Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Desertification status mapping in MuttumaWatershed by using Random Forest Model

Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.Random Forest Model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND Type 2 diabetes mellitus(T2DM)is associated with periodontitis.Currently,there are few studies proposing predictive models for periodontitis in patients with T2DM.AIM To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.METHODS In this a retrospective study,300 patients with T2DM who were hospitalized at the First People’s Hospital of Wenling from January 2022 to June 2022 were selected for inclusion,and their data were collected from hospital records.We used logistic regression to analyze factors associated with periodontitis in patients with T2DM,and random forest and logistic regression prediction models were established.The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve(AUC).RESULTS Of 300 patients with T2DM,224 had periodontitis,with an incidence of 74.67%.Logistic regression analysis showed that age[odds ratio(OR)=1.047,95%confidence interval(CI):1.017-1.078],teeth brushing frequency(OR=4.303,95%CI:2.154-8.599),education level(OR=0.528,95%CI:0.348-0.800),glycosylated hemoglobin(HbA1c)(OR=2.545,95%CI:1.770-3.661),total cholesterol(TC)(OR=2.872,95%CI:1.725-4.781),and triglyceride(TG)(OR=3.306,95%CI:1.019-10.723)influenced the occurrence of periodontitis(P<0.05).The random forest model showed that the most influential variable was HbA1c followed by age,TC,TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showedthat in the training dataset, the AUC of the random forest model was higher than that of the logistic regressionmodel (AUC = 1.000 vs AUC = 0.851;P < 0.05). In the validation dataset, there was no significant difference in AUCbetween the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915;P > 0.05).CONCLUSION Both random forest and logistic regression models have good predictive value and can accurately predict the riskof periodontitis in patients with T2DM.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.

Modelling the dead fuel moisture content in a grassland of Ergun City,China

The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.

Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Alterations in the human oral microbiome in cholangiocarcinoma

Dear Editor,Alterations in the human microbiome are closely related to various hepatobiliary diseases.Gut microbial dysbiosis has been found in patients with cholangiocarcinoma(CCA)[1].However,the characteristics of oral microbiome in patients with CCA have not been studied.

Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Surface albedo is a quantitative indicator for land surface processes and climate modeling,and plays an important role in surface radiation balance and climate change.In this study,by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS),we analyzed the spatiotemporal variation,persistence status,land cover type differences,and annual and seasonal differences of surface albedo,as well as the relationship between surface albedo and various influencing factors(including Normalized Difference Snow Index(NDSI),precipitation,Normalized Difference Vegetation Index(NDVI),land surface temperature,soil moisture,air temperature,and digital elevation model(DEM))in the north of Xinjiang Uygur Autonomous Region(northern Xinjiang)of Northwest China from 2010 to 2020 based on the unary linear regression,Hurst index,and Pearson's correlation coefficient analyses.Combined with the random forest(RF)model and geographical detector(Geodetector),the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated.The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer.The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south,showing a weak decreasing trend and a small and stable overall variation.Land cover types had a significant impact on the variation of surface albedo.The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation,and negatively correlated with NDVI,land surface temperature,soil moisture,and air temperature.In addition,the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons.To be specific,NDSI had the largest influence on surface albedo,followed by precipitation,land surface temperature,and soil moisture;whereas NDVI,air temperature,and DEM showed relatively weak influences.However,the interactions of any two influencing factors on surface albedo were enhanced,especially the interaction of air temperature and DEM.NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation,with an explanatory power greater than 92.00%.This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Climate change indirectly enhances sandstorm prevention services by altering ecosystem patterns on the Qinghai-Tibet Plateau

Climate change influences both ecosystems and ecosystem services.The impacts of climate change on ecosystems and ecosystem services have been separately documented.However,it is less well known how ecosystem changes driven by climate change will influence ecosystem services,especially in climate-sensitive regions.Here,we analyzed future climate trends between 2040 and 2100 under four Shared Socioeconomic Pathway(SSP) scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5) from the Coupled Model Intercomparison Project 6(CMIP6).We quantified their impacts on ecosystems patterns and on the ecosystem service of sandstorm prevention on the Qinghai-Tibet Plateau(QTP),one of the most climate-sensitive regions in the world,using Random Forest model(RF) and Revised Wind Erosion Equation(RWEQ).Strong warming(0.04℃/yr) and wetting(0.65 mm/yr) trends were projected from 2015 to 2100.Under these trends,there will be increased interspersion in the pattern of grassland and sparse vegetation with meadow and swamp vegetation,although their overall area will remain similar,while the areas of shrub and needleleaved forest classes will increase and move toward higher altitudes.Driven by the changes in ecosystem patterns caused by climate change indirectly,grassland will play an irreplaceable role in providing sandstorm prevention services,and sandstorm prevention services will increase gradually from 2040 to 2100(1.059-1.070 billion tons) on the QTP.However,some areas show a risk of deterioration in the future and these should be the focus of ecological rehabilitation.Our research helps to understand the cascading relationship among climate change,ecosystem patterns and ecosystem services,which provides important spatio-temporal information for future ecosystem service management.

Dew amount and its long-term variation in the Kunes River Valley,Northwest China

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

Quantification of Central and Eastern China's atmospheric CH_(4) enhancement changes and its contributions based on machine learning approach

Methane is the second largest anthropogenic greenhouse gas,and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks.Therefore,the monitoring of CH_(4) concentration changes and the assessment of underlying driving factors can provide scientific basis for the government’s policy making and evaluation.China is the world’s largest emitter of anthropogenic methane.However,due to the lack of ground-based observation sites,little work has been done on the spatial-temporal variations for the past decades and influencing factors in China,especially for areas with high anthropogenic emissions as Central and Eastern China.Here to quantify atmospheric CH_(4) enhancements trends and its driving factors in Central and Eastern China,we combined the most up-to-date TROPOMI satellite-based column CH_(4)(xCH_(4))concentration from 2018 to 2022,anthropogenic and natural emissions,and a random forest-based machine learning approach,to simulate atmospheric xCH_(4) enhancements from 2001 to 2018.The results showed that(1)the random forest model was able to accurately establish the relationship between emission sources and xCH_(4) enhancement with a correlation coefficient(R^(2))of 0.89 and a root mean-square error(RMSE)of 11.98 ppb;(2)The xCH_(4) enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018,with a relative change of 3.27%±0.13%;(3)The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH_(4) enhancement,contributing 68.00% and 31.21%,respectively,and the decrease of animal ruminants contributed-6.70% of its enhancement trend.

Green manure rotation and application increase rice yield and soil carbon in the Yangtze River valley of China

The addition of organic matter via green manure rotation with rice is considered a smart agricultural practice to maintain soil productivity and support environmental sustainability.However,few studies have quantitatively assessed the impact of green manure rotation and application on the interactions between agronomic management practice,soil fertility,and crop production.In this study,800 pairs of data from 108 studies conducted in the agricultural region of the Yangtze River,China were assessed,and random forest(RF)modeling was performed to evaluate the effect of green manure rotation and application on rice yield and soil properties.Compared to a winter fallow system,rotation and application of green manure significantly increased rice yield and soil organic carbon(SOC)by 8.1%and 8.4%,respectively.According to the RF models,rice type,green manure application rate and duration,mineral and organic nitrogen application rates,and initial SOC content and soil pH were identified as the main drivers for rice yield and SOC changes.Marginal benefit analysis revealed that green manure application rates for early rice in double cropping system and the rice in single cropping system were approximately 20 and 26 t ha-1(fresh weight),respectively.Further,the optimum green manure application rate was approximately 25 t ha-1(fresh weight)for carbon sequestration.However,it should be noted that green manure application to soils with high SOC level might result in the soils becoming a net carbon source.Our study contributed scientific and quantitative indicators for achieving the greatest benefits in rice yield and increasing SOC upon application of green manure.

Study of ground ozone and precursors along with particulate matter at residential sites in the vicinity of power plant

Emission source characterization and meteorological influence are the key aspects to gain insight into the ground ozone governing mechanisms.Receptor-based data analysis techniques help in comprehending local ozone fluctuations in the lack of accurate information on the emission characteristics.Through sophisticated data analysis,the current study offers insight into the key factors influencing the ozone changes in the vicinity of power plants.Ground ozone(O_(3))and its precursor variables carbon monoxide(CO),nitric oxide(NO),nitrogen dioxide(NO_(2)),Sulphur dioxide(SO_(2)),benzene,toluene,ethyl-benzene and xylene(BTEX)along with the particulate matter of size less than 10 and 2.5 micron(PM_(10) and PM_(2.5))and meteorological variables have been studied at a residential site near the coal-fired power plant in the two cities;Chandrapur and Nagpur during 2016–2019.O_(3) is observed to be not correlated significantly(r<0.16 and<0.1 in Nagpur and Chandrapur,respectively)with any of its precursor variables in two cities.On a finer time scale,however,an association of O_(3) with CO,NO,NO_(2) and BTEX suggested that the O_(3) formation mechanism is driven by volatile organic compounds(VOCs)(mainly BTEX),CO and NO_(x).On the coarser scale,however,seasonality and other factors have distorted the correlation.Random forest model with O_(3) concentration as the response variable and NO_(2),NO,SO_(2),CO,BTEX,PM_(10) and PM_(2.5) as independent variables suggested that PM_(10),NO,CO and solar radiation are highly important variables governing the O_(3) dynamics in Chandrapur.In Nagpur,wind direction,relative humidity,temperature,toluene and NO_(2) are more important.Qualitative analysis to assess the contribution of emission sources suggested the influence of traffic emissions in Nagpur and the dominance of non-traffic related emissions,mainly power plant and mining activities in Chandrapur.The hazard quotient is observed to be>1 in both cities suggesting a health hazard to the residents living in the area.

Inferring the Postmortem Interval of Rat Cadaver after Boiling Water Treatment Based on Microbial Community Succession

Background:In forensic investigations,accurate estimation of the postmortem interval(PMI)is an important task,but also an ongoing challenge.Especially in cases where the cadaver has been specially treated,for example,by boiling,the determination of PMI becomes extremely difficult.Previous studies have shown that the succession of the microbial community after decomposition of the cadaver can be used to infer PMI.However,the feasibility of determining the PMI of boiled cadavers has not yet been demonstrated.Aims and Objectives:The main objective of this study was to test whether we can infer PMI of boiled cadavers based on the succession of microbial communities.Materials and Methods:SD rats were killed by cervical dislocation.Subsequently,the rat cadavers were divided into the case(boiled cadavers)and control(unboiled cadavers)groups.Rectal samples were collected from the rats for 45 days and at nine time points.High-throughput sequencing of the 16S rRNA gene was performed to characterize the microbial community in the rectum.Results:The results showed that the composition and relative abundance of bacterial communities at the phylum level were significantly different between the case and control groups.The alpha diversity of the microbial community showed a decreasing trend with the decomposition process.Principal coordinate analysis showed that the case and control groups had obvious patterns along the succession of microbial communities.The rectal microbial communities showed a significant linear trend in the time course of decomposition.A random forest model was used to infer PMI.The goodness-of-fit(R2)of the model was 68.00%and 84.00%,and the mean absolute errors were 2.05 and 1.48 days within 45 days of decomposition for the case and control groups,respectively.Conclusions:Our results suggest that microbial community succession could be a potential method to infer PMI of boiled cadavers.

Comparison of sampling designs for calibrating digital soil maps at multiple depths

Digital soil mapping (DSM) aims to produce detailed maps of soil properties or soil classes to improve agricultural management and soil quality assessment. Optimized sampling design can reduce the substantial costs and efforts associated with sampling, profile description, and laboratory analysis. The purpose of this study was to compare common sampling designs for DSM, including grid sampling (GS), grid random sampling (GRS), stratified random sampling (StRS), and conditioned Latin hypercube sampling (cLHS). In an agricultural field (11 ha) in Quebec, Canada, a total of unique 118 locations were selected using each of the four sampling designs (45 locations each), and additional 30 sample locations were selected as an independent testing dataset (evaluation dataset). Soil visible near-infrared (Vis-NIR) spectra were collected in situ at the 148 locations (1 m depth), and soil cores were collected from a subset of 32 locations and subdivided at 10-cm depth intervals, totaling 251 samples. The Cubist model was used to elucidate the relationship between Vis-NIR spectra and soil properties (soil organic matter (SOM) and clay), which was then used to predict the soil properties at all 148 sample locations. Digital maps of soil properties at multiple depths for the entire field (148 sample locations) were prepared using a quantile random forest model to obtain complete model maps (CM-maps). Soil properties were also mapped using the samples from each of the 45 locations for each sampling design to obtain sampling design maps (SD-maps). The SD-maps were evaluated using the independent testing dataset (30 sample locations), and the spatial distribution and model uncertainty of each SD-map were compared with those of the corresponding CM-map. The spatial and feature space coverage were compared across the four sampling designs. The results showed that GS resulted in the most even spatial coverage, cLHS resulted in the best coverage of the feature space, and GS and cLHS resulted in similar prediction accuracies and spatial distributions of soil properties. The SOM content was underestimated using GRS, with large errors at 0–50 cm depth, due to some values not being captured by this sampling design, whereas larger errors for the deeper soil layers were produced using StRS. Predictions of SOM and clay contents had higher accuracy for topsoil (0–30 cm) than for deep subsoil (60–100 cm). It was concluded that the soil sampling designs with either good spatial coverage or feature space coverage can provide good accuracy in 3D DSM, but their performances may be different for different soil properties.

An Efficient WRF Framework for Discovering Risk Genes and Abnormal Brain Regions in Parkinson's Disease Based on Imaging Genetics Data

As an emerging research field of brain science,multimodal data fusion analysis has attracted broader attention in the study of complex brain diseases such as Parkinson's disease(PD).However,current studies primarily lie with detecting the association among different modal data and reducing data attributes.The data mining method after fusion and the overall analysis framework are neglected.In this study,we propose a weighted random forest(WRF)model as the feature screening classifier.The interactions between genes and brain regions are detected as input multimodal fusion features by the correlation analysis method.We implement sample classification and optimal feature selection based on WRF,and construct a multimodal analysis framework for exploring the pathogenic factors of PD.The experimental results in Parkinson's Progression Markers Initiative(PPMI)database show that WRF performs better compared with some advanced methods,and the brain regions and genes related to PD are detected.The fusion of multi-modal data can improve the classification of PD patients and detect the pathogenic factors more comprehensively,which provides a novel perspective for the diagnosis and research of PD.We also show the great potential of WRF to perform the multimodal data fusion analysis of other brain diseases.

Purkait’s triangle revisited:role in sex and ancestry estimation

Identification of unknown remains recovered from marine and terrestrial locations is a significant humanitarian problem.This investigation proposes a simple method applicable to fragmentary femora for a more refined level of ancestry and/or sex estimation.To that end,we re-examined Purkait’s triangle which involves three inter-landmark distances between the traction epiphyses and the articular rim of femoral head.A large sample(n=584)from geographically diverse(Egyptian,Indian and Greek)populations was compiled.Additionally,shape(n=3)and trigonometrically derived variables and ratios(n=9 variables)were employed to detect any geographically-clustered morphological differences between these populations.Random forest modelling(RFM)and linear discriminant function analysis(LDA)were employed to create classification models in instances where sex was known or unknown.The sample was apportioned into training and test sets with a ratio 70/30.The classification accuracies were evaluated by means of k fold cross-validation procedure.In sex estimation,RFM showed similar performance to LDA.However,RFM outperformed LDA in ancestry estimation.Ancestry estimation was satisfactory in the Indian and Egyptian samples albeit the Greek sample was problematic.The Greek samples presented greater morphological overlap with the Indian sample due to high within-group variation.Test samples were accurately assigned to their ancestral category when sex was known.Generally,higher classification accuracies in the validation sample were obtained in the sex-specific model of females than in males.Using RFM and the linear variables,the overall accuracy reached 83%which is distributed as 95%,71%and 86%for the Egyptian,Indian and Greek females,respectively;whereas in males,the overall accuracy is 72%and is distributed as 58%,87%and 50%for the Egyptian,Indian and Greek males,respectively.Classification accuracies were also calculated per group in the test data using the 12 derived variables.For the females,the accuracies using the medians model was comparable to the linear model whereas in males the angles model outperformed the linear model for each group but with similar overall accuracy.The classification rates of male specific ancestry were 82%,78%and 56%for the Egyptian,Indian and Greek males,respectively.In conclusion,Purkait’s triangle has potential utility in ancestry and sex estimation albeit it is not possible to separate all groups successfully with the same efficiency.Intrapopulation variation may impact the accuracy of assigned group membership in forensic contexts.

Regional variation of urban air quality in China and its dominant factors

It is of great theoretical and practical importance to carry out research on the spatio-temporal evolution of urban air pollution and its driving forces,which helps to facilitate a deeper understanding of the mutual feedback mechanisms between the urban environment and socio-economic systems.Comprehension of these mechanisms will contribute to the design and implementation of efficient environmental policies that ultimately will improve the quality of urbanization development.This paper illustrates the spatio-temporal evolutionary characteristics of six urban ambient air pollutant concentrations,namely,CO,NO_(2),O_(3),PM_(10),PM_(2.5),SO_(2),in 286 sample cities above the prefecture level in China from 2014 to 2019.The interactions between the pollutant concentrations are analyzed based on panel regression models.A random forest model is then employed to explore the correlations between the concentrations of these six pollutants and 13 natural and socio-economic impact factors to isolate the most crucial ones.The results reveal three aspects.First,within the research period,the average annual concentration of O_(3)increased while that of other pollutants decreased year by year.Second,there were significant interactions between concentrations of the six pollutants,leading to obvious compound air pollution in urban areas.Third,the impact of natural and socio-economic factors on urban air quality varied greatly among different air pollutants,with air temperature,vegetation coverage,urbanization level and traffic factors ranking high and the different response thresholds to the dominant influencing factors.In light of the limited ability of humans to control the natural environment and meteorological conditions,it is recommended that urban air quality be further improved by optimizing urban density,controlling anthropogenic emission sources,and implementing strict air pollution prevention and control measures.