Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

BACKGROUND:Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. METHODS:Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. RESULTS:Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. CONCLUSIONS:The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations

Enhancing saccharification of cassava stems by starch hydrolysis prior to pretreatment

Chemical characterization of cassava stems from different origin revealed that glucans accounted for 54-63% of the dry weight, whereas 35-67% of these glucans consisted of starch. The cassava stems were subjected to a saccharification study including starch hydrolysis, pretreatment with either sulfuric acid or 1-ethyl-3-methylimidazolium acetate ([Emim]OAc), and enzymatic hydrolysis of cellulose. Starch hydrolysis prior to pretreatment decreased sugar degradation, improved enzymatic convertibility of cellulose, and increased overall glucan conversion. Glucan recovery after pretreatment of starch-free cassava stems (SFCS) was around 85%, but below 52% when the stems were pretreated under the same conditions without preparatory starch hydrolysis. The total amount of hydrolyzed glucan after cellulose hydrolysis was two-fold higher for pretreated SFCS than for directly pretreated stems. Pretreatment with [Emim]OAc resulted in 20% higher glucan conversion than pretreatment with acid. Pyrolysis-GC/MS, X-ray diffraction, CP/MAS C-13 NMR and FTIR analyses revealed major differences between H2SO4- and [Emim]OAc-pretreated material. Bio4Energ

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

BackgroundDetailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.MethodsTime-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.ResultsMaximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.ConclusionsThe random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations

Lagged Effects of Exposure to Air Pollutants on the Risk of Pulmonary Tuberculosis in a Highly Polluted Region

Background: Although significant correlations have been observed between air pollutants and the development of pulmonary tuberculosis (PTB) in many developed countries, data are scarce for developing and highly polluted regions. Method: A combined Poisson generalized linear regression–distributed lag nonlinear model was used to determine the associations between long-term exposure (2005–2017) to air pollutants and the risk of PTB in the Beijing–Tianjin–Hebei region. Results: The monthly PTB cases exhibited a fluctuating downward trend. For each 10 μg/m3 increase in concentration, the maximum lag-specific risk and cumulative relative risk (RR) were 1.011 (95% confidence interval (CI): 1.0091.012, lag: 3 months) and 1.042 (1.036–1.048, 5 months) for PM2.5, and 1.023 (1.015–1.031, 0 months) and 1.041 (1.026–1.055, 2 months) for NO2. The risk of PTB was negatively correlated with O3 exposure, and the minimum lag-specific risk and cumulative RR were 0.991 (95% CI: 0.987–0.994, lag: 0 months) and 0.974 (0.968–0.981, 4 months), respectively. No age-dependent effects were observed. Conclusions: Our results revealed potential associations between outdoor exposure to PM2.5, NO2, and O3 and the risk of PTB. Further research should explore the corresponding interactions and potential mechanisms

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System.

BackgroundDetailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.MethodsTime-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.ResultsMaximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.ConclusionsThe random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations

Mean decrease in accuracy for candidate variables.

<p>Mean decrease in accuracy for candidate variables.</p

Model validation of time-activity classification by leave-one-fold-out and leave-one-subject-out

<p>Model validation of time-activity classification by leave-one-fold-out and leave-one-subject-out</p

Effects of Tillage and N Applications on the Cassava Rhizosphere Fungal Communities

Cassava (Manihot esculenta Crantz) is mainly cultivated in marginal land in the south of China where seasonal drought stress occurs frequently and the soil becomes more compact year by year. The study aimed to explore the effect of Fenlong tillage (FLT) combined with nitrogen applications on cassava rhizosphere soil particle composition and fungal community diversity. Conventional tillage (CT) was set as the control. The results indicated that the contents of clay and silt of the cassava rhizosphere soil were influenced by the tillage method, nitrogen (N), and their interaction. There was no difference in the richness and diversity of rhizosphere soil fungal communities among all treatments in 2019, while the richness of FLT was lower than that of CT in 2020. FLT caused a stronger influence on the community structure of rhizosphere fungi than N applications in the first year. The differences in the community structure of all treatments were reduced by continuous cropping of cassava in the second year. The top 10 dominant rhizosphere fungi at the class level of cassava found in 2019 and 2020 were Sordariomycetes, Dothideomycetes, Eurotiomycetes, Agaricomycetes, Intramacronucleata, norank_p__Mucoromycota, unclassified_p__Ascomycota, unclassified_k__Fungi, Pezizomycetes, and Glomeromycetes, which had an important relationship with soil pH, activity of urease, available nitrogen, available phosphorus, organic matter, and clay. These results indicated that FLT created a better soil environment for cassava growth than CT, thus promoting the formation of more stable rhizosphere fungal community structures

Out-of-bag error variation with different variables (from left to right the variables on the X axis were sequentially entered into the random forests model).

<p>Out-of-bag error variation with different variables (from left to right the variables on the X axis were sequentially entered into the random forests model).</p