE-Q) for assessment of eating disorder symptoms and outcome. Psychological aspects were assessed via the evaluation of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS). Socio-demographic data were also collected. A number of factors identified in previous research as predictive of dropout from hospital treatment were tested using stepwise descending Cox regressions. We found that factors predictive of dropout varied according to age groups (being under 18 as opposed to 18 and over). For participants under 18, predictive factors were living in a single-parent family, severe intake restriction as measured on the "dietary restriction" subscale of the Morgan & Russell scale, and a low patient-reported score on the EDE-Q "restraint concerns" subscale. For those over 18, dropout was predicted from a low depression score on the HADS, low level of concern about weight on the EDE-Q subscale, and lower educational status. To prevent dropout from hospitalization for AN, the appropriate therapeutic measures vary according to whether

Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

PubMed Central

Siepel, Adam; Lis, John T.

2012-01-01

DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

Prediction of concentration levels of metformin and other high consumption pharmaceuticals in wastewater and regional surface water based on sales data.

PubMed

Oosterhuis, Mathijs; Sacher, Frank; Ter Laak, Thomas L

2013-01-01

Local consumption data of pharmaceuticals were used to study the emission to wastewater and surface waters in two small Dutch water catchments. For nine high consumption pharmaceuticals: metformin, metoprolol, sotalol, losartan, valsartan, irbesartan, hydrochlorothiazide, diclofenac and carbamazepine, predicted emissions were compared to wastewater concentrations, removal in sewage treatment plants and recovery in regional surface water. The study shows that local consumption data can be very useful to select pharmaceuticals for monitoring and to predict wastewater concentrations. Measured influent concentrations were on average 78% with a range of 31-138% of predicted influent concentrations. Metformin is the pharmaceutical with the highest concentration in wastewater (64-98 μg/L) but it is removed with >98% in sewage treatment plants (STP). Guanylurea, a biodegradation product of metformin, was detected in STP effluents and surface waters at concentrations of 39-56 μg/L and 1.8-3.9 μg/L, respectively. The STP removal of the different pharmaceuticals varied strongly. For carbamazepine, hydrochlorothiazide and sotalol a significant better removal was found at higher temperatures and longer hydraulic retention times while for metoprolol significantly better removal was only observed at higher temperatures. Predicting environmental concentrations from regional consumption data might be an alternative to monitoring of pharmaceuticals in wastewater and surface waters. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Predictive factors for moderate or severe exacerbations in asthma patients receiving outpatient care.

PubMed

Gutiérrez, Francisco Javier Álvarez; Galván, Marta Ferrer; Gallardo, Juan Francisco Medina; Mancera, Marta Barrera; Romero, Beatriz Romero; Falcón, Auxiliadora Romero

2017-05-02

Asthma exacerbations are important events that affect disease control, but predictive factors for severe or moderate exacerbations are not known. The objective was to study the predictive factors for moderate (ME) and severe (SE) exacerbations in asthma patients receiving outpatient care. Patients aged > 12 years with asthma were included in the study and followed-up at 4-monthly intervals over a 12-month period. Clinical (severity, level of control, asthma control test [ACT]), atopic, functional, inflammatory, SE and ME parameters were recorded. Univariate analysis was used to compare data from patients presenting at least 1 SE or ME during the follow-up period vs no exacerbations. Statistically significant (p <0.1) factors were then subjected to multiple analysis by binary logistic regression. A total of 330 patients completed the study, most of whom were atopic (76%), women (nearly 70%), with moderate and mild persistent asthma (>80%). Twenty-seven patients (8%) had a SE and 183 had a ME (58.5%) during follow-up. In the case of SEs, the only predictive factor identified in the multiple analysis was previous SE (baseline visit OR 4.218 95% CI 1.53-11.58, 4-month follow-up OR 6.88 95% CI 2.018-23.51) and inhalation technique (OR 3.572 95% CI 1.324-9.638). In the case of MEs, the only predictive factor found in the multiple analysis were previous ME (baseline visit OR 2.90 95% CI 1.54-5.48, 4-month follow- up OR 1.702 95% CI 1.146-2.529). The primary predictive factor for SE or ME is prior SE or ME, respectively. SEs seem to constitute a specific patient "phenotype", in which the sole predictive factor is prior SEs.

Activated sludge pilot plant: comparison between experimental and predicted concentration profiles using three different modelling approaches.

PubMed

Le Moullec, Y; Potier, O; Gentric, C; Leclerc, J P

2011-05-01

This paper presents an experimental and numerical study of an activated sludge channel pilot plant. Concentration profiles of oxygen, COD, NO(3) and NH(4) have been measured for several operating conditions. These profiles have been compared to the simulated ones with three different modelling approaches, namely a systemic approach, CFD and compartmental modelling. For these three approaches, the kinetics model was the ASM-1 model (Henze et al., 2001). The three approaches allowed a reasonable simulation of all the concentration profiles except for ammonium for which the simulations results were far from the experimental ones. The analysis of the results showed that the role of the kinetics model is of primary importance for the prediction of activated sludge reactors performance. The fact that existing kinetics parameters in the literature have been determined by parametric optimisation using a systemic model limits the reliability of the prediction of local concentrations and of the local design of activated sludge reactors. Copyright © 2011 Elsevier Ltd. All rights reserved.

[Predictive factors of the outcomes of prenatal hydronephrosis.

PubMed

Bragagnini, Paolo; Estors, Blanca; Delgado, Reyes; Rihuete, Miguel Ángel; Gracia, Jesús

2016-12-01

To determine prenatal and postnatal independent predictors of poor outcome, spontaneous resolution, or the need for surgery in patients with prenatal hydronephrosis. We performed a retrospective study of patients with prenatal hydronephrosis. The renal pelvis APD was measured in the third prenatal trimester ultrasound, as well as in the first and second postnatal ultrasound. Other variables were taken into account, both prenatal and postnatal. For statistical analysis we used Student t-test, chi-square test, survival analysis, logrank test, and ROC curves. We included 218 patients with 293 renal units (RU). Of these, 147/293 (50.2%) RU were operated. 76/293 (25.9%) RU had spontaneous resolution and other 76/293 (25.9%) RU had poor outcome. As risk factors for surgery we found low birth weight (OR 3.84; 95% CI 1.24-11.84), prematurity (OR 4.17; 95% CI 1.35-12.88), duplication (OR 4.99; 95% CI 2.21-11.23) and the presence of nephrourological underlying pathology (OR 53.54; 95% CI 26.23-109.27). For the non-spontaneous resolution, we found as risk factors the alterations of amniotic fluid volume (RR 1.46; 95% CI 1.33-1.60) as well as the underlying nephrourological pathology and duplication. In the poor outcome, we found as risk factors the alterations of amniotic fluid volume (OR 4.54; 95% CI 1.31-15.62), the presence of nephrourological pathology (OR 4.81 95% CI 2.60-8.89) and RU that was operated (OR 4.23, 95% CI 2.35-7.60). The APD of the renal pelvis in all three ultrasounds were reliable for surgery prediction (area under the curve 0.65; 0.82; 0.71) or spontaneous resolution (area under the curve 0.80; 0.91; 0.80), only the first postnatal ultrasound has predictive value in the poor outcome (area under the curve 0.73). The higher sensitivity and specificity of the APD as predictor value was on the first postnatal ultrasound, 14.60 mm for surgery; 11.35 mm for spontaneous resolution and 15.50 mm for poor outcome. The higher APD in the renal pelvis in any of the

Predicting chemical degradation during storage from two successive concentration ratios: Theoretical investigation.

PubMed

Peleg, Micha; Normand, Mark D

2015-09-01

When a vitamin's, pigment's or other food component's chemical degradation follows a known fixed order kinetics, and its rate constant's temperature-dependence follows a two parameter model, then, at least theoretically, it is possible to extract these two parameters from two successive experimental concentration ratios determined during the food's non-isothermal storage. This requires numerical solution of two simultaneous equations, themselves the numerical solutions of two differential rate equations, with a program especially developed for the purpose. Once calculated, these parameters can be used to reconstruct the entire degradation curve for the particular temperature history and predict the degradation curves for other temperature histories. The concept and computation method were tested with simulated degradation under rising and/or falling oscillating temperature conditions, employing the exponential model to characterize the rate constant's temperature-dependence. In computer simulations, the method's predictions were robust against minor errors in the two concentration ratios. The program to do the calculations was posted as freeware on the Internet. The temperature profile can be entered as an algebraic expression that can include 'If' statements, or as an imported digitized time-temperature data file, to be converted into an Interpolating Function by the program. The numerical solution of the two simultaneous equations requires close initial guesses of the exponential model's parameters. Programs were devised to obtain these initial values by matching the two experimental concentration ratios with a generated degradation curve whose parameters can be varied manually with sliders on the screen. These programs too were made available as freeware on the Internet and were tested with published data on vitamin A. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic thermal analysis of a concentrated photovoltaic system

NASA Astrophysics Data System (ADS)

Avrett, John T., II; Cain, Stephen C.; Pochet, Michael

2012-02-01

Concentrated photovoltaic (PV) technology represents a growing market in the field of terrestrial solar energy production. As the demand for renewable energy technologies increases, further importance is placed upon the modeling, design, and simulation of these systems. Given the U.S. Air Force cultural shift towards energy awareness and conservation, several concentrated PV systems have been installed on Air Force installations across the country. However, there has been a dearth of research within the Air Force devoted to understanding these systems in order to possibly improve the existing technologies. This research presents a new model for a simple concentrated PV system. This model accurately determines the steady state operating temperature as a function of the concentration factor for the optical part of the concentrated PV system, in order to calculate the optimum concentration that maximizes power output and efficiency. The dynamic thermal model derived is validated experimentally using a commercial polysilicon solar cell, and is shown to accurately predict the steady state temperature and ideal concentration factor.

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that

Individual Factors Predicting Mental Health Court Diversion Outcome

ERIC Educational Resources Information Center

Verhaaff, Ashley; Scott, Hannah

2015-01-01

Objective: This study examined which individual factors predict mental health court diversion outcome among a sample of persons with mental illness participating in a postcharge diversion program. Method: The study employed secondary analysis of existing program records for 419 persons with mental illness in a court diversion program. Results:…

Psychosocial Factors Predicting First-Year College Student Success

ERIC Educational Resources Information Center

Krumrei-Mancuso, Elizabeth J.; Newton, Fred B.; Kim, Eunhee; Wilcox, Dan

2013-01-01

This study made use of a model of college success that involves students achieving academic goals and life satisfaction. Hierarchical regressions examined the role of six psychosocial factors for college success among 579 first-year college students. Academic self-efficacy and organization and attention to study were predictive of first semester…

Learning Approaches, Demographic Factors to Predict Academic Outcomes

ERIC Educational Resources Information Center

Nguyen, Tuan Minh

2016-01-01

Purpose: The purpose of this paper is to predict academic outcome in math and math-related subjects using learning approaches and demographic factors. Design/Methodology/Approach: ASSIST was used as the instrumentation to measure learning approaches. The study was conducted in the International University of Vietnam with 616 participants. An…

The Role of Socioeconomic Factors in the Prediction of Persistence in Puerto Rico

ERIC Educational Resources Information Center

Dika, Sandra L.

2014-01-01

While research literature suggests that socioeconomic factors play a role in predicting educational attainment, very little research has been done to examine these relationships using data from Puerto Rico. A logistic regression approach was adopted to investigate the extent to which family and school socioeconomic factors predict retention from…

Factors Predictive of Healing in Large Rotator Cuff Tears: Is It Possible to Predict Retear Preoperatively?

PubMed

Jeong, Ho Yeon; Kim, Hwan Jin; Jeon, Yoon Sang; Rhee, Yong Girl

2018-03-01

Many studies have identified risk factors that cause retear after rotator cuff repair. However, it is still questionable whether retears can be predicted preoperatively. To determine the risk factors related to retear after arthroscopic rotator cuff repair and to evaluate whether it is possible to predict the occurrence of retear preoperatively. Case-control study; Level of evidence, 3. This study enrolled 112 patients who underwent arthroscopic rotator cuff repair with single-row technique for a large-sized tear, defined as a tear with a mediolateral length of 3 to 5 cm. All patients underwent routine magnetic resonance imaging (MRI) at 9 months postoperatively to assess tendon integrity. The sample included 61 patients (54.5%) in the healed group and 51 (45.5%) in the retear group. In multivariate analysis, the independent predictors of retears were supraspinatus muscle atrophy ( P < .001) and fatty infiltration of the infraspinatus ( P = .027), which could be preoperatively measured by MRI. A significant difference was found between the two groups in sex, the acromiohumeral interval, tendon tension, and preoperative or intraoperative mediolateral tear length and musculotendinous junction position in univariate analysis. However, these variables were not independent predictors in multivariate analysis. The cutoff values of occupation ratio of supraspinatus and fatty infiltration of the infraspinatus were 43% and grade 2, respectively. The occupation ratio of supraspinatus <43% and grade ≥2 fatty infiltration of the infraspinatus were the strongest predictors of retear, with an area under the curve of 0.908, sensitivity of 98.0%, and specificity of 83.6% (accuracy = 90.2%). In patients with large rotator cuff tears, it was possible to predict the retear before rotator cuff repair regardless of intraoperative factors. The retear could be predicted most effectively when the occupation ratio of supraspinatus was <43% or the fatty infiltration of infraspinatus was

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

PubMed

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

[Estimation of average traffic emission factor based on synchronized incremental traffic flow and air pollutant concentration].

PubMed

Li, Run-Kui; Zhao, Tong; Li, Zhi-Peng; Ding, Wen-Jun; Cui, Xiao-Yong; Xu, Qun; Song, Xian-Feng

2014-04-01

On-road vehicle emissions have become the main source of urban air pollution and attracted broad attentions. Vehicle emission factor is a basic parameter to reflect the status of vehicle emissions, but the measured emission factor is difficult to obtain, and the simulated emission factor is not localized in China. Based on the synchronized increments of traffic flow and concentration of air pollutants in the morning rush hour period, while meteorological condition and background air pollution concentration retain relatively stable, the relationship between the increase of traffic and the increase of air pollution concentration close to a road is established. Infinite line source Gaussian dispersion model was transformed for the inversion of average vehicle emission factors. A case study was conducted on a main road in Beijing. Traffic flow, meteorological data and carbon monoxide (CO) concentration were collected to estimate average vehicle emission factors of CO. The results were compared with simulated emission factors of COPERT4 model. Results showed that the average emission factors estimated by the proposed approach and COPERT4 in August were 2.0 g x km(-1) and 1.2 g x km(-1), respectively, and in December were 5.5 g x km(-1) and 5.2 g x km(-1), respectively. The emission factors from the proposed approach and COPERT4 showed close values and similar seasonal trends. The proposed method for average emission factor estimation eliminates the disturbance of background concentrations and potentially provides real-time access to vehicle fleet emission factors.

Predicting temporal changes in total iron concentrations in groundwaters flowing from abandoned deep mines: a first approximation

NASA Astrophysics Data System (ADS)

Younger, Paul L.

2000-06-01

Discharges of contaminated groundwater from abandoned deep mines are a major environmental problem in many parts of the world. While process-based models of pollutant generation have been successfully developed for certain surface mines and waste rock piles of relatively simple geometry and limited areal extent, such models are not readily applicable to large systems of laterally extensive, interconnected, abandoned deep mines. As a first approximation for such systems, hydrological and lithological factors, which can reasonably be expected to influence pollutant release, have been assessed by empirically assessing data from 81 abandoned deep coal mine discharges in the UK. These data demonstrate that after flooding of a deep mine is complete and groundwater begins to migrate from the mine voids into surface waters or adjoining aquifers, flushing of the mine voids by fresh recharge results in a gradual improvement in the quality of groundwater (principally manifested as decreasing Fe concentrations and stabilisation of pH around 7). Alternative representations of the flushing process have been examined. While elegant analytical solutions of the advection-dispersion equation can be made to mimic the changes in iron concentration, parameterisation is tendentious in practice. Scrutiny of the UK data suggest that to a first approximation, the duration of the main period of flushing can be predicted to endure around four times as long as the foregoing process of mine flooding. Short- and long-term iron concentrations (i.e. at the start of the main period of flushing and after its completion, respectively) can be estimated from the sulphur content of the worked strata. If strata composition data are unavailable, some indication of pollution potential can be obtained from considerations of the proximity of worked strata to marine beds (which typically have high pyrite contents). The long-term concentrations of iron in a particular discharge can also be approximated on the

Building factorial regression models to explain and predict nitrate concentrations in groundwater under agricultural land

NASA Astrophysics Data System (ADS)

Stigter, T. Y.; Ribeiro, L.; Dill, A. M. M. Carvalho

2008-07-01

SummaryFactorial regression models, based on correspondence analysis, are built to explain the high nitrate concentrations in groundwater beneath an agricultural area in the south of Portugal, exceeding 300 mg/l, as a function of chemical variables, electrical conductivity (EC), land use and hydrogeological setting. Two important advantages of the proposed methodology are that qualitative parameters can be involved in the regression analysis and that multicollinearity is avoided. Regression is performed on eigenvectors extracted from the data similarity matrix, the first of which clearly reveals the impact of agricultural practices and hydrogeological setting on the groundwater chemistry of the study area. Significant correlation exists between response variable NO3- and explanatory variables Ca 2+, Cl -, SO42-, depth to water, aquifer media and land use. Substituting Cl - by the EC results in the most accurate regression model for nitrate, when disregarding the four largest outliers (model A). When built solely on land use and hydrogeological setting, the regression model (model B) is less accurate but more interesting from a practical viewpoint, as it is based on easily obtainable data and can be used to predict nitrate concentrations in groundwater in other areas with similar conditions. This is particularly useful for conservative contaminants, where risk and vulnerability assessment methods, based on assumed rather than established correlations, generally produce erroneous results. Another purpose of the models can be to predict the future evolution of nitrate concentrations under influence of changes in land use or fertilization practices, which occur in compliance with policies such as the Nitrates Directive. Model B predicts a 40% decrease in nitrate concentrations in groundwater of the study area, when horticulture is replaced by other land use with much lower fertilization and irrigation rates.

Concentration of folate in colorectal tissue biopsies predicts prevalence of adenomatous polyps

USDA-ARS?s Scientific Manuscript database

Background and aims: Folate has been implicated as a potential aetiological factor for colorectal cancer. Previous research has not adequately exploited concentrations of folate in normal colonic mucosal biopsies to examine the issue. Methods: Logistic regression models were used to estimate ORs ...

A study of elastic and plastic stress concentration factors due to notches and fillets in flat plates

NASA Technical Reports Server (NTRS)

Hardrath, Herbert F; Ohman, Lachlan

1953-01-01

Six large 24s-t3 aluminum-alloy-sheet specimens containing various notches or fillets were tested in tension to determine their stress concentration factors in both the elastic and plastic ranges. The elastic stress concentration factors were found to be slightly higher than those calculated by Neuber's method and those obtained photoelastically by Frocht. The results showed further that the stress concentration factor decreases as strains at the discontinuity enter the plastic range. A generalization of Stowell's relation for the plastic stress concentration factor at a circular hole in an infinite plate was applied to the specimen shapes tested and gave good agreement with test results.

Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

USGS Publications Warehouse

Nowell, Lisa H.; Crawford, Charles G.; Gilliom, Robert J.; Nakagaki, Naomi; Stone, Wesley W.; Thelin, Gail; Wolock, David M.

2009-01-01

Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992-2001) as part of the U.S. Geological Survey's National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agency's River Reach File 1 and to estimate the probability that whole-fish concentrations exceed benchmarks for protection of fish-eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present-day pesticide concentrations in fish. ?? 2009 SETAC.

Fetal gender prediction based on maternal plasma testosterone and insulin-like peptide 3 concentrations at midgestation and late gestation in cattle.

PubMed

Kibushi, M; Kawate, N; Kaminogo, Y; Hannan, M A; Weerakoon, W W P N; Sakase, M; Fukushima, M; Seyama, T; Inaba, T; Tamada, H

2016-10-15

We compared maternal plasma testosterone and insulin-like peptide 3 (INSL3) concentrations between dams carrying a male versus female fetus from early to late gestation and examined the application of maternal hormonal concentrations to fetal gender prediction in dairy and beef cattle. Blood samples were collected from Holstein cows or heifers (N = 31) and Japanese Black beef cows (N = 33) at 1-month intervals at 2 to 8 months of gestation. Fetal gender was confirmed by visual observation of external genitalia of calves just after birth. Plasma testosterone and INSL3 concentrations were determined by enzyme-immunoassay. Fetal genders were judged based on cutoff values of maternal testosterone and INSL3 concentrations (male, if it was ≥ cutoff value; female, if < cutoff value), which we set for each hormone at each gestational month using receiver operating characteristic curves. Plasma testosterone concentrations were higher for dams with a male fetus than those with a female at 4, 5, 7, and 8 months for the dairy cattle (P < 0.05) and at 4, 5, 6, and 8 months for the beef cows (P < 0.05). Plasma INSL3 concentrations were higher for dams with a male fetus than those with a female at 2 and 6 months for the dairy cattle (P < 0.05) and at 4 to 8 months for the beef cows (P < 0.05). The predictive values and detection rates for fetal gender prediction based on maternal testosterone concentrations were 75.8% to 79.3% for dairy cattle at 5 and 7 months and for beef cows at 5 and 6 months, whereas those values by maternal INSL3 concentrations were 71.0% to 72.4% for the dairy cattle at 6 months and beef cows at 4 and 8 months. When multiple time points of testosterone and INSL3 concentrations at several midgestation and late gestation months were considered for fetal gender prediction, predictive values were 89.3% (5-7 months) and 85.7% to 88.0% (4-6, 8 months) for the dairy and beef breeds, respectively. Maternal testosterone and INSL3

The Style As a Factor of Office Building Concentration Locations in European Cities

NASA Astrophysics Data System (ADS)

Bocian, Anna

2017-03-01

Where should office building concentrations be located in cities? What kind of factors has an influence on its locations? The aim of the research is to examine factors of office locations in cities. Selected office building concentrations in European cities were investigated as case studies. The research method was the spatial decision paradigm. The style, one of the main elements of the paradigm, was selected to answer the research question. The style was defined a composition of existing urban structures. Basic elements of urban composition in selected European cities were examined closely. Research results are conditions of office building concentration locations in European cities in term of urban composition. Such knowledge should be a base of decision-making processe during preparing master plans and city development plans.

Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

PubMed

Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

2016-09-01

A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

Predictive factors for red blood cell transfusion in children undergoing noncomplex cardiac surgery.

PubMed

Mulaj, Muj; Faraoni, David; Willems, Ariane; Sanchez Torres, Cristel; Van der Linden, Philippe

2014-08-01

Red blood cell (RBC) transfusion is frequently required in pediatric cardiac surgery and is associated with altered outcome and increased costs. Determining which factors predict transfusion in this context will enable clinicians to adopt strategies that will reduce the risk of RBC transfusion. This study aimed to assess predictive factors associated with RBC transfusion in children undergoing low-risk cardiac surgery with cardiopulmonary bypass (CPB). Children undergoing surgery to repair ventricular septal defect or atrioventricular septal defect from 2006 to 2011 were included in this retrospective study. Demography, preoperative laboratory testing, intraoperative data, and RBC transfusion were reviewed. Univariate and multivariate logistic regression analysis were used to define factors that were able to predict RBC transfusion. Then, we employed receiver operating characteristic analysis to design a predictive score. Among the 334 children included, 261 (78%) were transfused. Age (<18 months), priming volume of the CPB (>43 mL/kg), type of oxygenator used, minimal temperature reached during CPB (<32°C), and preoperative hematocrit (<34%) were independently associated with RBC transfusion in the studied population. A predictive score 2 or greater was the best predictor of RBC transfusion. The present study identified several factors that were significantly associated with perioperative RBC transfusion. Based on these factors, we designed a predictive score that can be used to develop a patient-based blood management program with the aim of reducing the incidence of RBC transfusion. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed Central

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-01-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 ± 9.2 and 14.1 ± 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 ± 6.9 and 14.2 ± 8.2 mL s−1 in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi. PMID:19966831

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

Fibrinogen concentration and its role in CVD risk in black South Africans--effect of urbanisation.

PubMed

Pieters, Marlien; de Maat, Moniek P M; Jerling, Johann C; Hoekstra, Tiny; Kruger, Annamarie

2011-09-01

The aim of this study was to investigate correlates of fibrinogen concentration in black South Africans, as well as its association with cardiovascular disease (CVD) risk and whether urbanisation influences this association. A total of 1,006 rural and 1,004 urban black South Africans from the PURE study were cross-sectionally analysed. The association of fibrinogen with CVD risk was determined by investigating the association of fibrinogen with other CVD risk markers as well as with predicted CVD risk using the Reynolds Risk score. The rural group had a significantly higher fibrinogen concentration than the urban group, despite higher levels of risk factors and increased predicted CVD risk in the urban group. Increased levels of CVD risk factors were, however, still associated with increased fibrinogen concentration. Fibrinogen correlated significantly, but weakly, with overall predicted CVD risk. This correlation was stronger in the urban than in the rural group. Multiple regression analysis showed that a smaller percentage of the variance in fibrinogen is explained by the traditional CVD risk factors in the rural than in the urban group. In conclusion, fibrinogen is weakly associated with CVD risk (predicted overall risk as well with individual risk factors) in black South Africans, and is related to the degree of urbanisation. Increased fibrinogen concentration, in black South Africans, especially in rural areas, is largely unexplained, and likely not strongly correlated with traditional CVD-related lifestyle and pathophysiological processes. This does, however, not exclude the possibility that once increased, the fibrinogen concentration contributes to future development of CVD.

Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive impairment.

PubMed

Tabaton, Massimo; Odetti, Patrizio; Cammarata, Sergio; Borghi, Roberta; Monacelli, Fiammetta; Caltagirone, Carlo; Bossù, Paola; Buscema, Massimo; Grossi, Enzo

2010-01-01

The search for markers that are able to predict the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is crucial for early mechanistic therapies. Using artificial neural networks (ANNs), 22 variables that are known risk factors of AD were analyzed in 80 patients with aMCI, for a period spanning at least 2 years. The cases were chosen from 195 aMCI subjects recruited by four Italian Alzheimer's disease units. The parameters of glucose metabolism disorder, female gender, and apolipoprotein E epsilon3/epsilon4 genotype were found to be the biological variables with high relevance for predicting the conversion of aMCI. The scores of attention and short term memory tests also were predictors. Surprisingly, the plasma concentration of amyloid-beta (42) had a low predictive value. The results support the utility of ANN analysis as a new tool in the interpretation of data from heterogeneous and distinct sources.

Remission of Intermediate Uveitis: Incidence and Predictive Factors.

PubMed

Kempen, John H; Gewaily, Dina Y; Newcomb, Craig W; Liesegang, Teresa L; Kaçmaz, R Oktay; Levy-Clarke, Grace A; Nussenblatt, Robert B; Rosenbaum, James T; Sen, H Nida; Suhler, Eric B; Thorne, Jennifer E; Foster, C Stephen; Jabs, Douglas A; Payal, Abhishek; Fitzgerald, Tonetta D

2016-04-01

To evaluate the incidence of remission among patients with intermediate uveitis; to identify factors potentially predictive of remission. Retrospective cohort study. Involved eyes of patients with primary noninfectious intermediate uveitis at 4 academic ocular inflammation subspecialty practices, followed sufficiently long to meet the remission outcome definition, were studied retrospectively by standardized chart review data. Remission of intermediate uveitis was defined as a lack of inflammatory activity at ≥2 visits spanning ≥90 days in the absence of any corticosteroid or immunosuppressant medications. Factors potentially predictive of intermediate uveitis remission were evaluated using survival analysis. Among 849 eyes (of 510 patients) with intermediate uveitis followed over 1934 eye-years, the incidence of intermediate uveitis remission was 8.6/100 eye-years (95% confidence interval [CI], 7.4-10.1). Factors predictive of disease remission included prior pars plana vitrectomy (PPV) (hazard ratio [HR] [vs no PPV] = 2.39; 95% CI, 1.42-4.00), diagnosis of intermediate uveitis within the last year (HR [vs diagnosis >5 years ago] =3.82; 95% CI, 1.91-7.63), age ≥45 years (HR [vs age <45 years] = 1.79; 95% CI, 1.03-3.11), female sex (HR = 1.61; 95% CI, 1.04-2.49), and Hispanic race/ethnicity (HR [vs white race] = 2.81; 95% CI, 1.23-6.41). Presence/absence of a systemic inflammatory disease, laterality of uveitis, and smoking status were not associated with differential incidence. Our results suggest that intermediate uveitis is a chronic disease with an overall low rate of remission. Recently diagnosed patients and older, female, and Hispanic patients were more likely to remit. With regard to management, pars plana vitrectomy was associated with increased probability of remission. Copyright © 2016 Elsevier Inc. All rights reserved.

Remission of Intermediate Uveitis: Incidence and Predictive Factors

PubMed Central

Kempen, John H.; Gewaily, Dina Y.; Newcomb, Craig W.; Liesegang, Teresa L.; Kaçmaz, R. Oktay; Levy-Clarke, Grace A.; Nussenblatt, Robert B.; Rosenbaum, James T.; Sen, H. Nida; Suhler, Eric B.; Thorne, Jennifer E.; Foster, C. Stephen; Jabs, Douglas A.; Payal, Abhishek; Fitzgerald, Tonetta D.

2016-01-01

Purpose To evaluate the incidence of remission among patients with intermediate uveitis; to identify factors potentially predictive of remission. Design Retrospective cohort study. Methods Involved eyes of patients with primary non-infectious intermediate uveitis at 4 academic ocular inflammation subspecialty practices, followed sufficiently long to meet the remission outcome definition, were studied retrospectively by standardized chart review data. Remission of intermediate uveitis was defined as a lack of inflammatory activity at ≥2 visits spanning ≥90 days in the absence of any corticosteroid or immunosuppressant medications. Factors potentially predictive of intermediate uveitis remission were evaluated using survival analysis. Results Among 849 eyes (of 510 patients) with intermediate uveitis followed over 1,934 eye-years, the incidence of intermediate uveitis remission was 8.6/100 eye-years (95% confidence interval (CI), 7.4–10.1). Factors predictive of disease remission included prior pars plana vitrectomy (PPV) (HR (vs. no PPV)=2.39; 95% CI, 1.42–4.00), diagnosis of intermediate uveitis within the last year (vs. diagnosis >5 years ago)=3.82; 95% CI, 1.91–7.63), age ≥45 years (HR (vs. age <45 years)=1.79; 95% CI, 1.03–3.11), female sex (HR=1.61; 95% CI, 1.04–2.49), and Hispanic race/ethnicity (HR (vs. white race)=2.81; 95% CI, 1.23–6.41). Presence/absence of a systemic inflammatory disease, laterality of uveitis, and smoking status were not associated with differential incidence. Conclusions Our results suggest that intermediate uveitis is a chronic disease with an overall low rate of remission. Recently diagnosed cases, and older, female and Hispanic cases were more likely to remit. With regards to management, pars plana vitrectomy was associated with increased probability of remission. PMID:26772874

Predictive factors for pharyngocutaneous fistulization after total laryngectomy: a Dutch Head and Neck Society audit.

PubMed

Lansaat, Liset; van der Noort, Vincent; Bernard, Simone E; Eerenstein, Simone E J; Plaat, Boudewijn E C; Langeveld, Ton A P M; Lacko, Martin; Hilgers, Frans J M; de Bree, Remco; Takes, Robert P; van den Brekel, Michiel W M

2018-03-01

Incidences of pharyngocutaneous fistulization (PCF) after total laryngectomy (TL) reported in the literature vary widely, ranging from 2.6 to 65.5%. Comparison between different centers might identify risk factors, but also might enable improvements in quality of care. To enable this on a national level, an audit in the 8 principle Dutch Head and Neck Centers (DHNC) was initiated. A retrospective chart review of all 324 patients undergoing laryngectomy in a 2-year (2012 and 2013) period was performed. Overall PCF%, PCF% per center and factors predictive for PCF were identified. Furthermore, a prognostic model predicting the PCF% per center was developed. To provide additional data, a survey among the head and neck surgeons of the participating centers was carried out. Overall PCF% was 25.9. The multivariable prediction model revealed that previous treatment with (chemo)radiotherapy in combination with a long interval between primary treatment and TL, previous tracheotomy, near total pharyngectomy, neck dissection, and BMI < 18 were the best predictors for PCF. Early oral intake did not influence PCF rate. PCF% varied quite widely between centers, but for a large extend this could be explained with the prediction model. PCF performance rate (difference between the PCF% and the predicted PCF%) per DHNC, though, shows that not all differences are explained by factors established in the prediction model. However, these factors explain enough of the differences that, compensating for these factors, hospital is no longer independently predictive for PCF. This nationwide audit has provided valid comparative PCF data confirming the known risk factors from the literature which are important for counseling on PCF risks. Data show that variations in PCF% in the DHNCs (in part) are explainable by the variations in these predictive factors. Since elective neck dissection is a major risk factor for PCF, it only should be performed on well funded indication.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

PubMed Central

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

Factors Affecting 25-Hydroxyvitamin D Concentration in Response to Vitamin D Supplementation

PubMed Central

Mazahery, Hajar; von Hurst, Pamela R.

2015-01-01

Sun exposure is the main source of vitamin D. Due to many lifestyle risk factors vitamin D deficiency/insufficiency is becoming a worldwide health problem. Low 25(OH)D concentration is associated with adverse musculoskeletal and non-musculoskeletal health outcomes. Vitamin D supplementation is currently the best approach to treat deficiency and to maintain adequacy. In response to a given dose of vitamin D, the effect on 25(OH)D concentration differs between individuals, and it is imperative that factors affecting this response be identified. For this review, a comprehensive literature search was conducted to identify those factors and to explore their significance in relation to circulating 25(OH)D response to vitamin D supplementation. The effect of several demographic/biological factors such as baseline 25(OH)D, aging, body mass index(BMI)/body fat percentage, ethnicity, calcium intake, genetics, oestrogen use, dietary fat content and composition, and some diseases and medications has been addressed. Furthermore, strategies employed by researchers or health care providers (type, dose and duration of vitamin D supplementation) and environment (season) are other contributing factors. With the exception of baseline 25(OH)D, BMI/body fat percentage, dose and type of vitamin D, the relative importance of other factors and the mechanisms by which these factors may affect the response remains to be determined. PMID:26121531

Profiling healthy eaters. Determining factors that predict healthy eating practices among Dutch adults.

PubMed

Swan, Emily; Bouwman, Laura; Hiddink, Gerrit Jan; Aarts, Noelle; Koelen, Maria

2015-06-01

Research has identified multiple factors that predict unhealthy eating practices. However what remains poorly understood are factors that promote healthy eating practices. This study aimed to determine a set of factors that represent a profile of healthy eaters. This research applied Antonovsky's salutogenic framework for health development to examine a set of factors that predict healthy eating in a cross-sectional study of Dutch adults. Data were analyzed from participants (n = 703) who completed the study's survey in January 2013. Logistic regression analysis was performed to test the association of survey factors on the outcome variable high dietary score. In the multivariate logistic regression model, five factors contributed significantly (p < .05) to the predictive ability of the overall model: being female; living with a partner; a strong sense of coherence (construct from the salutogenic framework), flexible restraint of eating, and self-efficacy for healthy eating. Findings complement what is already known of the factors that relate to poor eating practices. This can provide nutrition promotion with a more comprehensive picture of the factors that both support and hinder healthy eating practices. Future research should explore these factors to better understand their origins and mechanisms in relation to healthy eating practices. Copyright © 2015 Elsevier Ltd. All rights reserved.

Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data

USGS Publications Warehouse

Veltman, K.; Huijbregts, M.A.J.; Vijver, M.G.; Peijnenburg, W.J.G.M.; Hobbelen, P.H.F.; Koolhaas, J.E.; van Gestel, C.A.M.; van Vliet, P.C.J.; Jan, Hendriks A.

2007-01-01

The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope < 1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper. ?? 2006 Elsevier Ltd. All rights reserved.

Predictive and Prognostic Factors in Ovarian and Uterine Carcinosarcomas

PubMed Central

Cicin, İrfan; Özatlı, Tahsin; Türkmen, Esma; Özturk, Türkan; Özçelik, Melike; Çabuk, Devrim; Gökdurnalı, Ayşe; Balvan, Özlem; Yıldız, Yaşar; Şeker, Metin; Özdemir, Nuriye; Yapar, Burcu; Tanrıverdi, Özgür; Günaydin, Yusuf; Menekşe, Serkan; Öksüzoğlu, Berna; Aksoy, Asude; Erdogan, Bülent; Bekir Hacıoglu, M.; Arpaci, Erkan; Sevinç, Alper

2016-01-01

Background: Prognostic factors and the standard treatment approach for gynaecological carcinosarcomas have not yet been clearly defined. Although carcinosarcomas are more aggressive than pure epithelial tumours, they are treated similarly. Serous/clear cell and endometrioid components may be predictive factors for the efficacy of adjuvant chemotherapy (CT) or radiotherapy (RT) or RT in patients with uterine and ovarian carcinosarcomas. Heterologous carcinosarcomas may benefit more from adjuvant CT. Aims: We aimed to define the prognostic and predictive factors associated with treatment options in ovarian (OCS) and uterine carcinosarcoma (UCS). Study Design: Retrospective cross-sectional study Methods: We retrospectively reviewed the medical records of patients with ovarian and uterine carcinosarcoma from 2000 to 2013, and 127 women were included in this study (24 ovarian and 103 uterine). Patients admitted to seventeen oncology centres in Turkey between 2000 and December 2013 with a histologically proven diagnosis of uterine carcinosarcoma with FIGO 2009 stage I–III and patients with sufficient data obtained from well-kept medical records were included in this study. Stage IV tumours were excluded. The patient records were retrospectively reviewed. Data from 104 patients were evaluated for this study. Results: Age (≥70 years) was a poor prognostic factor for UCS (p=0.036). Pelvic±para aortic lymph node dissection did not affect overall survival (OS) (p=0.35). Macroscopic residual disease was related with OS (p<0.01). The median OS was significantly longer in stage I–II patients than stage III patients (p=0.03). Adjuvant treatment improved OS (p=0.013). Adjuvant radiotherapy tended to increase the median OS (p=0.075). However, this tendency was observed in UCS (p=0.08) rather than OCS (p=0.6).Adjuvant chemotherapy had no effect on OS (p=0.15).Adjuvant radiotherapy significantly prolonged the median OS in patients with endometrioid component (p=0.034). A

Aggressive behavior and change in salivary testosterone concentrations predict willingness to engage in a competitive task.

PubMed

Carré, Justin M; McCormick, Cheryl M

2008-08-01

The current study investigated relationships among aggressive behavior, change in salivary testosterone concentrations, and willingness to engage in a competitive task. Thirty-eight male participants provided saliva samples before and after performing the Point Subtraction Aggression Paradigm (a laboratory measure that provides opportunity for aggressive and defensive behavior while working for reward; all three involve pressing specific response keys). Baseline testosterone concentrations were not associated with aggressive responding. However, aggressive responding (but not point reward or point protection responding) predicted the pre- to post-PSAP change in testosterone: Those with the highest aggressive responding had the largest percent increase in testosterone concentrations. Together, aggressive responding and change in testosterone predicted willingness to compete following the PSAP. Controlling for aggression, men who showed a rise in testosterone were more likely to choose to compete again (p=0.03) and controlling for testosterone change, men who showed the highest level of aggressive responding were more likely to choose the non-competitive task (p=0.02). These results indicate that situation-specific aggressive behavior and testosterone responsiveness are functionally relevant predictors of future social behavior.

Factors affecting pollutant concentrations in the near-road environment

NASA Astrophysics Data System (ADS)

Baldwin, Nichole; Gilani, Owais; Raja, Suresh; Batterman, Stuart; Ganguly, Rajiv; Hopke, Philip; Berrocal, Veronica; Robins, Thomas; Hoogterp, Sarah

2015-08-01

An improved understanding of traffic-related air pollutants is needed to estimate exposures and adverse health impacts in traffic corridors and near-road environments. In this study, concentrations of black carbon (BC), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and particulate matter (PM2.5, PM10, ultrafine particles, and accumulation mode particles, AMP) were measured using a mobile air pollutant laboratory along nine transects across major roads in Detroit, MI in winter 2012. Repeated measurements were taken during rush-hour periods at sites in residential neighborhoods located 50-500 m from both sides of the road. Concentration gradients attributable to on-road emissions were estimated by accounting for traffic volume and mix, wind speed, wind direction, and background concentrations. BC, NO, NOx, and UFP had the strongest gradients, and elevated concentrations of NOx, NO2, PM2.5 and PM10, as well as decreased particle size, were found at the 50 m sites compared to background levels. Exponential models incorporating effects of road size, wind speed, and up- and downwind distance explained from 31 to 53% of the variability in concentration gradients for BC, NO, NOx, UFP and particle size. The expected concentration increments 50 m from the study roads were 17.0 ppb for NO, 17.7 ppb for NOx, 2245 particles/cm3 for UFP, and 0.24 μg/m3 for BC, and the expected distance to decrease increments by half was 89-129 m in the downwind direction, and 14-20 m in the upwind direction. While accounting for portion of the temporal and spatial variability across transects and measurement periods, these results highlight the influence of road-to-road differences and other locally-varying factors important in urban and industrial settings. The study demonstrates a methodology to quantify near-road concentrations and influences on these concentrations while accounting for temporal and spatial variability, and it provides information useful for estimating exposures of

Prediction and validation of concentration gradient generation in a paper-based microfluidic channel

NASA Astrophysics Data System (ADS)

Jang, Ilhoon; Kim, Gang-June; Song, Simon

2016-11-01

A paper-based microfluidic channel has obtained attention as a diagnosis device that can implement various chemical or biological reactions. With benefits of thin, flexible, and strong features of paper devices, for example, it is often utilized for cell culture where controlling oxygen, nutrients, metabolism, and signaling molecules gradient affects the growth and movement of the cells. Among various features of paper-based microfluidic devices, we focus on establishment of concentration gradient in a paper channel. The flow is subject to dispersion and capillary effects because a paper is a porous media. In this presentation, we describe facile, fast and accurate method of generating a concentration gradient by using flow mixing of different concentrations. Both theoretical prediction and experimental validation are discussed along with inter-diffusion characteristics of porous flows. This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korea government(MSIP) (No. 2016R1A2B3009541).

Angiogenic factors combined with clinical risk factors to predict preterm pre-eclampsia in nulliparous women: a predictive test accuracy study.

PubMed

Myers, J E; Kenny, L C; McCowan, L M E; Chan, E H Y; Dekker, G A; Poston, L; Simpson, N A B; North, R A

2013-09-01

To assess the performance of clinical risk factors, uterine artery Doppler and angiogenic markers to predict preterm pre-eclampsia in nulliparous women. Predictive test accuracy study. Prospective multicentre cohort study Screening for Pregnancy Endpoints (SCOPE). Low-risk nulliparous women with a singleton pregnancy were recruited. Clinical risk factor data were obtained and plasma placental growth factor (PlGF), soluble endoglin and soluble fms-like tyrosine kinase-1 (sFlt-1) were measured at 14-16 weeks of gestation. Prediction models were developed using multivariable stepwise logistic regression. Preterm pre-eclampsia (delivered before 37(+0)  weeks of gestation). Of the 3529 women recruited, 187 (5.3%) developed pre-eclampsia of whom 47 (1.3%) delivered preterm. Controls (n = 188) were randomly selected from women without preterm pre-eclampsia and included women who developed other pregnancy complications. An area under a receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.67-0.84) was observed using previously reported clinical risk variables. The AUC improved following the addition of PlGF measured at 14-16 weeks (0.84; 95% CI 0.77-0.91), but no further improvement was observed with the addition of uterine artery Doppler or the other angiogenic markers. A sensitivity of 45% (95% CI 0.31-0.59) (5% false-positive rate) and post-test probability of 11% (95% CI 9-13) were observed using clinical risk variables and PlGF measurement. Addition of plasma PlGF at 14-16 weeks of gestation to clinical risk assessment improved the identification of nulliparous women at increased risk of developing preterm pre-eclampsia, but the performance is not sufficient to warrant introduction as a clinical screening test. These findings are marker dependent, not assay dependent; additional markers are needed to achieve clinical utility. © 2013 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2013 RCOG.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

PubMed

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion

Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

Treesearch

Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

2004-01-01

To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

Point Spectroscopy System for Noncontact and Noninvasive Prediction of Transcutaneous Bilirubin Concentration

NASA Astrophysics Data System (ADS)

Ong, P. E.; K. C Huong, Audrey

2017-08-01

This paper presents the use of a point spectroscopy system to determine one’s transcutaneous bilirubin level using Modified Lambert Beer model and the developed fitting routine. This technique required a priori knowledge of extinction coefficient of bilirubin and hemoglobin components in the wavelength range of 440-500 nm for the prediction of the required parameter value. This work was conducted on different skin sites of six healthy Asians namely on the thenar region of the palm of their hand, back of the hand, posterior and anterior forearm. The obtained results revealed the lowest mean transcutaneous bilirubin concentration of 0.44±0.3 g/l predicted for palm site while the highest bilirubin level of 0.98±0.2 g/l was estimated for posterior forearm. These values were also compared with that presented in the literature. This study found considerably good consistency in the value predicted for different subjects especially at the thenar region of the palm. This work concluded that the proposed system and technique may be suitably served as an alternative means to noncontact and noninvasive measurement of one’s transcutaneous bilirubin level at palm site.

Influence of physical and chemical properties of HTSXT-FTIR samples on the quality of prediction models developed to determine absolute concentrations of total proteins, carbohydrates and triglycerides: a preliminary study on the determination of their absolute concentrations in fresh microalgal biomass.

PubMed

Serrano León, Esteban; Coat, Rémy; Moutel, Benjamin; Pruvost, Jérémy; Legrand, Jack; Gonçalves, Olivier

2014-11-01

Absolute concentrations of total macromolecules (triglycerides, proteins and carbohydrates) in microorganisms can be rapidly measured by FTIR spectroscopy, but caution is needed to avoid non-specific experimental bias. Here, we assess the limits within which this approach can be used on model solutions of macromolecules of interest. We used the Bruker HTSXT-FTIR system. Our results show that the solid deposits obtained after the sampling procedure present physical and chemical properties that influence the quality of the absolute concentration prediction models (univariate and multivariate). The accuracy of the models was degraded by a factor of 2 or 3 outside the recommended concentration interval of 0.5-35 µg spot(-1). Change occurred notably in the sample hydrogen bond network, which could, however, be controlled using an internal probe (pseudohalide anion). We also demonstrate that for aqueous solutions, accurate prediction of total carbohydrate quantities (in glucose equivalent) could not be made unless a constant amount of protein was added to the model solution (BSA). The results of the prediction model for more complex solutions, here with two components: glucose and BSA, were very encouraging, suggesting that this FTIR approach could be used as a rapid quantification method for mixtures of molecules of interest, provided the limits of use of the HTSXT-FTIR method are precisely known and respected. This last finding opens the way to direct quantification of total molecules of interest in more complex matrices.

Adolescent Alcohol Use: Protective and Predictive Parent, Peer, and Self-Related Factors

PubMed Central

Donaldson, Candice D.; Crano, William D.

2018-01-01

Adolescent alcohol use has been linked with a multitude of problems and a trajectory predictive of problematic use in adulthood. Thus, targeting factors that enhance early prevention efforts is vital. The current study highlights variables that mitigate or predict alcohol use and heavy episodic drinking. Using Monitoring the Future (MTF) data, multiple path analytic models revealed links between parental involvement and alcohol abstinence and initiation. Parental involvement predicted enhanced self-esteem and less self-derogation and was negatively associated with peer alcohol norms for each MTF grade sampled, with stronger associations for 8th and 10th graders than 12th graders. For younger groups, self-esteem predicted increased perceptions of alcohol risk and reduced drinking. Self-derogation was associated with peers’ pro-alcohol norms, which was linked to lower risk perceptions, lower personal disapproval of use, and increased drinking. Peer influence had a stronger association with consumption for 8th and 10th graders, whereas 12th graders’ drinking was related to personal factors of alcohol risk perception and disapproval. In all grades, general alcohol use had a strong connection to heavy episodic drinking within the past 2 weeks. Across-grade variations in association of parent, peer, and personal factors suggest the desirability of tailored interventions focused on specific factors for each grade level, with the overall goal of attenuating adolescent alcohol use. PMID:27562038

Clinical Prediction Making: Examining Influential Factors Related to Clinician Predictions of Recidivism among Juvenile Offenders

ERIC Educational Resources Information Center

Calley, Nancy G.; Richardson, Emily M.

2011-01-01

This study examined factors influencing clinician predictions of recidivism for juvenile offenders, including youth age at initial juvenile justice system involvement, youth age at discharge, program completion status, clinician perception of strength of the therapeutic relationship, and clinician perception of youth commitment to treatment.…

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

PubMed

Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

2010-07-01

The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. (c) 2010 SETAC.

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

PubMed Central

Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M. G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public. PMID:23291550

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

USGS Publications Warehouse

Francy, Donna S.; Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M.G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection.

PubMed

Francy, Donna S; Stelzer, Erin A; Duris, Joseph W; Brady, Amie M G; Harrison, John H; Johnson, Heather E; Ware, Michael W

2013-03-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Concentrating phenolic acids from Lonicera japonica by nanofiltration technology

NASA Astrophysics Data System (ADS)

Li, Cunyu; Ma, Yun; Li, Hongyang; Peng, Guoping

2017-03-01

Response surface analysis methodology was used to optimize the concentrate process of phenolic acids from Lonicera japonica by nanofiltration technique. On the basis of the influences of pressure, temperature and circulating volume, the retention rate of neochlorogenic acid, chlorogenic acid and 4-dicaffeoylquinic acid were selected as index, molecular weight cut-off of nanofiltration membrane, concentration and pH were selected as influencing factors during concentrate process. The experiment mathematical model was arranged according to Box-Behnken central composite experiment design. The optimal concentrate conditions were as following: nanofiltration molecular weight cut-off, 150 Da; solutes concentration, 18.34 µg/mL; pH, 4.26. The predicted value of retention rate was 97.99% under the optimum conditions, and the experimental value was 98.03±0.24%, which was in accordance with the predicted value. These results demonstrate that the combination of Box-Behnken design and response surface analysis can well optimize the concentrate process of Lonicera japonica water-extraction by nanofiltration, and the results provide the basis for nanofiltration concentrate for heat-sensitive traditional Chinese medicine.

Influence of indoor environmental factors on mass transfer parameters and concentrations of semi-volatile organic compounds.

PubMed

Wei, Wenjuan; Mandin, Corinne; Ramalho, Olivier

2018-03-01

Semi-volatile organic compounds (SVOCs) in indoor environments can partition among the gas phase, airborne particles, settled dust, and available surfaces. The mass transfer parameters of SVOCs, such as the mass transfer coefficient and the partition coefficient, are influenced by indoor environmental factors. Subsequently, indoor SVOC concentrations and thus occupant exposure can vary depending on environmental factors. In this review, the influence of six environmental factors, i.e., indoor temperature, humidity, ventilation, airborne particle concentration, source loading factor, and reactive chemistry, on the mass transfer parameters and indoor concentrations of SVOCs was analyzed and tentatively quantified. The results show that all mass transfer parameters vary depending on environmental factors. These variations are mostly characterized by empirical equations, particularly for humidity. Theoretical calculations of these parameters based on mass transfer mechanisms are available only for the emission of SVOCs from source surfaces when airborne particles are not present. All mass transfer parameters depend on the temperature. Humidity influences the partition of SVOCs among different phases and is associated with phthalate hydrolysis. Ventilation has a combined effect with the airborne particle concentration on SVOC emission and their mass transfer among different phases. Indoor chemical reactions can produce or eliminate SVOCs slowly. To better model the dynamic SVOC concentration indoors, the present review suggests studying the combined effect of environmental factors in real indoor environments. Moreover, interactions between indoor environmental factors and human activities and their influence on SVOC mass transfer processes should be considered. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise.

PubMed

Ražanskas, Petras; Verikas, Antanas; Olsson, Charlotte; Viberg, Per-Arne

2015-08-19

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R(2) = 0:77 to R(2) = 0:98 (for blood lactate) and from R(2) = 0:81 to R(2) = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

MEASURED CONCENTRATIONS OF HERBICIDES AND MODEL PREDICTIONS OF ATRAZINE FATE IN THE PATUXENT RIVER ESTUARY

EPA Science Inventory

McConnell, Laura L., Jennifer A. Harman-Fetcho and James D. Hagy, III. 2004. Measured Concentrations of Herbicides and Model Predictions of Atrazine Fate in the Patuxent River Estuary. J. Environ. Qual. 33(2):594-604. (ERL,GB X1051).

The environmental fate of herbicides i...

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

PubMed

Mbeutcha, Aurélie; Mathieu, Romain; Rouprêt, Morgan; Gust, Kilian M; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2016-10-01

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, C. C., Jr.

1988-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 to + or - 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was developed to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Strengths of specimens containing crack-like slits were calculated from predicted failing strains using uniaxial stress-strain curves. Predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only + or - 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

Anti-Müllerian hormone concentrations and antral follicle counts for the prediction of pregnancy outcomes after intrauterine insemination.

PubMed

Moro, Francesca; Tropea, Anna; Scarinci, Elisa; Leoncini, Emanuele; Boccia, Stefania; Federico, Alex; Alesiani, Ornella; Lanzone, Antonio; Apa, Rosanna

2016-04-01

To evaluate anti-Müllerian hormone (AMH) concentrations and antral follicle counts (AFCs) in the prediction of pregnancy outcomes after controlled ovarian stimulation among women undergoing intrauterine insemination. A retrospective study included women with unexplained infertility aged 41years or younger who attended a fertility clinic in Italy between December 2009 and May 2014. Ovarian stimulation was achieved with recombinant follicle-stimulating hormone or highly purified human menopausal gonadotropin. Receiver operating characteristic curves were generated to predict ongoing pregnancy. The primary outcome was the association between AMH/AFC and ongoing pregnancy, and was assessed by logistic regression. Overall, 276 women were included, of whom 43 (15.6%) achieved ongoing pregnancy. Multivariate analysis showed that women with a serum day-3 concentration of AMH higher than 2.3ng/mL were more likely to have ongoing pregnancy than were those with a concentration lower than 2.3ng/mL (odds ratio 5.84, 95% confidence interval 2.38-14.31; P<0.001). No associations were recorded for AFCs. AMH should be used to predict the pregnancy outcome of intrauterine insemination. Copyright © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Deriving predicted no-effect concentrations (PNECs) for emerging contaminants in the river Po, Italy, using three approaches: Assessment factor, species sensitivity distribution and AQUATOX ecosystem modelling.

PubMed

Gredelj, Andrea; Barausse, Alberto; Grechi, Laura; Palmeri, Luca

2018-06-20

Over the past decades, per- and polyfluoroalkyl substances (PFASs) found in environmental matrices worldwide have raised concerns due to their toxicity, ubiquity and persistence. A widespread pollution of groundwater and surface waters caused by PFASs in Northern Italy has been recently discovered, becoming a major environmental issue, also because the exact risk for humans and nature posed by this contamination is unclear. Here, the Po River in Northern Italy was selected as a study area to assess the ecological risk posed by perfluoroalkyl acids (PFAAs), a class of PFASs, considering the noticeable concentration of various PFAAs detected in the Po waters over the past years. Moreover, the Po has a large environmental and socio-economic importance: it is the largest Italian river and drains a densely inhabited, intensely cultivated and heavily industrialized watershed. Predicted no-effect concentrations (PNECs) were derived using two regulated methodologies, assessment factors (AFs) and species sensitivity distribution (SSD), which rely on published ecotoxicological laboratory tests. Results were compared to those of a novel methodology using the mechanistic ecosystem model AQUATOX to compute PNECs in an ecologically-sound manner, i.e. considering physical, chemical, biological and ecological processes in the river. The model was used to quantify how the biomasses of the modelled taxa in the river food web deviated from natural conditions due to varying inputs of the chemicals. PNEC for each chemical was defined as the lowest chemical concentration causing a non-negligible yearly biomass loss for a simulated taxon with respect to a control simulation. The investigated PFAAs were Perfluorooctanoic acid (PFOA) and Perfluorooctanesulfonic acid (PFOS) as long-chained compounds, and Perfluorobutanoic acid (PFBA) and Perfluorobutanesulfonic acid (PFBS) as short-chained homologues. Two emerging contaminants, Linear Alkylbenzene Sulfonate (LAS) and triclosan, were also

Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System.

PubMed

Eser, Alexander; Primas, Christian; Reinisch, Sieglinde; Vogelsang, Harald; Novacek, Gottfried; Mould, Diane R; Reinisch, Walter

2018-01-30

Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P < .0001). The Bayesian dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab. © 2018, The American College of Clinical Pharmacology.

Inadequate hepcidin serum concentrations predict incident type 2 diabetes mellitus.

PubMed

Pechlaner, Raimund; Weiss, Günter; Bansal, Sukhvinder; Mayr, Manuel; Santer, Peter; Pallhuber, Barbara; Notdurfter, Marlene; Bonora, Enzo; Willeit, Johann; Kiechl, Stefan

2016-02-01

Type 2 diabetes mellitus (T2DM) is closely associated with elevated body iron stores. The hormone hepcidin is the key regulator of iron homeostasis. Inadequately low hepcidin levels were recently reported in subjects with manifest T2DM. We investigated whether alterations of hepcidin levels precede the manifestation of T2DM and predict T2DM development independently of established risk conditions. This prospective population-based study included 675 subjects aged 50-89 years, 51.9% of whom were female. Hepcidin levels were measured by gold standard tandem mass spectrometry. Diabetes was diagnosed according to American Diabetes Association criteria, and incident diabetes was recorded between baseline in 2000 and 2010. The baseline hepcidin-to-ferritin ratio in subjects that subsequently developed diabetes during follow-up was reduced on average by 29.8% as compared with subjects with normal glucose tolerance (95% confidence interval, -50.7% to -0.2%; p = 0.049). After adjustment for age, sex, and serum ferritin, higher hepcidin levels were associated with reduced risk of incident diabetes (hazard ratio per 1-unit higher log2 hepcidin, 0.80; 95% confidence interval, 0.64-0.98; p = 0.035; 33 events). Additional adjustment for established diabetes risk factors and determinants of hepcidin concentration did not appreciably change these results (HR, 0.81; 95% CI, 0.66-0.99). Likewise, inadequately low hepcidin levels were also detected in subjects with prevalent T2DM (n = 76). Hepcidin levels that are inadequately low in relation to body iron stores are an independent predictor for incident T2DM and may contribute to diabetes-related tissue iron overload. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

NASA Astrophysics Data System (ADS)

Chen, Ziyue; Cai, Jun; Gao, Bingbo; Xu, Bing; Dai, Shuang; He, Bin; Xie, Xiaoming

2017-01-01

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM2.5 concentration should be better understood. This research examined seasonal variations of PM2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM2.5 concentration. For the Jing-Jin-Ji region, the higher PM2.5 concentration, the stronger influences meteorological factors exert on PM2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.

Physiological epidermal growth factor concentrations activate high affinity receptors to elicit calcium oscillations.

PubMed

Marquèze-Pouey, Béatrice; Mailfert, Sébastien; Rouger, Vincent; Goaillard, Jean-Marc; Marguet, Didier

2014-01-01

Signaling mediated by the epidermal growth factor (EGF) is crucial in tissue development, homeostasis and tumorigenesis. EGF is mitogenic at picomolar concentrations and is known to bind its receptor on high affinity binding sites depending of the oligomerization state of the receptor (monomer or dimer). In spite of these observations, the cellular response induced by EGF has been mainly characterized for nanomolar concentrations of the growth factor, and a clear definition of the cellular response to circulating (picomolar) concentrations is still lacking. We investigated Ca2+ signaling, an early event in EGF responses, in response to picomolar doses in COS-7 cells where the monomer/dimer equilibrium is unaltered by the synthesis of exogenous EGFR. Using the fluo5F Ca2+ indicator, we found that picomolar concentrations of EGF induced in 50% of the cells a robust oscillatory Ca2+ signal quantitatively similar to the Ca2+ signal induced by nanomolar concentrations. However, responses to nanomolar and picomolar concentrations differed in their underlying mechanisms as the picomolar EGF response involved essentially plasma membrane Ca2+ channels that are not activated by internal Ca2+ store depletion, while the nanomolar EGF response involved internal Ca2+ release. Moreover, while the picomolar EGF response was modulated by charybdotoxin-sensitive K+ channels, the nanomolar response was insensitive to the blockade of these ion channels.

Prediction of beef carcass and meat traits from rearing factors in young bulls and cull cows.

PubMed

Soulat, J; Picard, B; Léger, S; Monteils, V

2016-04-01

The aim of this study was to predict the beef carcass and LM (thoracis part) characteristics and the sensory properties of the LM from rearing factors applied during the fattening period. Individual data from 995 animals (688 young bulls and 307 cull cows) in 15 experiments were used to establish prediction models. The data concerned rearing factors (13 variables), carcass characteristics (5 variables), LM characteristics (2 variables), and LM sensory properties (3 variables). In this study, 8 prediction models were established: dressing percentage and the proportions of fat tissue and muscle in the carcass to characterize the beef carcass; cross-sectional area of fibers (mean fiber area) and isocitrate dehydrogenase activity to characterize the LM; and, finally, overall tenderness, juiciness, and flavor intensity scores to characterize the LM sensory properties. A random effect was considered in each model: the breed for the prediction models for the carcass and LM characteristics and the trained taste panel for the prediction of the meat sensory properties. To evaluate the quality of prediction models, 3 criteria were measured: robustness, accuracy, and precision. The model was robust when the root mean square errors of prediction of calibration and validation sub-data sets were near to one another. Except for the mean fiber area model, the obtained predicted models were robust. The prediction models were considered to have a high accuracy when the mean prediction error (MPE) was ≤0.10 and to have a high precision when the was the closest to 1. The prediction of the characteristics of the carcass from the rearing factors had a high precision ( > 0.70) and a high prediction accuracy (MPE < 0.10), except for the fat percentage model ( = 0.67, MPE = 0.16). However, the predictions of the LM characteristics and LM sensory properties from the rearing factors were not sufficiently precise ( < 0.50) and accurate (MPE > 0.10). Only the flavor intensity of the beef

[Concentrations of fine particulate matters and ultrafine particles and influenced factors during winter in an area of Beijing].

PubMed

Ni, Yang; Tu, Xing-ying; Zhu, Yi-dan; Guo, Xin-biao; Deng, Fu-rong

2014-06-18

To study the concentrations of fine particulate matters and ultrafine particles and influenced factors during winter in an area of Beijing. Real-time monitoring of particles' mass and number concentrations were conducted in an area of Beijing from February 7(th) to 27(th), 2013. At the same time, the meteorological data were also collected from the Beijing meteorological website. Differences of the particles' mass and number concentrations during different periods were analyzed using Mann-Whitney U test. Meanwhile, the influenced factors were also analyzed. The mean concentrations of fine particulate matters and ultrafine particles were (157.2 ± 142.8) μg/m³ and (25 018 ± 9 309) particles/cm³, respectively. The particles' number and mass concentrations in haze days were 1.27 times and 2.91 times higher than those in non-haze days, respectively. The mass concentrations of fine particulate matters in the self-monitoring site were higher than those in the nearest central monitoring sites, and the hourly-average concentrations of particles were significantly consistent with those at the commuter times. Meanwhile, the setting off of fireworks/firecrackers during the Spring Festival could lead to short-term increases of the particles' number and mass concentrations. When the wind speed was low and the related humidity was high, the concentrations of particulate matters were relatively high, and the mass concentrations of fine particulate matters were lagged about 1-2 d. The level of the particulate matters in this area was high. Heavy traffic, setting off of fireworks/firecrackers and meteorological factors may be some of the main factors affecting the concentrations of the particulate matters in this area. Among those factors, the effect of setting off of fireworks/firecrackers didn't last long and the effect of the meteorological factors had a hysteresis effect.

Systematic review of prognostic factors predicting outcome in non-surgically treated patients with sciatica.

PubMed

Verwoerd, A J H; Luijsterburg, P A J; Lin, C W C; Jacobs, W C H; Koes, B W; Verhagen, A P

2013-09-01

Identification of prognostic factors for surgery in patients with sciatica is important to be able to predict surgery in an early stage. Identification of prognostic factors predicting persistent pain, disability and recovery are important for better understanding of the clinical course, to inform patient and physician and support decision making. Consequently, we aimed to systematically review prognostic factors predicting outcome in non-surgically treated patients with sciatica. A search of Medline, Embase, Web of Science and Cinahl, up to March 2012 was performed for prospective cohort studies on prognostic factors for non-surgically treated sciatica. Two reviewers independently selected studies for inclusion and assessed the risk of bias. Outcomes were pain, disability, recovery and surgery. A best evidence synthesis was carried out in order to assess and summarize the data. The initial search yielded 4392 articles of which 23 articles reporting on 14 original cohorts met the inclusion criteria. High clinical, methodological and statistical heterogeneity among studies was found. Reported evidence regarding prognostic factors predicting the outcome in sciatica is limited. The majority of factors that have been evaluated, e.g., age, body mass index, smoking and sensory disturbance, showed no association with outcome. The only positive association with strong evidence was found for leg pain intensity at baseline as prognostic factor for subsequent surgery. © 2013 European Federation of International Association for the Study of Pain Chapters.

Intrinsic Predictive Factors of Noncontact Lateral Ankle Sprain in Collegiate Athletes

PubMed Central

Kobayashi, Takumi; Yoshida, Masahiro; Yoshida, Makoto; Gamada, Kazuyoshi

2013-01-01

Background: Lateral ankle sprain (LAS) is one of the most common injuries in sports. Despite extensive research, intrinsic factors that predict initial and recurrent noncontact LAS remain undefined. Purpose: To identify the predictive factors of initial and recurrent noncontact LAS, focusing on ankle flexibility and/or alignment in collegiate athletes. Study Design: Case-control study; Level of evidence, 3. Methods: A total of 191 athletes were assessed during the preseason for factors predictive of noncontact LAS. The baseline measurements included weightbearing dorsiflexion range of motion (ROM), leg-heel angle, foot internal rotation angle in plantar flexion, classification according to the mortise test, and navicular–medial malleolus (NMM) distance. Occurrence of noncontact LAS and participation in practice and games were prospectively recorded for 11 months. Results: Of the 191 athletes assessed, 169 (145 males, 24 females) completed the study; 125 athletes had a history of ankle sprain. During the observational period, 16 athletes suffered noncontact LAS (0.58 per 1000 athlete-exposures) consisting of 4 initial sprains and 12 recurrences. The hazard ratio estimated by a Cox regression analysis showed that athletes with an NMM distance ≥4.65 cm were 4.14 times more likely to suffer an initial noncontact LAS than were athletes with a shorter NMM distance (95% confidence interval, 1.12-14.30) and that athletes with a weightbearing dorsiflexion ROM >49.5° were 1.12 times as likely to suffer a recurrent noncontact LAS compared with athletes with a lower ROM (95% confidence interval, 1.05-1.20). Conclusion: NMM distance predicts initial noncontact LAS, and weightbearing dorsiflexion ROM predicts recurrent noncontact LAS. PMID:26535263

Predicting the Spectral Effects of Soils on Concentrating Photovoltaic Systems

DOE PAGES

Burton, Patrick D.; King, Bruce Hardison; Riley, Daniel M.

2014-12-15

The soiling losses on high concentrating photovoltaic (HCPV) systems may be influenced by the spectral properties of accumulated soil. We predicted the response of an isotype cell to changes in spectral content and reduction in transmission due to soiling using measured UV/vis transmittance through soil films. Artificial soil test blends deposited on glass coupons were used to supply the transmission data, which was then used to calculate the effect on model spectra. Moreover, the wavelength transparency of the test soil was varied by incorporating red and yellow mineral pigments into graded sand. The more spectrally responsive (yellow) soils were predictedmore » to alter the current balance between the top and middle subcells throughout a range of air masses corresponding to daily and seasonal variation.« less

A new method to measure effective soil solution concentration predicts copper availability to plants.

PubMed

Zhang, H; Zhao, F J; Sun, B; Davison, W; McGrath, S P

2001-06-15

Risk assessments of metal contaminated soils need to address metal bioavailability. To predict the bioavailability of metals to plants, it is necessary to understand both solution and solid phase supply processes in soils. In striving to find surrogate chemical measurements, scientists have focused either on soil solution chemistry, including free ion activities, or operationally defined fractions of metals. Here we introduce the new concept of effective concentration, CE, which includes both the soil solution concentration and an additional term, expressed as a concentration, that represents metal supplied from the solid phase. CE was measured using the technique of diffusive gradients in thin films (DGT) which, like a plant, locally lowers soil solution concentrations, inducing metal supply from the solid phase, as shown by a dynamic model of the DGT-soil system. Measurements of Cu as CE, soil solution concentration, by EDTA extraction and as free Cu2+ activity in soil solution were made on 29 different soils covering a large range of copper concentrations. Theywere compared to Cu concentrations in the plant material of Lepidium heterophyllum grown on the same soils. Plant concentrations were linearly related and highly correlated with CE but were more scattered and nonlinear with respect to free Cu2+ activity, EDTA extraction, or soil solution concentrations. These results demonstrate that the dominant supply processes in these soils are diffusion and labile metal release, which the DGT-soil system mimics. The quantity CE is shown to have promise as a quantitative measure of the bioavailable metal in soils.

Development and Preliminary Performance of a Risk Factor Screen to Predict Posttraumatic Psychological Disorder After Trauma Exposure

PubMed Central

Carlson, Eve B.; Palmieri, Patrick A.; Spain, David A.

2017-01-01

Objective We examined data from a prospective study of risk factors that increase vulnerability or resilience, exacerbate distress, or foster recovery to determine whether risk factors accurately predict which individuals will later have high posttraumatic (PT) symptom levels and whether brief measures of risk factors also accurately predict later symptom elevations. Method Using data from 129 adults exposed to traumatic injury of self or a loved one, we conducted receiver operating characteristic (ROC) analyses of 14 risk factors assessed by full-length measures, determined optimal cutoff scores and calculated predictive performance for the nine that were most predictive. For five risk factors, we identified sets of items that accounted for 90% of variance in total scores and calculated predictive performance for sets of brief risk measures. Results A set of nine risk factors assessed by full measures identified 89% of those who later had elevated PT symptoms (sensitivity) and 78% of those who did not (specificity). A set of four brief risk factor measures assessed soon after injury identified 86% of those who later had elevated PT symptoms and 72% of those who did not. Conclusions Use of sets of brief risk factor measures shows promise of accurate prediction of PT psychological disorder and probable PTSD or depression. Replication of predictive accuracy is needed in a new and larger sample. PMID:28622811

Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

PubMed Central

Lewis, Jesse S.; Farnsworth, Matthew L.; Burdett, Chris L.; Theobald, David M.; Gray, Miranda; Miller, Ryan S.

2017-01-01

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. PMID:28276519

Factors affecting species distribution predictions: A simulation modeling experiment

Treesearch

Gordon C. Reese; Kenneth R. Wilson; Jennifer A. Hoeting; Curtis H. Flather

2005-01-01

Geospatial species sample data (e.g., records with location information from natural history museums or annual surveys) are rarely collected optimally, yet are increasingly used for decisions concerning our biological heritage. Using computer simulations, we examined factors that could affect the performance of autologistic regression (ALR) models that predict species...

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, Clarence C., Jr.

1989-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 deg and +/- 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was develolped to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Far-field strains at failure were calculated from the strain intensity factor, and then strengths were calculated from the far-field strains using uniaxial stress-strain curves. The predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only +/- 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

Stress factors predicting injuries of hospital personnel.

PubMed

Salminen, Simo; Kivimäki, Mika; Elovainio, Marko; Vahtera, Jussi

2003-07-01

Stress at work has long been recognized as a factor in increasing risk for mental and physical health problems. The extent to which work stressors and stress predicted injuries occur in a large population of Finnish hospital workers was studied. A total of 5,111 employees (624 men, 4,487 women) from 10 hospitals participated in this study. Their psychological distress was measured by the General Health Questionnaire, and overload and job control by the Harris scale and the Job Content Questionnaire, respectively. Injuries certified by a physician were followed up for 3 years: injuries in 1997 (n = 213) were used as a measure of baseline and injuries in 1998-1999 (n = 443) were the dependent variables. Psychological distress was not significantly related to injuries. However, low decision latitude (risk ratio = 1.27 (1.04 to 1.54)), low skill discretion only for men (risk ratio = 2.76 (1.78 to 4.30)), and highly monotonous work (risk ratio = 1.26 (1.02 to 1.55)) were stressors predicting injuries. In addition, workers with numerous problems in interpersonal relationships (risk ratio = 1.43 (1.18 to 1.73)) or many conflicts in collaboration at work (risk ratio = 1.40 (1.15 to 1.71)) were more often involved in injuries. This study showed that stressors related to autonomy of work and interpersonal relationship at workplace are predictors of injuries in hospital settings. These factors are potentially amenable to organizational interventions. Copyright 2003 Wiley-Liss, Inc.

Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA)

PubMed Central

Maznah, Zainol; Halimah, Muhamad; Shitan, Mahendran; Kumar Karmokar, Provash; Najwa, Sulaiman

2017-01-01

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil. PMID:28060816

Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA).

PubMed

Maznah, Zainol; Halimah, Muhamad; Shitan, Mahendran; Kumar Karmokar, Provash; Najwa, Sulaiman

2017-01-01

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil.

Vitamin D deficiency and high serum IL-6 concentration as risk factors for tubal factor infertility in Chinese women.

PubMed

Chen, Weiwei; Jiao, Xianting; Zhang, Jun; Wang, Lei; Yu, Xiaodan

2018-05-01

The aim of this study was to investigate the relationship between 25-hydroxyvitamin-D [25(OH)D] and female infertility and to further explore the role of inflammatory cytokines. We recruited 356 infertile women diagnosed with tubal factor infertility (TFI) or polycystic ovary syndrome (PCOS) or endometriosis, as well as 180 fertile women. Serum concentrations of 25(OH)D, interleukin (IL)-6, IL-1 β, and interferon-α were measured. The 25(OH)D concentration in TFI women was the lowest (16.9 ng/mL) and was significantly different from that in the fertile women (19.4 ng/mL; P <0.05)]; whereas women with TFI had higher IL-6 concentrations. After adjusting for confounders, 25(OH)D deficiency presented a risk factor for TFI (odds ratio [OR], 4.2; 95% confidence interval [CI], 1.5-11.3). There was a dose-effect relation between IL-6 tertiles and TFI: the higher the IL-6, the higher the risk for TFI (middle versus low: OR, 3.7; 95% CI, 1.5-9.5; high versus low: OR, 13.2; 95% CI, 4.8-36.4). IL-6 showed a negative correlation with 25(OH)D (r = -0.19). Women with both high IL-6 and low 25(OH)D had the highest risk for TFI (OR, 10.6; 95% CI, 4.2-26.3). Both vitamin D deficiency and high serum IL-6 concentration are risk factors for TFI. Serum 25(OH)D concentration was significantly and negatively correlated with serum IL-6. There was an interaction between IL-6 and 25(OH)D for the risk for TFI-related infertility. We hypothesized that vitamin D might reduce the risk for TFI through suppressing the production of IL-6. Copyright © 2017 Elsevier Inc. All rights reserved.

Erratum: Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

PubMed

Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

2010-10-01

The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. © 2010 SETAC.

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea.

PubMed

Kim, Sun-Young; Song, Insang

2017-07-01

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM 10 and NO 2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM 10 and NO 2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM 10 and NO 2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM 10 and NO 2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R 2 s of 0.45 and 0.82 for PM 10 and NO 2 , respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and

Indoor Radon Concentration Related to Different Radon Areas and Indoor Radon Prediction

NASA Astrophysics Data System (ADS)

Juhásová Šenitková, Ingrid; Šál, Jiří

2017-12-01

Indoor radon has been observed in the buildings at areas with different radon risk potential. Preventive measures are based on control of main potential radon sources (soil gas, building material and supplied water) to avoid building of new houses above recommended indoor radon level 200 Bq/m3. Radon risk (index) estimation of individual building site bedrock in case of new house siting and building protection according technical building code are obligatory. Remedial actions in buildings built at high radon risk areas were carried out principally by unforced ventilation and anti-radon insulation. Significant differences were found in the level of radon concentration between rooms where radon reduction techniques were designed and those where it was not designed. The mathematical model based on radon exhalation from soil has been developed to describe the physical processes determining indoor radon concentration. The model is focused on combined radon diffusion through the slab and advection through the gap from sub-slab soil. In this model, radon emanated from building materials is considered not having a significant contribution to indoor radon concentration. Dimensional analysis and Gauss-Newton nonlinear least squares parametric regression were used to simplify the problem, identify essential input variables and find parameter values. The presented verification case study is introduced for real buildings with respect to various underground construction types. Presented paper gives picture of possible mathematical approach to indoor radon concentration prediction.

ACUTE PANCREATITIS GRAVITY PREDICTIVE FACTORS: WHICH AND WHEN TO USE THEM?

PubMed Central

FERREIRA, Alexandre de Figueiredo; BARTELEGA, Janaina Alves; URBANO, Hugo Corrêa de Andrade; de SOUZA, Iure Kalinine Ferraz

2015-01-01

Introduction: Acute pancreatitis has as its main causes lithiasic biliary disease and alcohol abuse. Most of the time, the disease shows a self-limiting course, with a rapid recovery, only with supportive treatment. However, in a significant percentage of cases, it runs with important local and systemic complications associated with high mortality rates. Aim: To present the current state of the use of these prognostic factors (predictive scores) of gravity, as the time of application, complexity and specificity. Method: A non-systematic literature review through 28 papers, with emphasis on 13 articles published in indexed journals between 2008 and 2013 using Lilacs, Medline, Pubmed. Results: Several clinical, laboratory analysis, molecular and image variables can predict the development of severe acute pancreatitis. Some of them by themselves can be determinant to the progression of the disease to a more severe form, such as obesity, hematocrit, age and smoking. Hematocrit with a value lower than 44% and serum urea lower than 20 mg/dl, both at admission, appear as risk factors for pancreatic necrosis. But the PCR differentiates mild cases of serious ones in the first 24 h. Multifactorial scores measured on admission and during the first 48 h of hospitalization have been used in intensive care units, being the most ones used: Ranson, Apache II, Glasgow, Iget and Saps II. Conclusion: Acute pancreatitis is a disease in which several prognostic factors are employed being useful in predicting mortality and on the development of the severe form. It is suggested that the association of a multifactorial score, especially the Saps II associated with Iget, may increase the prognosis accuracy. However, the professional's preferences, the experience on the service as well as the available tools, are factors that have determined the choice of the most suitable predictive score. PMID:26537149

Identification of the inactivating factors and mechanisms exerted on MS2 coliphage in concentrated synthetic urine.

PubMed

Oishi, Wakana; Sano, Daisuke; Decrey, Loic; Kadoya, Syunsuke; Kohn, Tamar; Funamizu, Naoyuki

2017-11-15

Volume reduction (condensation) is a key for the practical usage of human urine as a fertilizer because it enables the saving of storage space and the reduction of transportation cost. However, concentrated urine may carry infectious disease risks resulting from human pathogens frequently present in excreta, though the survival of pathogens in concentrated urine is not well understood. In this study, the inactivation of MS2 coliphage, a surrogate for single-stranded RNA human enteric viruses, in concentrated synthetic urine was investigated. The infectious titer reduction of MS2 coliphage in synthetic urine samples was measured by plaque assay, and the reduction of genome copy number was monitored by reverse transcription-quantitative PCR (RTqPCR). Among chemical-physical conditions such as pH and osmotic pressure, uncharged ammonia was shown to be the predominant factor responsible for MS2 inactivation, independently of urine concentration level. The reduction rate of the viral genome number varied among genome regions, but the comprehensive reduction rate of six genome regions was well correlated with that of the infectious titer of MS2 coliphage. This indicates that genome degradation is the main mechanism driving loss of infectivity, and that RT-qPCR targeting the six genome regions can be used as a culture-independent assay for monitoring infectivity loss of the coliphage in urine. MS2 inactivation rate constants were well predicted by a model using ion composition and speciation in synthetic urine samples, which suggests that MS2 infectivity loss can be estimated solely based on the solution composition, temperature and pH, without explicitly accounting for effects of osmotic pressure. Copyright © 2017 Elsevier B.V. All rights reserved.

Description of Aspergillus flavus growth under the influence of different factors (water activity, incubation temperature, protein and fat concentration, pH, and cinnamon essential oil concentration) by kinetic, probability of growth, and time-to-detection models.

PubMed

Kosegarten, Carlos E; Ramírez-Corona, Nelly; Mani-López, Emma; Palou, Enrique; López-Malo, Aurelio

2017-01-02

A Box-Behnken design was used to determine the effect of protein concentration (0, 5, or 10g of casein/100g), fat (0, 3, or 6g of corn oil/100g), a w (0.900, 0.945, or 0.990), pH (3.5, 5.0, or 6.5), concentration of cinnamon essential oil (CEO, 0, 200, or 400μL/kg) and incubation temperature (15, 25, or 35°C) on the growth of Aspergillus flavus during 50days of incubation. Mold response under the evaluated conditions was modeled by the modified Gompertz equation, logistic regression, and time-to-detection model. The obtained polynomial regression models allow the significant coefficients (p<0.05) for linear, quadratic and interaction effects for the Gompertz equation's parameters to be identified, which adequately described (R 2 >0.967) the studied mold responses. After 50days of incubation, every tested model system was classified according to the observed response as 1 (growth) or 0 (no growth), then a binary logistic regression was utilized to model A. flavus growth interface, allowing to predict the probability of mold growth under selected combinations of tested factors. The time-to-detection model was utilized to estimate the time at which A. flavus visible growth begins. Water activity, temperature, and CEO concentration were the most important factors affecting fungal growth. It was observed that there is a range of possible combinations that may induce growth, such that incubation conditions and the amount of essential oil necessary for fungal growth inhibition strongly depend on protein and fat concentrations as well as on the pH of studied model systems. The probabilistic model and the time-to-detection models constitute another option to determine appropriate storage/processing conditions and accurately predict the probability and/or the time at which A. flavus growth occurs. Copyright © 2016 Elsevier B.V. All rights reserved.

Nerve growth factor concentrations in the synovial fluid from healthy dogs and dogs with secondary osteoarthritis.

PubMed

Isola, M; Ferrari, V; Miolo, A; Stabile, F; Bernardini, D; Carnier, P; Busetto, R

2011-01-01

To measure the concentrations of nerve growth factor (NGF) in the synovial fluid from normal dogs and dogs with osteoarthritis (OA) secondary to common joint disorders. Nerve growth factor synovial concentrations were measured by ELISA assay in 50 dogs divided into three groups: 12 healthy, 16 affected by acute lameness within seven days before enrolment, and 22 with chronic lameness persisting by more than one month before enrolment and accompanied by radiological signs of OA. Both acute and chronic lameness were secondary to orthopaedic diseases involving the shoulder, elbow and stifle joints. Nerve growth factor synovial concentrations were compared between means for healthy and acute groups and between the three groups using an F-test. Significance level was set at p <0.05. Nerve growth factor was detected in all canine synovial fluid samples. However, the mean synovial NGF concentration of healthy dogs (3.65 ± 2.18 pg/ml) was not significantly different from the mean value in dogs with acute lameness (6.45 ± 2.45 pg/ml) (p = 0.79). Conversely, the mean synovial NGF concentration in dogs with chronic lameness (20.19 ± 17.51 pg/ml) was found to be significantly higher than that found in healthy dogs (p <0.01). This study demonstrates for the first time the presence of NGF in canine synovial fluid and its increased concentrations in dogs with chronic lameness compared to healthy dogs and dogs with acute lameness. The association between chronic lameness and raised synovial concentrations may suggest an involvement of NGF in OA inflammation and chronic pain.

Predicting the cosmological constant with the scale-factor cutoff measure

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Simone, Andrea; Guth, Alan H.; Salem, Michael P.

2008-09-15

It is well known that anthropic selection from a landscape with a flat prior distribution of cosmological constant {lambda} gives a reasonable fit to observation. However, a realistic model of the multiverse has a physical volume that diverges with time, and the predicted distribution of {lambda} depends on how the spacetime volume is regulated. A very promising method of regulation uses a scale-factor cutoff, which avoids a number of serious problems that arise in other approaches. In particular, the scale-factor cutoff avoids the 'youngness problem' (high probability of living in a much younger universe) and the 'Q and G catastrophes'more » (high probability for the primordial density contrast Q and gravitational constant G to have extremely large or small values). We apply the scale-factor cutoff measure to the probability distribution of {lambda}, considering both positive and negative values. The results are in good agreement with observation. In particular, the scale-factor cutoff strongly suppresses the probability for values of {lambda} that are more than about 10 times the observed value. We also discuss qualitatively the prediction for the density parameter {omega}, indicating that with this measure there is a possibility of detectable negative curvature.« less

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.

PubMed

Larkin, Andrew; Williams, David E; Kile, Molly L; Baird, William M

2015-06-01

There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM 2.5 ), coarse particulate matter (PM 10 ), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations

PubMed Central

Larkin, Andrew; Williams, David E.; Kile, Molly L.; Baird, William M.

2014-01-01

Background There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards. PMID:26146409

CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data

PubMed Central

O'Connor, Timothy; Bodén, Mikael

2017-01-01

Abstract Identifying the genomic regions and regulatory factors that control the transcription of genes is an important, unsolved problem. The current method of choice predicts transcription factor (TF) binding sites using chromatin immunoprecipitation followed by sequencing (ChIP-seq), and then links the binding sites to putative target genes solely on the basis of the genomic distance between them. Evidence from chromatin conformation capture experiments shows that this approach is inadequate due to long-distance regulation via chromatin looping. We present CisMapper, which predicts the regulatory targets of a TF using the correlation between a histone mark at the TF's bound sites and the expression of each gene across a panel of tissues. Using both chromatin conformation capture and differential expression data, we show that CisMapper is more accurate at predicting the target genes of a TF than the distance-based approaches currently used, and is particularly advantageous for predicting the long-range regulatory interactions typical of tissue-specific gene expression. CisMapper also predicts which TF binding sites regulate a given gene more accurately than using genomic distance. Unlike distance-based methods, CisMapper can predict which transcription start site of a gene is regulated by a particular binding site of the TF. PMID:28204599

Prediction and Observation of Electron Instabilities and Phase Space Holes Concentrated in the Lunar Plasma Wake

NASA Astrophysics Data System (ADS)

Hutchinson, Ian H.; Malaspina, David M.

2018-05-01

Recent theory and numerical simulation predicts that the wake of the solar wind flow past the Moon should be the site of electrostatic instabilities that give rise to electron holes. These play an important role in the eventual merging of the wake with the background solar wind. Analysis of measurements from the ARTEMIS satellites, orbiting the Moon at distances from 1.2 to 11 RM, detects holes highly concentrated in the wake, in agreement with prediction. The theory also predicts that the hole flux density observed should be hollow, peaking away from the wake axis. Observation statistics qualitatively confirm this hollowness, lending extra supporting evidence for the identification of their generation mechanism.

Venom Concentrations and Clotting Factor Levels in a Prospective Cohort of Russell's Viper Bites with Coagulopathy.

PubMed

Isbister, Geoffrey K; Maduwage, Kalana; Scorgie, Fiona E; Shahmy, Seyed; Mohamed, Fahim; Abeysinghe, Chandana; Karunathilake, Harendra; O'Leary, Margaret A; Gnanathasan, Christeine A; Lincz, Lisa F

2015-01-01

Russell's viper envenoming is a major problem in South Asia and causes venom induced consumption coagulopathy. This study aimed to investigate the kinetics and dynamics of venom and clotting function in Russell's viper envenoming. In a prospective cohort of 146 patients with Russell's viper envenoming, we measured venom concentrations, international normalised ratio [INR], prothrombin time (PT), activated partial thromboplastin time (aPTT), coagulation factors I, II, V, VII, VIII, IX and X, and von Willebrand factor antigen. The median age was 39 y (16-82 y) and 111 were male. The median peak INR was 6.8 (interquartile range [IQR]: 3.7 to >13), associated with low fibrinogen [median,<0.01 g/L; IQR: <0.01-0.9 g/L), low factor V levels [median,<5%; IQR: <5-4%], low factor VIII levels [median,40%; IQR: 12-79%] and low factor X levels [median, 48%; IQR: 29-67%]. There were smaller reductions in factors II, IX and VII over time. All factors recovered over 48 h post-antivenom. The median INR remained >3 at 6 h post-antivenom but had reduced to <2, by 24 h. The aPTT had also returned to close to normal (<50 sec) at 24 h. Factor VII, VIII and IX levels were unusually high pre-antivenom, median peak concentrations of 393%, 307% and 468% respectively. Pre-antivenom venom concentrations and the INR (r = 0.20, p = 0.02) and aPTT (r = 0.19, p = 0.03) were correlated (non-parametric Spearman analysis). Russell's viper coagulopathy results in prolonged aPTT, INR, low fibrinogen, factors V, VIII and X which recover over 48 h. Severity of clotting abnormalities was associated with venom concentrations.

Concentration dependent refractive index of a binary mixture at high pressure.

PubMed

Croccolo, Fabrizio; Arnaud, Marc-Alexandre; Bégué, Didier; Bataller, Henri

2011-07-21

In the present work binary mixtures of varying concentrations of two miscible hydrocarbons, 1,2,3,4-tetrahydronaphtalene (THN) and n-dodecane (C12), are subjected to increasing pressure up to 50 MPa in order to investigate the dependence of the so-called concentration contrast factor (CF), i.e., (∂n/∂c)(p, T), on pressure level. The refractive index is measured by means of a Mach-Zehnder interferometer. The setup and experimental procedure are validated with different pure fluids in the same pressure range. The refractive index of the THN-C12 mixture is found to vary both over pressure and concentration, and the concentration CF is found to exponentially decrease as the pressure is increased. The measured values of the refractive index and the concentration CFs are compared with values obtained by two different theoretical predictions, the well-known Lorentz-Lorenz formula and an alternative one proposed by Looyenga. While the measured refractive indices agree very well with predictions given by Looyenga, the measured concentration CFs show deviations from the latter of the order of 6% and more than the double from the Lorentz-Lorenz predictions.

Factors predictive of risk for complications in patients with oesophageal foreign bodies.

PubMed

Sung, Sang Hun; Jeon, Seong Woo; Son, Hyuk Su; Kim, Sung Kook; Jung, Min Kyu; Cho, Chang Min; Tak, Won Young; Kweon, Young Oh

2011-08-01

Reports on predictive risk factors associated with complications of ingested oesophageal foreign bodies are rare. The aim of this study was to determine the predictive risk factors associated with the complications of oesophageal foreign bodies. Three hundred sixteen cases with foreign bodies in the oesophagus were retrospectively investigated. The predictive risk factors for complications after foreign body ingestion were analysed by multivariate logistic regression, and included age, size and type of foreign body ingested, duration of impaction, and the level of foreign body impaction. The types of oesophageal foreign bodies included fish bones (37.0%), food (19.0%), and metals (18.4%). The complications associated with foreign bodies were ulcers (21.2%), lacerations (14.9%), erosions (12.0%), and perforation (1.9%). Multivariate analysis showed that the duration of impaction (p<0.001), and the type (p<0.001) and size of the foreign bodies (p<0.001) were significant independent risk factors associated with the development of complications in patients with oesophageal foreign bodies. In patients with oesophageal foreign bodies, the risk of complications was increased with a longer duration of impaction, bone type, and larger size. Copyright © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predictive factors of telemedicine service acceptance and behavioral intention of physicians.

PubMed

Rho, Mi Jung; Choi, In Young; Lee, Jaebeom

2014-08-01

Despite the proliferation of telemedicine technology, telemedicine service acceptance has been slow in actual healthcare settings. The purpose of this research is to develop a theoretical model for explaining the predictive factors influencing physicians' willingness to use telemedicine technology to provide healthcare services. We developed the Telemedicine Service Acceptance model based on the technology acceptance model (TAM) with the inclusion of three predictive constructs from the previously published telemedicine literature: (1) accessibility of medical records and of patients as clinical factors, (2) self-efficacy as an individual factor and (3) perceived incentives as regulatory factors. A survey was conducted, and structural equation modeling was applied to evaluate the empirical validity of the model and causal relationships within the model using the data collected from 183 physicians. Our results confirmed the validity of the original TAM constructs: the perceived usefulness of telemedicine directly impacted the behavioral intention to use it, and the perceived ease of use directly impacted both the perceived usefulness and the behavioral intention to use it. In addition, new predictive constructs were found to have ramifications on TAM variables: the accessibility of medical records and of patients directly impacted the perceived usefulness of telemedicine, self-efficacy had a significant positive effect on both the perceived ease of use and the perceived usefulness of telemedicine, and perceived incentives were found to be important with respect to the intention to use telemedicine technology. This study demonstrated that the Telemedicine Service Acceptance model was feasible and could explain the acceptance of telemedicine services by physicians. These results identified important factors for increasing the involvement of physicians in telemedicine practice. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Predictive factors for the occurrence of idiopathic menorrhagia: evidence for a hereditary trait.

PubMed

Kuzmina, Natalia; Palmblad, Jan; Mints, Miriam

2011-01-01

The aim of the present study was to assess predictive factors for occurrence of idiopathic menorrhagia (IM), a disease characterized by abnormal endometrial blood vessel morphology. It was hypothesized that IM exhibits familial clustering (suggesting inheritance) and is associated with other vascular abnormalities, primarily cutaneous hemangiomas. Women with IM (n=152) and healthy, regularly menstruating (n=56) women answered a questionnaire concerning menstrual pattern, susceptibility to bleeding and family history of abnormal gynecological bleeding. Factor analysis with principal component extraction was used to separate predictive factors that may be associated with IM. A total of 35 different items were analyzed. A strong association was found between IM and a family history of heavy menstrual bleeding (r=0.68), but not with cutaneous vascular abnormalities. Our results revealed that a family history of heavy menstrual bleeding may have the highest predictive value for the diagnosis of IM, indicating a hereditary trait.

Predictive factors associated with neck pain in patients with cervical disc degeneration

PubMed Central

Kong, Lingde; Tian, Weifeng; Cao, Peng; Wang, Haonan; Zhang, Bing; Shen, Yong

2017-01-01

Abstract The predictive factors associated with neck pain remain unclear. We conducted a cross-sectional study to assess predictive factors, especially Modic changes (MCs), associated with the intensity and duration of neck pain in patients with cervical disc degenerative disease. We retrospectively reviewed patients in our hospital from January 2013 to December 2016. Severe neck pain (SNP) and persistent neck pain (PNP) were the 2 main outcomes, and were assessed based on the numerical rating scale (NRS). Basic data, and also imaging data, were collected and analyzed as potential predictive factors. Univariate analysis and multiple logistic regression analysis were performed to assess the predictive factors for neck pain. In all, 381 patients (193 males and 188 females) with cervical degenerative disease were included in our study. The number of patients with SNP and PNP were 94 (24.67%) and 109 (28.61%), respectively. The NRS of neck pain in patients with type 1 MCs was significantly higher than type 2 MCs (4.8 ± 0.9 vs 3.9 ± 1.1; P = .004). The multivariate logistic analysis showed that kyphosis curvature (odds ratio [OR] 1.082, 95% confidence interval [CI] 1.044–1.112), spondylolisthesis (OR 1.339, 95% CI 1.226–1.462), and annular tear (OR 1.188, 95% CI 1.021–1.382) were factors associated with SNP, whereas kyphosis curvature (OR 1.568, 95% CI 1.022–2.394), spondylolisthesis (OR 1.486, 95% CI 1.082–2.041), and MCs (OR 1.152, 95% CI 1.074–1.234) were associated with PNP. We concluded that kyphosis curvature, spondylolisthesis, and annular tear are associated with SNP, whereas kyphosis curvature, spondylolisthesis, and MCs are associated with PNP. This study supports the view that MCs can lead to a long duration of neck pain. PMID:29069048

[Concentrations and influencing factors of gaseous polycyclic aromatic hydrocarbons in residential air in Beijing].

PubMed

Wei, Zhi-cheng; Chang, Biao; Qiu, Wei-xun; Wang, Yi; Wu, Shi-min; Xing, Bao-shan; Liu, Wen-xin; Tao, Shu

2007-09-01

7 gas phase PAHs components in indoor air collected from 38 families were investigated by modified passive air samplers in Beijing areas during the local heating and non-heating seasons, and the influencing factors were discussed as well. The analytical results indicate that the gasous PAHs in local indoor air are dominated by 2 and 3 rings compounds, the mean concentrations for the 7 individual gaseous components range from 1 to 40 ng/m3, and the average concentration of total gaseous PAHs is about 100 ng/m3. There is no significant difference in total gaseous PAHs concentrations between the heating and the non-heating seasons, while some apparent seasonal changes occur in ACY and FLA concentrations. Compared with heating season, contribution of 2 rings compounds decreases while the proportions of 3 and 4 rings species increase during the non-heating season. Based on household activity questionnaires and actual analytical concentrations, the main influencing factors accounted for gaseous PAHs in indoor air, identified by multifactor analysis of variance, include cigarette smoking, use of moth ball, intensity of draft, cuisine frequency and built age.

PRISM offers a comprehensive genomic approach to transcription factor function prediction

PubMed Central

Wenger, Aaron M.; Clarke, Shoa L.; Guturu, Harendra; Chen, Jenny; Schaar, Bruce T.; McLean, Cory Y.; Bejerano, Gill

2013-01-01

The human genome encodes 1500–2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells. PMID:23382538

Cross-system comparison of factors influencing chlorophyll-a concentration in Oregon estuaries

EPA Science Inventory

Water column chlorophyll-a (chla) is a proxy for phytoplankton biomass and is often used as a biological response indicator of eutrophication. Although watershed nutrient loading may influence chla concentration in estuaries, factors such as freshwater inflow, residence time, and...

Global analysis of bacterial transcription factors to predict cellular target processes.

PubMed

Doerks, Tobias; Andrade, Miguel A; Lathe, Warren; von Mering, Christian; Bork, Peer

2004-03-01

Whole-genome sequences are now available for >100 bacterial species, giving unprecedented power to comparative genomics approaches. We have applied genome-context methods to predict target processes that are regulated by transcription factors (TFs). Of 128 orthologous groups of proteins annotated as TFs, to date, 36 are functionally uncharacterized; in our analysis we predict a probable cellular target process or biochemical pathway for half of these functionally uncharacterized TFs.

Soluble CD30 and Hepatocyte growth factor as predictive markers of antibody-mediated rejection of the kidney allograft.

PubMed

Pavlova, Yelena; Viklicky, Ondrej; Slatinska, Janka; Bürgelova, Marcela; Süsal, Caner; Skibova, Jelena; Honsová, Eva; Striz, Ilja; Kolesar, Libor; Slavcev, Antonij

2011-07-01

Our retrospective study was aimed to assess the relevance of pre- and post-transplant measurements of serum concentrations of the soluble CD30 molecule (soluble CD30, sCD30) and the cytokine Hepatocyte growth factor (HGF) for prediction of the risk for development of antibody-mediated rejection (AMR) in kidney transplant patients. Evaluation of sCD30, HGF levels and the presence of HLA-specific antibodies in a cohort of 205 patients was performed before, 2weeks and 6months after transplantation. Patients were followed up for kidney graft function and survival for two years. We found a tendency of higher incidence of AMR in retransplanted patients with elevated pre-transplant sCD30 (≥100U/ml) (p=0.051), however no such correlation was observed in first-transplant patients. Kidney recipients with simultaneously high sCD30 and HLA-specific antibodies (sCD30+/Ab+) before transplantation had significantly lower AMR-free survival compared to the other patient groups (p<0.001). HGF concentrations were not associated with the incidence of AMR at any time point of measurement, nevertheless, the combined analysis HGF and sCD30 showed increased incidence of AMR in recipients with elevated pretransplant sCD30 and low HGF levels. the predictive value of pretransplant sCD30 for the development of antibody-mediated rejection after transplantation is significantly potentiated by the co-presence of HLA specific antibodies. The role of HGF as a rejection-protective factor in patients with high pretransplant HGF levels would need further investigation. Copyright © 2011 Elsevier B.V. All rights reserved.

Lithium poisoning in the intensive care unit: predictive factors of severity and indications for extracorporeal toxin removal to improve outcome.

PubMed

Vodovar, Dominique; El Balkhi, Souleiman; Curis, Emmanuel; Deye, Nicolas; Mégarbane, Bruno

2016-09-01

Lithium is responsible for life-threatening poisoning, not consistently improved by extracorporeal toxin removal (ECTR). Our aim was to identify predictive factors on admission of poisoning severity and based on an evaluation of practice, report indications for ECTR susceptible to improve outcome Methods: We performed a retrospective cohort study including all lithium-poisoned patients admitted to the ICU in a university hospital. The usual clinical, biological and toxicological variables were collected. Poisoning severity was defined by seizures, catecholamine infusion, mechanical ventilation >48 h and/or fatality. Univariate followed by multiple logistic regression analyses were performed to identify prognosticators of poisoning severity and ECTR use. From 1992 to 2013, 128 lithium-poisoned patients including acutely (10%), acute-on-chronically (63%) and chronically poisoned patients (27%) were included. The presumed ingested dose of lithium was 17.0 g [8.0-24.5] (median [25th-75th percentiles]). Serum lithium concentrations were 2.6 mmol/l [1.5-4.6], 2.8 mmol/l [1.8-4.5] and 2.8 mmol/l [2.1-3.0] on admission, peaking at 3.6 mmol/l [2.6; 6.2], 4.3 mmol/l [2.4; 6.2] and 2.8 mmol/l [2.1; 3.1] in the three groups, respectively. Severe poisoning occurred in 48 patients (38%) including four fatalities. Using the regression analysis, predictive factors of poisoning severity were Glasgow coma score ≤10 (Odds ratio (OR), 11.1; 95% confidence interval (CI), [4.1-33.3], p < 0.0001) and lithium concentration ≥5.2 mmol/l (OR, 6.0; CI, [1.7-25.5], p = 0.005). Ninety-eight patients (77%) developed acute kidney injury according to KDIGO criteria and 22 (17%) were treated with ECTR. Peak lithium concentration ≥5.2 mmol/l (OR, 22.4; CI, [6.4-96.4]; p < 0.0001) and peak creatinine concentration ≥200 μmol/l (OR, 5.0; CI, [1.4-19.2]; p = 0.01) were associated with ECTR use. Only 21/46 patients who presented one of these two criteria

Factors predicting mortality in severe acute pancreatitis.

PubMed

Compañy, L; Sáez, J; Martínez, J; Aparicio, J R; Laveda, R; Griñó, P; Pérez-Mateo, M

2003-01-01

Acute pancreatitis (AP) is a common disorder in which ensuing serious complications may lead to a fatal outcome in patients. To describe a large series of patients with severe AP (SAP) who were admitted to our hospital and to identify factors predicting mortality. In a retrospective study, all patients with SAP diagnosed between February 1996 and October 2000 according to the Atlanta criteria were studied. Out of a total of 363 AP patients, 67 developed SAP. The mean age of the patients was 69; the commonest etiology was biliary; 55.2% developed necrosis; the commonest systemic complication was respiratory failure (44.7%), followed by acute renal failure (35.8%) and shock (20.9%). A total of 31.3% of the patients died. Factors significantly related to mortality were age, upper digestive tract bleeding, acute renal failure, respiratory failure and shock by univariate analysis. However, pseudocysts seemed to have a protective effect. By multivariate analysis, independent prognostic factors were age, acute renal failure and respiratory failure. Patients with SAP mainly died due to systemic complications, especially acute renal failure and respiratory failure. Necrosis (in the absence or presence of infection) was not correlated with increased mortality. A pseudocyst was found to be a protective factor, probably because the definition itself led to the selection of patients who had survived multiorgan failure. Copyright 2003 S. Karger AG, Basel and IAP

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

Autologous Bone Marrow Concentrates and Concentrated Growth Factors Accelerate Bone Regeneration After Enucleation of Mandibular Pathologic Lesions.

PubMed

Talaat, Wael M; Ghoneim, Mohamed M; Salah, Omar; Adly, Osama A

2018-02-23

Stem cell therapy is a revolutionary new way to stimulate mesenchymal tissue regeneration. The platelets concentrate products started with platelet-rich plasma (PRP), followed by platelet-rich fibrin (PRF), whereas concentrated growth factors (CGF) are the latest generation of the platelets concentrate products which were found in 2011. The aim of the present study was to evaluate the potential of combining autologous bone marrow concentrates and CGF for treatment of bone defects resulting from enucleation of mandibular pathologic lesions. Twenty patients (13 males and 7 females) with mandibular benign unilateral lesions were included, and divided into 2 groups. Group I consisted of 10 patients who underwent enucleation of the lesions followed by grafting of the bony defects with autologous bone marrow concentrates and CGF. Group II consisted of 10 patients who underwent enucleation of the lesions without grafting (control). Radiographic examinations were done immediately postoperative, then at 1, 3, 6, and 12 months, to evaluate the reduction in size and changes in bone density at the bony defects. Results indicated a significant increase in bone density with respect to the baseline levels in both groups (P < 0.05). The increase in bone density was significantly higher in group I compared with group II at the 6- and 12-month follow-up examinations (P < 0.05). The percent of reduction in the defects' size was significantly higher in group I compared with group II after 12 months (P = 0.00001). In conclusion, the clinical application of autologous bone marrow concentrates with CGF is a cost effective and safe biotechnology, which accelerates bone regeneration and improves the density of regenerated bone.

Risk factors predict post-traumatic stress disorder differently in men and women

PubMed Central

Christiansen, Dorte M; Elklit, Ask

2008-01-01

Background About twice as many women as men develop post-traumatic stress disorder (PTSD), even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA), anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article. PMID:19017412

Predicting the propagation of concentration and saturation fronts in fixed-bed filters.

PubMed

Callery, O; Healy, M G

2017-10-15

The phenomenon of adsorption is widely exploited across a range of industries to remove contaminants from gases and liquids. Much recent research has focused on identifying low-cost adsorbents which have the potential to be used as alternatives to expensive industry standards like activated carbons. Evaluating these emerging adsorbents entails a considerable amount of labor intensive and costly testing and analysis. This study proposes a simple, low-cost method to rapidly assess the potential of novel media for potential use in large-scale adsorption filters. The filter media investigated in this study were low-cost adsorbents which have been found to be capable of removing dissolved phosphorus from solution, namely: i) aluminum drinking water treatment residual, and ii) crushed concrete. Data collected from multiple small-scale column tests was used to construct a model capable of describing and predicting the progression of adsorbent saturation and the associated effluent concentration breakthrough curves. This model was used to predict the performance of long-term, large-scale filter columns packed with the same media. The approach proved highly successful, and just 24-36 h of experimental data from the small-scale column experiments were found to provide sufficient information to predict the performance of the large-scale filters for up to three months. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transthyretin Concentrations in Acute Stroke Patients Predict Convalescent Rehabilitation.

PubMed

Isono, Naofumi; Imamura, Yuki; Ohmura, Keiko; Ueda, Norihide; Kawabata, Shinji; Furuse, Motomasa; Kuroiwa, Toshihiko

2017-06-01

For stroke patients, intensive nutritional management is an important and effective component of inpatient rehabilitation. Accordingly, acute care hospitals must detect and prevent malnutrition at an early stage. Blood transthyretin levels are widely used as a nutritional monitoring index in critically ill patients. Here, we had analyzed the relationship between the transthyretin levels during the acute phase and Functional Independence Measure in stroke patients undergoing convalescent rehabilitation. We investigated 117 patients who were admitted to our hospital with acute ischemic or hemorrhagic stroke from February 2013 to October 2015 and subsequently transferred to convalescent hospitals after receiving acute treatment. Transthyretin concentrations were evaluated at 3 time points as follows: at admission, and 5 and 10 days after admission. After categorizing patients into 3 groups according to the minimum transthyretin level, we analyzed the association between transthyretin and Functional Independence Measure. In our patients, transthyretin levels decreased during the first 5 days after admission and recovered slightly during the subsequent 5 days. Notably, Functional Independence Measure efficiency was significantly associated with the decrease in transthyretin levels during the 5 days after admission. Patients with lower transthyretin levels had poorer Functional Independence Measure outcomes and tended not to be discharged to their own homes. A minimal transthyretin concentration (<10 mg/dL) is predictive of a poor outcome in stroke patients undergoing convalescent rehabilitation. In particular, an early decrease in transthyretin levels suggests restricted rehabilitation efficiency. Accordingly, transthyretin levels should be monitored in acute stroke patients to indicate mid-term rehabilitation prospects. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Prediction of dissolved oxygen and carbon dioxide concentration profiles in tubular photobioreactors for microalgal culture

PubMed

Rubio; Fernandez; Perez; Camacho; Grima

1999-01-05

A model is developed for prediction of axial concentration profiles of dissolved oxygen and carbon dioxide in tubular photobioreactors used for culturing microalgae. Experimental data are used to verify the model for continuous outdoor culture of Porphyridium cruentum grown in a 200-L reactor with 100-m long tubular solar receiver. The culture was carried out at a dilution rate of 0.05 h-1 applied only during a 10-h daylight period. The quasi-steady state biomass concentration achieved was 3.0 g. L-1, corresponding to a biomass productivity of 1.5 g. L-1. d-1. The model could predict the dissolved oxygen level in both gas disengagement zone of the reactor and at the end of the loop, the exhaust gas composition, the amount of carbon dioxide injected, and the pH of the culture at each hour. In predicting the various parameters, the model took into account the length of the solar receiver tube, the rate of photosynthesis, the velocity of flow, the degree of mixing, and gas-liquid mass transfer. Because the model simulated the system behavior as a function of tube length and operational variables (superficial gas velocity in the riser, composition of carbon dioxide in the gas injected in the solar receiver and its injection rate), it could potentially be applied to rational design and scale-up of photobioreactors. Copyright 1999 John Wiley & Sons, Inc.

Prevalence and predictive factors of post-traumatic hypopituitarism.

PubMed

Klose, M; Juul, A; Poulsgaard, L; Kosteljanetz, M; Brennum, J; Feldt-Rasmussen, U

2007-08-01

To estimate the prevalence and predictive factors of hypopituitarism following traumatic brain injury (TBI). A cross-sectional cohort study. One hundred and four hospitalized TBI patients (26F/78M), median age 41 (range 18-64) years, body mass index (BMI) 25 (17-39) kg/m(2); severity: mild [Glasgow Coma Scale (GCS) score 13-15) n = 44, moderate (GCS 9-12) n = 20, severe (GCS < 9) n = 40]. Patients were evaluated 13 (10-27) months post-injury, with measurement of baseline (0800-1000 h) and post-stimulatory hormonal levels during an insulin tolerance test (ITT) (86%) or, if contraindicated, an arginine(arg)-GHRH test + Synacthen test (14%). Insufficiencies were confirmed by retesting. Hypopituitarism was found in 16 (15%) patients, affecting one axis in 10, two axes in four and more than two axes in two patients. The GH axis was most frequently affected (15%), followed by secondary hypoadrenalism (5%), hypogonadism (2%), hypothyroidism (2%) and diabetes insipidus (2%). The risk of pituitary insufficiency was increased in patients with severe TBI as opposed to mild TBI [odds ratio (OR) 10.1, 95% confidence interval (CI) 2.1-48.4, P = 0.004], and in those patients with increased intracerebral pressure [OR 6.5, 95% CI 1.0-42.2, P = 0.03]. Patients with only one affected axis were all GH deficient; 60% (n = 6) of these were overweight or obese. The prevalence of hypopituitarism was estimated at 16%. Although high, this value was lower than previously reported, and may still be overestimated because of well-known confounding factors, such as obesity. Indicators of increased TBI severity were predictive of hypopituitarism, with a high negative predictive value. Neuroendocrine evaluation should therefore be considered in patients with severe TBI, and in particular in those with increased intracerebral pressure (ICP).

Specific headache factors predict sleep disturbances among youth with migraine.

PubMed

Heyer, Geoffrey L; Rose, Sean C; Merison, Kelsey; Perkins, Sara Q; Lee, Jo Ellen M

2014-10-01

There is a paucity of pediatric data addressing the complex relationship between primary headaches and sleep disturbances. Our study objective was to explore headache-related factors that predict sleep disturbance and to compare sleep complaints with other forms of headache-related disability among youth with migraines. A prospective cohort study was conducted in patients 10-18 years old with migraine or probable migraine and without daily sleep complaints. The patients completed a 90-day internet-based headache diary. On headache days, patients rated headache intensity, answered Pediatric Migraine Disability Assessment-based questions modified for daily scoring, and reported sleep disturbances that resulted as a direct effect of proximate headaches. Fifty-two patients generated 4680 diary entries, 984 patients (21%) involved headaches. Headache intensity (P = 0.009) and timing of headache onset (P < 0.001) were predictive of sleep disturbances. Three Pediatric Migraine Disability Assessment-based items were also associated with sleep disturbances: partial school-day absence (P = 0.04), recreational activities prevented (P < 0.001), and decreased functioning during recreational activities (P < 0.001). Sleep disturbances correlated positively and significantly with daily headache disability scores (rpb = 0.35; P < 0.01). We conclude that specific headache factors predict sleep disturbances among youth with primary headaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Critical predicted no effect concentrations (PNECs) should not be based on a single toxicity test.

PubMed

Chapman, Peter M; Elphick, James R

2015-05-01

Predicted no-effect concentrations (PNECs), which represent the concentration of a substance below which an unacceptable effect most likely will not occur, are widely used for risk assessment and in environmental policy and regulation. They are typically based on single-species laboratory toxicity tests; often, a single test result for the most sensitive endpoints drives the derivation of a PNEC. In the present study, the authors provide a case study emphasizing the importance of determining the reliability of those most sensitive endpoints. Five 21-d Daphnia magna toxicity tests conducted using the same procedures by 2 laboratories gave 20% inhibitory concentration responses to a specific ionic composition of total dissolved solids that varied from 684 mg/L to more than 1510 mg/L. The concentration-response curve was shallow; thus, these differences could have been attributable to chance alone. The authors strongly recommend that the most sensitive endpoints that determine PNECs not be based on a single toxicity test result but rather on the geometric mean of at least 3 test results to adequately assess and bound test variability, especially when the concentration-response curve is shallow. © 2015 SETAC.

A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis Revision 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Narlesky, Joshua Edward; Kelly, Elizabeth J.

2015-09-10

This report documents the new PG calibration regression equation. These calibration equations incorporate new data that have become available since revision 1 of “A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis” was issued [3] The calibration equations are based on a weighted least squares (WLS) approach for the regression. The WLS method gives each data point its proper amount of influence over the parameter estimates. This gives two big advantages, more precise parameter estimates and better and more defensible estimates of uncertainties. The WLS approach makes sense both statistically and experimentally because themore » variances increase with concentration, and there are physical reasons that the higher measurements are less reliable and should be less influential. The new magnesium calibration includes a correction for sodium and separate calibration equation for items with and without chlorine. These additional calibration equations allow for better predictions and smaller uncertainties for sodium in materials with and without chlorine. Chlorine and sodium have separate equations for RICH materials. Again, these equations give better predictions and smaller uncertainties chlorine and sodium for RICH materials.« less

Identifying the necessary and sufficient number of risk factors for predicting academic failure.

PubMed

Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

2012-03-01

Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Effects of maternal serum 25-hydroxyvitamin D concentrations in the first trimester on subsequent pregnancy outcomes in an Australian population.

PubMed

Schneuer, Francisco J; Roberts, Christine L; Guilbert, Cyrille; Simpson, Judy M; Algert, Charles S; Khambalia, Amina Z; Tasevski, Vitomir; Ashton, Anthony W; Morris, Jonathan M; Nassar, Natasha

2014-02-01

Low serum 25-hydroxyvitamin D [25(OH)D] concentrations during pregnancy have been associated with adverse pregnancy outcomes in a few studies but not in other studies. We assessed the serum 25(OH)D concentration at 10-14 wk of pregnancy and its association with adverse pregnancy outcomes and examined the predictive accuracy. In this nested case-control study, we measured serum 25(OH)D in 5109 women with singleton pregnancies who were attending first-trimester screening in New South Wales, Australia. Multivariate logistic regression was conducted to examine the association between low 25(OH)D concentrations and adverse pregnancy outcomes (small for gestational age, preterm birth, preeclampsia, gestational diabetes, miscarriage, and stillbirth). The predictive accuracy of models was assessed. The median (IQR) 25(OH)D concentration for the total population was 56.4 nmol/L (43.3-69.8 nmol/L). Serum 25(OH)D concentrations showed significant variation by parity, smoking, weight, season of sampling, country of birth, and socioeconomic status. After adjustment for maternal and clinical risk factors, low 25(OH)D concentrations were not associated with most adverse pregnancy outcomes. The area under the receiver operating characteristic curve (AUC) and likelihood ratio for a composite of severe adverse pregnancy outcomes of 25(OH)D concentrations <25 nmol/L were 0.51 and 1.44, respectively, and, for risk factors alone, were 0.64 and 2.87, respectively. The addition of 25(OH)D information to maternal and clinical risk factors did not improve the ability to predict severe adverse pregnancy outcomes (AUC: 0.64; likelihood ratio: 2.32; P = 0.39). Low 25(OH)D serum concentrations in the first trimester of pregnancy are not associated with adverse pregnancy outcomes and do not predict complications any better than routinely assessed clinical and maternal risk-factor information.

A factor analysis approach to examining relationships among ovarian steroid concentrations, gonadotrophin concentrations and menstrual cycle length characteristics in healthy, cycling women

PubMed Central

Barrett, E.S.; Thune, I.; Lipson, S.F.; Furberg, A.-S.; Ellison, P.T.

2013-01-01

STUDY QUESTION How are ovarian steroid concentrations, gonadotrophins and menstrual cycle characteristics inter-related within normal menstrual cycles? SUMMARY ANSWER Within cycles, measures of estradiol production are highly related to one another, as are measures of progesterone production; however, the two hormones also show some independence from one another, and measures of cycle length and gonadotrophin concentrations show even greater independence, indicating minimal integration within cycles. WHAT IS KNOWN ALREADY The menstrual cycle is typically conceptualized as a cohesive unit, with hormone levels, follicular development and ovulation all closely inter-related within a single cycle. Empirical support for this idea is limited, however, and to our knowledge, no analysis has examined the relationships among all of these components simultaneously. STUDY DESIGN, SIZE, DURATION A total of 206 healthy, cycling Norwegian women participated in a prospective cohort study (EBBA-I) over the duration of a single menstrual cycle. Of these, 192 contributed hormonal and cycle data to the current analysis. PARTICIPANTS/MATERIALS, SETTING, METHODS Subjects provided daily saliva samples throughout the menstrual cycle from which estradiol and progesterone concentrations were measured. FSH and LH concentrations were measured in serum samples from three points in the same menstrual cycle and cycle length characteristics were calculated based on hormonal data and menstrual records. A factor analysis was conducted to examine the underlying relationships among 22 variables derived from the hormonal data and menstrual cycle characteristics. MAIN RESULTS AND THE ROLE OF CHANCE Six rotated factors emerged, explaining 80% of the variance in the data. Of these, factors representing estradiol and progesterone concentrations accounted for 37 and 13% of the variance, respectively. There was some association between measures of estradiol and progesterone production within cycles; however

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

[Lightning-caused fire, its affecting factors and prediction: a review].

PubMed

Zhang, Ji-Li; Bi, Wu; Wang, Xiao-Hong; Wang, Zi-Bo; Li, Di-Fei

2013-09-01

Lightning-caused fire is the most important natural fire source. Its induced forest fire brings enormous losses to human beings and ecological environment. Many countries have paid great attention to the prediction of lightning-caused fire. From the viewpoint of the main factors affecting the formation of lightning-caused fire, this paper emphatically analyzed the effects and action mechanisms of cloud-to-ground lightning, fuel, meteorology, and terrain on the formation and development process of lightning-caused fire, and, on the basis of this, summarized and reviewed the logistic model, K-function, and other mathematical methods widely used in prediction research of lightning-caused fire. The prediction methods and processes of lightning-caused fire in America and Canada were also introduced. The insufficiencies and their possible solutions for the present researches as well as the directions of further studies were proposed, aimed to provide necessary theoretical basis and literature reference for the prediction of lightning-caused fire in China.

Scattering Solar Thermal Concentrators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giebink, Noel C.

2015-01-31

. However, the concentrator optical efficiency was found to decrease significantly with increasing aperture width beyond 0.5 m due to parasitic waveguide out-coupling loss and low-level absorption that become dominant at larger scale. A heat transfer model was subsequently implemented to predict collector fluid heat gain and outlet temperature as a function of flow rate using the optical model as a flux input. It was found that the aperture width size limitation imposed by the optical efficiency characteristics of the waveguide limits the absolute optical power delivered to the heat transfer element per unit length. As compared to state-of-the-art parabolic trough CPV system aperture widths approaching 5 m, this limitation leads to an approximate factor of order of magnitude increase in heat transfer tube length to achieve the same heat transfer fluid outlet temperature. The conclusion of this work is that scattering solar thermal concentration cannot be implemented at the scale and efficiency required to compete with the performance of current parabolic trough CSP systems. Applied within the alternate context of CPV, however, the results of this work have likely opened up a transformative new path that enables quasi-static, high efficiency CPV to be implemented on rooftops in the form factor of traditional fixed-panel photovoltaics.« less

Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2012-01-01

This product "Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area" is a 1:250,000-scale vector spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach

DTIC Science & Technology

2011-09-01

insulin for at least 12 mo, and had glycated hemoglobin ( HbA1c ) >6.1%. In the Guardian study, subjects were included if they were between 3 and 7 yr...old or between 12 and 18 yr old, had been diagnosed with type 1 diabetes for more than 1 yr, had been using an insulin pump, and had HbA1c ≤ 10.0...oral agents, basal insulin, or both for at least 3 mo, and had HbA1c between 7% and 12%. Predicting Human Subcutaneous Glucose Concentration in

Predictive factors for overall quality of life in patients with advanced cancer.

PubMed

Cramarossa, Gemma; Chow, Edward; Zhang, Liying; Bedard, Gillian; Zeng, Liang; Sahgal, Arjun; Vassiliou, Vassilios; Satoh, Takefumi; Foro, Palmira; Ma, Brigette B Y; Chie, Wei-Chu; Chen, Emily; Lam, Henry; Bottomley, Andrew

2013-06-01

This study examined which domains/symptoms from the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 15 Palliative (QLQ-C15-PAL), an abbreviated version of the health-related EORTC QLQ-C30 questionnaire designed for palliative cancer patients, were predictive of overall quality of life (QOL) in advanced cancer patients. Patients with advanced cancer from six countries completed the QLQ-C15-PAL at consultation and at one follow-up point. Univariate and multivariate regression analyses were conducted to determine the predictive value of the EORTC QLQ-C15-PAL functional/symptom scores for global QOL (question 15). Three hundred forty-nine patients completed the EORTC QLQ-C15-PAL at baseline. In the total patient sample, worse emotional functioning, pain, and appetite loss were the most significant predictive factors for worse QOL. In the subgroup of patients with bone metastases (n = 240), the domains mentioned above were also the most significant predictors, whereas in patients with brain metastases (n = 109), worse physical and emotional functioning most significantly predicted worse QOL. One-month follow-up in 267 patients revealed that the significant predictors changed somewhat over time. For example, in the total patient sample, physical functioning, fatigue, and appetite loss were significant predictors at the follow-up point. A sub-analysis of predictive factors affecting QOL by primary cancer (lung, breast, and prostate) was also conducted for the total patient sample. Deterioration of certain EORTC QLQ-C15-PAL functional/symptom scores significantly contributes to worse overall QOL. Special attention should be directed to managing factors most influential on overall QOL to ensure optimal management of advanced cancer patients.

Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

EPA Science Inventory

Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

PubMed Central

2011-01-01

Background This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. Results Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring

Platelet-rich concentrates differentially release growth factors and induce cell migration in vitro.

PubMed

Schär, Michael O; Diaz-Romero, Jose; Kohl, Sandro; Zumstein, Matthias A; Nesic, Dobrila

2015-05-01

Platelet-rich concentrates are used as a source of growth factors to improve the healing process. The diverse preparation protocols and the gaps in knowledge of their biological properties complicate the interpretation of clinical results. In this study we aimed to (1) analyze the concentration and kinetics of growth factors released from leukocyte- and platelet-rich fibrin (L-PRF), leukocyte- and platelet-rich plasma (L-PRP), and natural blood clot during in vitro culture; (2) investigate the migration of mesenchymal stem cells (MSCs) and human umbilical vein endothelial cells (HUVECs) as a functional response to the factors released; and (3) uncover correlations between individual growth factors with the initial platelet/leukocyte counts or the induced cell migration. L-PRF, L-PRP, and natural blood clot prepared from 11 donors were cultured in vitro for 28 days and media supernatants collected after 8 hours and 1, 3, 7, 14, and 28 days. Released transforming growth factor β1 (TGF-β1), vascular endothelial growth factor (VEGF), insulin growth factor (IGF-1), platelet-derived growth factor AB (PDGF-AB), and interleukin-1β (IL-1β) were measured in the supernatants with enzyme-linked immunosorbent assay. Migration of MSC and HUVEC induced by the supernatants was evaluated in Boyden chambers. More TGF-ß1 was released (mean ± SD in pg/mL of blood) from L-PRF (37,796 ± 5492) compared with L-PRP (23,738 ± 6848; p < 0.001) and blood clot (3739 ± 4690; p < 0.001), whereas more VEGF and IL-1ß were released from blood clot (1933 ± 704 and 2053 ± 908, respectively) compared with both L-PRP (642 ± 208; p < 0.001 and 273 ± 386; p < 0.001, respectively) and L-PRF (852 ± 376; p < 0.001 and 65 ± 56, p < 0.001, respectively). No differences were observed in IGF-1 and PDGF-AB released from any of the concentrates. TGF-β1 release peaked at Day 7 in L-PRF and at 8 hours and Day 7 in L-PRP and 8 hours and Day 14 in blood clot. In all concentrates, main release of VEGF

Sample pre-concentration with high enrichment factors at a fixed location in paper-based microfluidic devices.

PubMed

Yeh, Shih-Hao; Chou, Kuang-Hua; Yang, Ruey-Jen

2016-03-07

The lack of sensitivity is a major problem among microfluidic paper-based analytical devices (μPADs) for early disease detection and diagnosis. Accordingly, the present study presents a method for improving the enrichment factor of low-concentration biomarkers by using shallow paper-based channels realized through a double-sided wax-printing process. In addition, the enrichment factor is further enhanced by exploiting the ion concentration polarization (ICP) effect on the cathodic side of the nanoporous membrane, in which a stationary sample plug is obtained. The occurrence of ICP on the shallow-channel μPAD is confirmed by measuring the current-voltage response as the external voltage is increased from 0 to 210 V (or the field strength from 0 to 1.05 × 10(4) V m(-1)) over 600 s. In addition, to the best of our knowledge, the electroosmotic flow (EOF) speed on the μPAD fabricated with a wax-channel is measured for the first time using a current monitoring method. The experimental results show that for a fluorescein sample, the concentration factor is increased from 130-fold in a conventional full-thickness paper channel to 944-fold in the proposed shallow channel. Furthermore, for a fluorescein isothiocyanate-labeled bovine serum albumin (FITC-BSA) sample, the proposed shallow-channel μPAD achieves an 835-fold improvement in the concentration factor. The concentration technique presented here provides a novel strategy for enhancing the detection sensitivity of μPAD applications.

Predicting the concentration of verotoxin-producing Escherichia coli bacteria during processing and storage of fermented raw-meat sausages.

PubMed

Quinto, E J; Arinder, P; Axelsson, L; Heir, E; Holck, A; Lindqvist, R; Lindblad, M; Andreou, P; Lauzon, H L; Marteinsson, V Þ; Pin, C

2014-05-01

A model to predict the population density of verotoxigenic Escherichia coli (VTEC) throughout the elaboration and storage of fermented raw-meat sausages (FRMS) was developed. Probabilistic and kinetic measurement data sets collected from publicly available resources were completed with new measurements when required and used to quantify the dependence of VTEC growth and inactivation on the temperature, pH, water activity (aw), and concentration of lactic acid. Predictions were compared with observations in VTEC-contaminated FRMS manufactured in a pilot plant. Slight differences in the reduction of VTEC were predicted according to the fermentation temperature, 24 or 34°C, with greater inactivation at the highest temperature. The greatest reduction was observed during storage at high temperatures. A population decrease greater than 6 decimal logarithmic units was observed after 66 days of storage at 25°C, while a reduction of only ca. 1 logarithmic unit was detected at 12°C. The performance of our model and other modeling approaches was evaluated throughout the processing of dry and semidry FRMS. The greatest inactivation of VTEC was predicted in dry FRMS with long drying periods, while the smallest reduction was predicted in semidry FMRS with short drying periods. The model is implemented in a computing tool, E. coli SafeFerment (EcSF), freely available from http://www.ifr.ac.uk/safety/EcoliSafeFerment. EcSF integrates growth, probability of growth, and thermal and nonthermal inactivation models to predict the VTEC concentration throughout FRMS manufacturing and storage under constant or fluctuating environmental conditions.

Predicting the Concentration of Verotoxin-Producing Escherichia coli Bacteria during Processing and Storage of Fermented Raw-Meat Sausages

PubMed Central

Quinto, E. J.; Arinder, P.; Axelsson, L.; Heir, E.; Holck, A.; Lindqvist, R.; Lindblad, M.; Andreou, P.; Lauzon, H. L.; Marteinsson, V. Þ.

2014-01-01

A model to predict the population density of verotoxigenic Escherichia coli (VTEC) throughout the elaboration and storage of fermented raw-meat sausages (FRMS) was developed. Probabilistic and kinetic measurement data sets collected from publicly available resources were completed with new measurements when required and used to quantify the dependence of VTEC growth and inactivation on the temperature, pH, water activity (aw), and concentration of lactic acid. Predictions were compared with observations in VTEC-contaminated FRMS manufactured in a pilot plant. Slight differences in the reduction of VTEC were predicted according to the fermentation temperature, 24 or 34°C, with greater inactivation at the highest temperature. The greatest reduction was observed during storage at high temperatures. A population decrease greater than 6 decimal logarithmic units was observed after 66 days of storage at 25°C, while a reduction of only ca. 1 logarithmic unit was detected at 12°C. The performance of our model and other modeling approaches was evaluated throughout the processing of dry and semidry FRMS. The greatest inactivation of VTEC was predicted in dry FRMS with long drying periods, while the smallest reduction was predicted in semidry FMRS with short drying periods. The model is implemented in a computing tool, E. coli SafeFerment (EcSF), freely available from http://www.ifr.ac.uk/safety/EcoliSafeFerment. EcSF integrates growth, probability of growth, and thermal and nonthermal inactivation models to predict the VTEC concentration throughout FRMS manufacturing and storage under constant or fluctuating environmental conditions. PMID:24561587

Growth factor and pro-inflammatory cytokine contents in platelet-rich plasma (PRP), plasma rich in growth factors (PRGF), advanced platelet-rich fibrin (A-PRF), and concentrated growth factors (CGF).

PubMed

Masuki, Hideo; Okudera, Toshimitsu; Watanebe, Taisuke; Suzuki, Masashi; Nishiyama, Kazuhiko; Okudera, Hajime; Nakata, Koh; Uematsu, Kohya; Su, Chen-Yao; Kawase, Tomoyuki

2016-12-01

The development of platelet-rich fibrin (PRF) drastically simplified the preparation procedure of platelet-concentrated biomaterials, such as platelet-rich plasma (PRP), and facilitated their clinical application. PRF's clinical effectiveness has often been demonstrated in pre-clinical and clinical studies; however, it is still controversial whether growth factors are significantly concentrated in PRF preparations to facilitate wound healing and tissue regeneration. To address this matter, we performed a comparative study of growth factor contents in PRP and its derivatives, such as advanced PRF (A-PRF) and concentrated growth factors (CGF). PRP and its derivatives were prepared from the same peripheral blood samples collected from healthy donors. A-PRF and CGF preparations were homogenized and centrifuged to produce extracts. Platelet and white blood cell counts in A-PRF and CGF preparations were determined by subtracting those counts in red blood cell fractions, supernatant acellular serum fractions, and A-PRF/CGF exudate fractions from those counts of whole blood samples. Concentrations of growth factors (TGF-β1, PDGF-BB, VEGF) and pro-inflammatory cytokines (IL-1β, IL-6) were determined using ELISA kits. Compared to PRP preparations, both A-PRF and CGF extracts contained compatible or higher levels of platelets and platelet-derived growth factors. In a cell proliferation assay, both A-PRF and CGF extracts significantly stimulated the proliferation of human periosteal cells without significant reduction at higher doses. These data clearly demonstrate that both A-PRF and CGF preparations contain significant amounts of growth factors capable of stimulating periosteal cell proliferation, suggesting that A-PRF and CGF preparations function not only as a scaffolding material but also as a reservoir to deliver certain growth factors at the site of application.

Climate and Physiography Predict Mercury Concentrations in Game Fish Species in Quebec Lakes Better than Anthropogenic Disturbances.

PubMed

Lucotte, Marc; Paquet, Serge; Moingt, Matthieu

2016-05-01

The fluctuations of mercury levels (Hg) in fish consumed by sport fishers in North-Eastern America depend upon a plethora of interrelated biological and abiological factors. To identify the dominant factors ultimately controlling fish Hg concentrations, we compiled mercury levels (Hg) during the 1976-2010 period in 90 large natural lakes in Quebec (Canada) for two major game species: northern pike (Esox lucius) and walleye (Sander vitreus). Our statistical analysis included 28 geographic information system variables and 15 climatic variables, including sulfate deposition. Higher winter temperatures explained 36% of the variability in higher walleye growth rates, in turn accounting for 54% of the variability in lower Hg concentrations. For northern pike, the dominance of a flat topography in the watershed explained 31% of the variability in lower Hg concentrations. Higher mean annual temperatures explained 27% of the variability in higher pike Hg concentrations. Pelagic versus littoral preferred habitats for walleye and pike respectively could explain the contrasted effect of temperature between the two species. Heavy logging could only explain 2% of the increase in walleye Hg concentrations. The influence of mining on fish Hg concentrations appeared to be masked by climatic effects.

Metabolic health assessment of zoo elephants: Management factors predicting leptin levels and the glucose-to-insulin ratio and their associations with health parameters

PubMed Central

Brown, Janine L.

2017-01-01

Screening for metabolic-related health problems can enhance animal welfare, so the purpose of this study was to conduct the first metabolic health assessment of zoo elephants and use epidemiological methods to determine how factors in the captive environment were associated with metabolic hormone concentrations. In addition, we examined relationships between metabolic status and several fitness parameters: foot health, musculoskeletal health, reproductive cyclicity, and body condition. Two blood samples were collected 2 weeks apart from 87 Asian (Elephas maximus) and 105 African (Loxodonta africana) elephants managed by zoos accredited by the Association of Zoos and Aquariums for analysis of serum leptin, insulin, glucose and the glucose-to-insulin ratio (G:I). In females, mean (± SD) leptin concentrations and the G:I were lower (P<0.05) in Asian (3.93 ± 2.21 ng/ml and 110 ± 86 units) compared to African (4.37 ± 2.89 ng/ml and 208 ± 133 units) elephants, respectively. For males, mean leptin and the G:I were 4.99 ± 3.61 ng/ml and 253 ± 181 units for Asian, and 3.72 ± 2.00 ng/ml and 326 ± 231 units for African elephants, respectively, with no differences between species (P>0.05). As mean leptin concentration increased there was an increase in the odds of a female being non-cycling (P = 0.0083). The G:I was associated inversely with body condition (P = 0.0002); as the G:I increased there was a decreased risk of BCS = 4 or 5 as compared to the ideal, or BCS = 3. Neither leptin nor G:I were predictive of foot or musculoskeletal health scores. Factors related to walking and feeding practices were most influential in predicting metabolic status, whereas social and housing factors showed smaller, but significant effects. The metabolic health benefits of walking were detected if the time spent in staff-directed walking was 7 hours or more per week. The most protective feeding practices included implementing a random rather than predictable feeding schedule and

Metabolic health assessment of zoo elephants: Management factors predicting leptin levels and the glucose-to-insulin ratio and their associations with health parameters.

PubMed

Morfeld, Kari A; Brown, Janine L

2017-01-01

Screening for metabolic-related health problems can enhance animal welfare, so the purpose of this study was to conduct the first metabolic health assessment of zoo elephants and use epidemiological methods to determine how factors in the captive environment were associated with metabolic hormone concentrations. In addition, we examined relationships between metabolic status and several fitness parameters: foot health, musculoskeletal health, reproductive cyclicity, and body condition. Two blood samples were collected 2 weeks apart from 87 Asian (Elephas maximus) and 105 African (Loxodonta africana) elephants managed by zoos accredited by the Association of Zoos and Aquariums for analysis of serum leptin, insulin, glucose and the glucose-to-insulin ratio (G:I). In females, mean (± SD) leptin concentrations and the G:I were lower (P<0.05) in Asian (3.93 ± 2.21 ng/ml and 110 ± 86 units) compared to African (4.37 ± 2.89 ng/ml and 208 ± 133 units) elephants, respectively. For males, mean leptin and the G:I were 4.99 ± 3.61 ng/ml and 253 ± 181 units for Asian, and 3.72 ± 2.00 ng/ml and 326 ± 231 units for African elephants, respectively, with no differences between species (P>0.05). As mean leptin concentration increased there was an increase in the odds of a female being non-cycling (P = 0.0083). The G:I was associated inversely with body condition (P = 0.0002); as the G:I increased there was a decreased risk of BCS = 4 or 5 as compared to the ideal, or BCS = 3. Neither leptin nor G:I were predictive of foot or musculoskeletal health scores. Factors related to walking and feeding practices were most influential in predicting metabolic status, whereas social and housing factors showed smaller, but significant effects. The metabolic health benefits of walking were detected if the time spent in staff-directed walking was 7 hours or more per week. The most protective feeding practices included implementing a random rather than predictable feeding schedule and

The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

PubMed

Thiels, Cornelius A; Yu, Denny; Abdelrahman, Amro M; Habermann, Elizabeth B; Hallbeck, Susan; Pasupathy, Kalyan S; Bingener, Juliane

2017-01-01

Reliable prediction of operative duration is essential for improving patient and care team satisfaction, optimizing resource utilization and reducing cost. Current operative scheduling systems are unreliable and contribute to costly over- and underestimation of operative time. We hypothesized that the inclusion of patient-specific factors would improve the accuracy in predicting operative duration. We reviewed all elective laparoscopic cholecystectomies performed at a single institution between 01/2007 and 06/2013. Concurrent procedures were excluded. Univariate analysis evaluated the effect of age, gender, BMI, ASA, laboratory values, smoking, and comorbidities on operative duration. Multivariable linear regression models were constructed using the significant factors (p < 0.05). The patient factors model was compared to the traditional surgical scheduling system estimates, which uses historical surgeon-specific and procedure-specific operative duration. External validation was done using the ACS-NSQIP database (n = 11,842). A total of 1801 laparoscopic cholecystectomy patients met inclusion criteria. Female sex was associated with reduced operative duration (-7.5 min, p < 0.001 vs. male sex) while increasing BMI (+5.1 min BMI 25-29.9, +6.9 min BMI 30-34.9, +10.4 min BMI 35-39.9, +17.0 min BMI 40 + , all p < 0.05 vs. normal BMI), increasing ASA (+7.4 min ASA III, +38.3 min ASA IV, all p < 0.01 vs. ASA I), and elevated liver function tests (+7.9 min, p < 0.01 vs. normal) were predictive of increased operative duration on univariate analysis. A model was then constructed using these predictive factors. The traditional surgical scheduling system was poorly predictive of actual operative duration (R 2  = 0.001) compared to the patient factors model (R 2  = 0.08). The model remained predictive on external validation (R 2  = 0.14).The addition of surgeon as a variable in the institutional model further improved predictive ability of the model

Application of transmission infrared spectroscopy and partial least squares regression to predict immunoglobulin G concentration in dairy and beef cow colostrum.

PubMed

Elsohaby, Ibrahim; Windeyer, M Claire; Haines, Deborah M; Homerosky, Elizabeth R; Pearson, Jennifer M; McClure, J Trenton; Keefe, Greg P

2018-03-06

The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were -3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples

Predictive factors for postoperative visual function of primary chronic rhegmatogenous retinal detachment after scleral buckling.

PubMed

Fang, Wei; Li, Jiu-Ke; Jin, Xiao-Hong; Dai, Yuan-Min; Li, Yu-Min

2016-01-01

To evaluate predictive factors for postoperative visual function of primary chronic rhegmatgenous retinal detachment (RRD) after sclera buckling (SB). Totally 48 patients (51 eyes) with primary chronic RRD were included in this prospective interventional clinical cases study, which underwent SB alone from June 2008 to December 2014. Age, sex, symptoms duration, detached extension, retinal hole position, size, type, fovea on/off, proliferative vitreoretinopathy (PVR), posterior vitreous detachment (PVD), baseline best corrected visual acuity (BCVA), operative duration, follow up duration, final BCVA were measured. Pearson correlation analysis, Spearman correlation analysis and multivariate linear stepwise regression were used to confirm predictive factors for better final visual acuity. Student's t-test, Wilcoxon two-sample test, Chi-square test and logistic stepwise regression were used to confirm predictive factors for better vision improvement. Baseline BCVA was 0.8313±0.6911 logMAR and final BCVA was 0.4761±0.4956 logMAR. Primary surgical success rate was 92.16% (47/51). Correlation analyses revealed shorter symptoms duration (r=0.3850, P=0.0053), less detached area (r=0.5489, P<0.0001), fovea (r=0.4605, P=0.0007), no PVR (r=0.3138, P=0.0250), better baseline BCVA (r=0.7291, P<0.0001), shorter operative duration (r=0.3233, P=0.0207) and longer follow up (r=-0.3358, P=0.0160) were related with better final BCVA, while independent predictive factors were better baseline BCVA [partial R-square (PR(2))=0.5316, P<0.0001], shorter symptoms duration (PR(2)=0.0609, P=0.0101), longer follow up duration (PR(2)=0.0278, P=0.0477) and shorter operative duration (PR(2)=0.0338, P=0.0350). Patients with vision improvement took up 49.02% (25/51). Univariate and multivariate analyses both revealed predictive factors for better vision improvement were better baseline vision [odds ratio (OR) =50.369, P=0.0041] and longer follow up duration (OR=1.144, P=0.0067). Independent

Evaluation of missing value methods for predicting ambient BTEX concentrations in two neighbouring cities in Southwestern Ontario Canada

NASA Astrophysics Data System (ADS)

Miller, Lindsay; Xu, Xiaohong; Wheeler, Amanda; Zhang, Tianchu; Hamadani, Mariam; Ejaz, Unam

2018-05-01

High density air monitoring campaigns provide spatial patterns of pollutant concentrations which are integral in exposure assessment. Such analysis can assist with the determination of links between air quality and health outcomes, however, problems due to missing data can threaten to compromise these studies. This research evaluates four methods; mean value imputation, inverse distance weighting (IDW), inter-species ratios, and regression, to address missing spatial concentration data ranging from one missing data point up to 50% missing data. BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations were measured in Windsor and Sarnia, Ontario in the fall of 2005. Concentrations and inter-species ratios were generally similar between the two cities. Benzene (B) was observed to be higher in Sarnia, whereas toluene (T) and the T/B ratios were higher in Windsor. Using these urban, industrialized cities as case studies, this research demonstrates that using inter-species ratios or regression of the data for which there is complete information, along with one measured concentration (i.e. benzene) to predict for missing concentrations (i.e. TEX) results in good agreement between predicted and measured values. In both cities, the general trend remains that best agreement is observed for the leave-one-out scenario, followed by 10% and 25% missing, and the least agreement for the 50% missing cases. In the absence of any known concentrations IDW can provide reasonable agreement between observed and estimated concentrations for the BTEX species, and was superior over mean value imputation which was not able to preserve the spatial trend. The proposed methods can be used to fill in missing data, while preserving the general characteristics and rank order of the data which are sufficient for epidemiologic studies.

Conifer density within lake catchments predicts fish mercury concentrations in remote subalpine lakes

USGS Publications Warehouse

Eagles-Smith, Collin A.; Herring, Garth; Johnson, Branden L.; Graw, Rick

2016-01-01

Remote high-elevation lakes represent unique environments for evaluating the bioaccumulation of atmospherically deposited mercury through freshwater food webs, as well as for evaluating the relative importance of mercury loading versus landscape influences on mercury bioaccumulation. The increase in mercury deposition to these systems over the past century, coupled with their limited exposure to direct anthropogenic disturbance make them useful indicators for estimating how changes in mercury emissions may propagate to changes in Hg bioaccumulation and ecological risk. We evaluated mercury concentrations in resident fish from 28 high-elevation, sub-alpine lakes in the Pacific Northwest region of the United States. Fish total mercury (THg) concentrations ranged from 4 to 438 ng/g wet weight, with a geometric mean concentration (±standard error) of 43 ± 2 ng/g ww. Fish THg concentrations were negatively correlated with relative condition factor, indicating that faster growing fish that are in better condition have lower THg concentrations. Across the 28 study lakes, mean THg concentrations of resident salmonid fishes varied as much as 18-fold among lakes. We used a hierarchal statistical approach to evaluate the relative importance of physiological, limnological, and catchment drivers of fish Hg concentrations. Our top statistical model explained 87% of the variability in fish THg concentrations among lakes with four key landscape and limnological variables: catchment conifer density (basal area of conifers within a lake's catchment), lake surface area, aqueous dissolved sulfate, and dissolved organic carbon. Conifer density within a lake's catchment was the most important variable explaining fish THg concentrations across lakes, with THg concentrations differing by more than 400 percent across the forest density spectrum. These results illustrate the importance of landscape characteristics in controlling mercury bioaccumulation in fish.

Conifer density within lake catchments predicts fish mercury concentrations in remote subalpine lakes.

PubMed

Eagles-Smith, Collin A; Herring, Garth; Johnson, Branden; Graw, Rick

2016-05-01

Remote high-elevation lakes represent unique environments for evaluating the bioaccumulation of atmospherically deposited mercury through freshwater food webs, as well as for evaluating the relative importance of mercury loading versus landscape influences on mercury bioaccumulation. The increase in mercury deposition to these systems over the past century, coupled with their limited exposure to direct anthropogenic disturbance make them useful indicators for estimating how changes in mercury emissions may propagate to changes in Hg bioaccumulation and ecological risk. We evaluated mercury concentrations in resident fish from 28 high-elevation, sub-alpine lakes in the Pacific Northwest region of the United States. Fish total mercury (THg) concentrations ranged from 4 to 438 ng/g wet weight, with a geometric mean concentration (±standard error) of 43 ± 2 ng/g ww. Fish THg concentrations were negatively correlated with relative condition factor, indicating that faster growing fish that are in better condition have lower THg concentrations. Across the 28 study lakes, mean THg concentrations of resident salmonid fishes varied as much as 18-fold among lakes. We used a hierarchal statistical approach to evaluate the relative importance of physiological, limnological, and catchment drivers of fish Hg concentrations. Our top statistical model explained 87% of the variability in fish THg concentrations among lakes with four key landscape and limnological variables: catchment conifer density (basal area of conifers within a lake's catchment), lake surface area, aqueous dissolved sulfate, and dissolved organic carbon. Conifer density within a lake's catchment was the most important variable explaining fish THg concentrations across lakes, with THg concentrations differing by more than 400 percent across the forest density spectrum. These results illustrate the importance of landscape characteristics in controlling mercury bioaccumulation in fish. Published by Elsevier

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

PubMed Central

2013-01-01

Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. PMID:23497145

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

PubMed

Yang, Qingsheng; Mwenda, Kevin M; Ge, Miao

2013-03-12

The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China.The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Predicting Factors of Chronic Subdural Hematoma Following Surgical Clipping in Unruptured and Ruptured Intracranial Aneurysm.

PubMed

Kwon, Min-Yong; Kim, Chang-Hyun; Lee, Chang-Young

2016-09-01

The aim of this study is to analyze the differences in the incidence, predicting factors, and clinical course of chronic subdural hematoma (CSDH) following surgical clipping between unruptured (UIA) and ruptured intracranial aneurysm (RIA). We conducted a retrospective analysis of 752 patients (UIA : 368 and RIA : 384) who underwent surgical clipping during 8 years. The incidence and predicting factors of CSDH development in the UIA and RIA were compared according to medical records and radiological data. The incidence of postoperative CSDH was higher in the UIA (10.9%) than in the RIA (3.1%) (p=0.000). In multivariate analysis, a high Hounsfield (HF) unit (blood clots) for subdural fluid collection (SFC), persistence of SFC ≥5 mm and male sex in the UIA and A high HF unit for SFC and SFC ≥5 mm without progression to hydrocephalus in the RIA were identified as the independent predicting factors for CSDH development (p<0.05). There were differences in the incidence and predicting factors for CSDH following surgical clipping between UIA and RIA. Blood clots in the subdural space and persistence of SFC ≥5 mm were predicting factors in both UIA and RIA. However, progression to hydrocephalus may have in part contributed to low CSDH development in the RIA. We suggest that cleaning of blood clots in the subdural space and efforts to minimize SFC ≥5 mm at the end of surgery is helpful to prevent CSDH following aneurysmal clipping.

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Insulin-Like Growth Factor 1 Predicts Post-Load Hypoglycemia following Bariatric Surgery: A Prospective Cohort Study

PubMed Central

Itariu, Bianca K.; Zeyda, Maximilian; Prager, Gerhard; Stulnig, Thomas M.

2014-01-01

Postprandial hypoglycemia is a complication following gastric bypass surgery, which frequently remains undetected. Severe hypoglycemic episodes, however, put patients at risk, e.g., for syncope. A major cause of hypoglycemia following gastric bypass is hyperinsulinemic nesidioblastosis. Since pancreatic islets in nesidioblastosis overexpress insulin-like growth factor 1 (IGF-1) receptor α and administration of recombinant IGF-1 provokes hypoglycemia, our main objective was to investigate the occurrence of post-load hypoglycemia one year after bariatric surgery and its relation to pre- and post-operative IGF-1 serum concentrations. We evaluated metabolic parameters including 2 h 75 g oral glucose tolerance test (OGTT) and measured IGF-1 serum concentration in thirty-six non-diabetic patients (29 f/7 m), aged 41.3±2.0 y with a median (IQR) BMI of 30.9 kg/m2 (27.5–34.3 kg/m2), who underwent elective bariatric surgery (predominantly gastric bypass, 83%) at our hospital. Post-load hypoglycemia as defined by a 2 h glucose concentration <60 mg/dl was detected in 50% of patients. Serum insulin and C-peptide concentration during the OGTT and HOMA-IR (homeostatic model assessment–insulin resistance) were similar in hypoglycemic and euglycemic patients. Strikingly, pre- and post-operative serum IGF-1 concentrations were significantly higher in hypoglycemic patients (p = 0.012 and p = 0.007 respectively). IGF-1 serum concentration before surgery negatively correlated with 2 h glucose concentration during the OGTT (rho = −0.58, p = 0.0003). Finally, IGF-1 serum concentrations before and after surgery significantly predicted post-load hypoglycemia with odds ratios of 1.28 (95%CI:1.03–1.55, p = 0.029) and 1.18 (95%CI:1.03–1.33, p = 0.015), respectively, for each 10 ng/ml increment. IGF-1 serum concentration could be a valuable biomarker to identify patients at risk for hypoglycemia following bariatric surgery independently of a diagnostic OGTT

[Cesarean after labor induction: Risk factors and prediction score].

PubMed

Branger, B; Dochez, V; Gervier, S; Winer, N

2018-05-01

The objective of the study is to determine the risk factors for caesarean section at the time of labor induction, to establish a prediction algorithm, to evaluate its relevance and to compare the results with observation. A retrospective study was carried out over a year at Nantes University Hospital with 941 cervical ripening and labor inductions (24.1%) terminated by 167 caesarean sections (17.8%). Within the cohort, a case-control study was conducted with 147 caesarean sections and 148 vaginal deliveries. A multivariate analysis was carried out with a logistic regression allowing the elaboration of an equation of prediction and an ROC curve and the confrontation between the prediction and the reality. In univariate analysis, six variables were significant: nulliparity, small size of the mother, history of scarried uterus, use of prostaglandins as a mode of induction, unfavorable Bishop score<6, variety of posterior release. In multivariate analysis, five variables were significant: nulliparity, maternal size, maternal BMI, scar uterus and Bishop score. The most predictive model corresponded to an area under the curve of 0.86 (0.82-0.90) with a correct prediction percentage ("well classified") of 67.6% for a caesarean section risk of 80%. The prediction criteria would make it possible to inform the woman and the couple about the potential risk of Caesarean section in urgency or to favor a planned Caesarean section or a low-lying attempt on more objective, repeatable and transposable arguments in a medical team. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Relations that affect the probability and prediction of nitrate concentration in private wells in the glacial aquifer system in the United States

USGS Publications Warehouse

Warner, Kelly L.; Arnold, Terri L.