Predicting vitamin D deficiency using optimized random forest classifier.

BACKGROUND: Vitamin D can be acquired from various dietary sources, but exposure to sunlight's ultraviolet rays can convert a natural compound called ergosterol present in the skin into vitamin D. AIM: The current study aimed to investigate vital parameters and use an optimized random forest (OptRF) classifier to understand better and predict the effect of environmental and nutritional factors of Vitamin D deficiency. METHODS: A predictive, cross-sectional, and correlational design was utilized in a study involving 350 male and female Tabuk citizens in Saudi Arabia. The Weka machine-learning tool was employed for comprehensive data analysis, with the OptRF algorithm being tailored through advanced feature selection methods and meticulous hyperparameter tuning. RESULTS: In addition to the OptRF classifier, a number of traditional machine learning techniques have been tested and compared on the dataset of vitamin D to analyze and build the predictive model for classifying vitamin D deficiency. In general, the OptRF-based predictive model can statistically describe data for determining significant features related to Vitamin D deficiency. OptRF demonstrated its ability to classify vitamin D deficiency cases with high accuracy 91.42 %. CONCLUSION: This study showed that Tabuk citizens are at high risk of vitamin D deficiency especially among females (gender predictor) with little regard to age, income, smoking, and sun exposure. In addition, exercise, less Vitamin D intake, and less intake of Calcium are also predictors of Vitamin D deficiency. Due to the link between Vitamin D Deficiency and major chronic illnesses, it is important to emphasize the importance of identifying risk factors and screening for Vitamin D Deficiency. It may be appropriate for nutritionists, nurses, and physicians to promote community awareness about strategies to improve dietary Vitamin D intake or consider recommending supplements.

The impact of vitamin D changes during pregnancy on the development of maternal adverse events: a random forest analysis.

BACKGROUND: Maternal vitamin D deficiency during pregnancy has been associated with various maternal adverse events (MAE). However, the evidence regarding the effect of vitamin D supplementation on these outcomes is still inconclusive. METHODS: This secondary analysis utilized a case-control design. 403 samples with MAE and 403 samples without any outcomes were selected from the Khuzestan Vitamin D Deficiency Screening Program in Pregnancy study. Random forest (RF) analysis was used to evaluate the effect of maternal vitamin D changes during pregnancy on MAE. RESULTS: The results showed that women who remained deficient (35.2%) or who worsened from sufficient to deficient (30.0%) had more MAE than women who improved (16.4%) or stayed sufficient (11.8%). The RF model had an AUC of 0.74, sensitivity of 72.6%, and specificity of 69%, which indicate a moderate to high performance for predicting MAE. The ranked variables revealed that systolic blood pressure is the most important variable for MAE, followed by diastolic blood pressure and vitamin D changes during pregnancy. CONCLUSION: This study provides evidence that maternal vitamin D changes during pregnancy have a significant impact on MAE. Our findings suggest that monitoring and treatment of vitamin D deficiency during pregnancy may be a potential preventive strategy for reducing the risk of MAE. The presented RF model had a moderate to high performance for predicting MAE.

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS).

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

Identification of a Histone Deacetylase 8 Inhibitor through Drug Screenings Based on Machine Learning.

Histone deacetylase 8 (HDAC8) is a zinc-dependent HDAC that catalyzes the deacetylation of nonhistone proteins. It is involved in cancer development and HDAC8 inhibitors are promising candidates as anticancer agents. However, most reported HDAC8 inhibitors contain a hydroxamic acid moiety, which often causes mutagenicity. Therefore, we used machine learning for drug screening and attempted to identify non-hydroxamic acids as HDAC8 inhibitors. In this study, we established a prediction model based on the random forest (RF) algorithm for screening HDAC8 inhibitors because it exhibited the best predictive accuracy in the training dataset, including data generated by the synthetic minority over-sampling technique (SMOTE). Using the trained RF-SMOTE model, we screened the Osaka University library for compounds and selected 50 virtual hits. However, the 50 hits in the first screening did not show HDAC8-inhibitory activity. In the second screening, using the RF-SMOTE model, which was established by retraining the dataset including 50 inactive compounds, we identified non-hydroxamic acid 12 as an HDAC8 inhibitor with an IC50 of 842 nM. Interestingly, its IC50 values for HDAC1 and HDAC3-inhibitory activity were 38 and 12 µM, respectively, showing that compound 12 has high HDAC8 selectivity. Using machine learning, we expanded the chemical space for HDAC8 inhibitors and identified non-hydroxamic acid 12 as a novel HDAC8 selective inhibitor.

Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts.

Fluorescence Fingerprint (FF) is a powerful tool for rapid quality assessment of various foods and plant-derived products. However, the conventional utilization of FFs measured at a single dilution level (DL) to substitute chemical analyses is extremely challenging, especially for multicomponent materials like spice extracts because fluorescence intensity and concentration widely differ between components, with complex phenomena like inner filter effects. Here, we proposed a new strategy to use the meta-data comprised of FFs measured at multiple DLs with machine learning to estimate common chemical attributes including total polyphenol and flavonoid contents, and antioxidant abilities. This strategy achieved more consistently satisfactory performance in estimation of all chemical attributes of spice extracts compared to using a single DL. Hence, the workflow employed in this study is expected to serve as an alternative method to quickly evaluate the chemical quality of spice extracts, as well as other plant products and food materials.

A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public health. The current forecast models are generally useful, but they falter when long time series of data are managed. The emergence of new computational techniques such as the LSTM algorithms could constitute a significant improvement for the pollen risk assessment. In this study, several LSTM variants were applied to forecast monthly pollen integrals in Málaga (southern Spain) using meteorological variables as predictors. Olea and Urticaceae pollen types were modelled as proxies of different annual pollen curves, using data from the period 1992-2022. The aims of this study were to determine the LSTM variants with the highest accuracy when forecasting monthly pollen integrals as well as to compare their performance with the traditional pollen forecast methods. The results showed that the CNN-LSTM were the most accurate when forecasting the monthly pollen integrals for both pollen types. Moreover, the traditional forecast methods were outperformed by all the LSTM variants. These findings highlight the importance of implementing LSTM models in pollen forecasting for public health and research applications.

Study on remote sensing inversion and temporal-spatial variation of Hulun lake water quality based on machine learning.

Hulun Lake is facing significant water quality degradation, necessitating effective monitoring for safety. Traditional methods lack the necessary spatial and temporal coverage, underscoring the need for a remote sensing model. In this study, we utilized the Landsat 8 OLI dataset, incorporating cross-section monitoring and field sampling data comprehensively. Employing the random forest algorithm, we constructed a remote sensing inversion model for six water quality parameters in Hulun Lake: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO). The model was applied to the non-freezing period of Hulun Lake from 2016 to 2021, exhibiting commendable performance and generating high-resolution maps. Time series analysis revealed that during the study period, the pollution levels of TN, TP, and COD in Hulun Lake were extremely serious, exceeding the Class V water standard of China's surface water environmental quality standard. Regional analysis indicated lower pollutant concentrations in the central lake area compared to the lake inlet. The inflowing rivers with high pollution adversely impacted Hulun Lake's water quality. To ensure the continued health of Hulun Lake's water quality, it is imperative to monitor lake water quality attentively and implement necessary measures to prevent further deterioration. This study holds crucial importance for shaping and executing ecological protection and restoration strategies for Hulun Lake.

Mining association rules between the granulation feasibility and physicochemical properties of aqueous extracts from Chinese herbal medicine in fluidized bed granulation.

Fluidized bed granulation (FBG) is a widely used granulation technology in the pharmaceutical industry. However, defluidization caused by the formation of large aggregates poses a challenge to FBG, particularly in traditional Chinese medicine (TCM) due to its complex physicochemical properties of aqueous extracts. Therefore, this study aims to identify the complex relationships between physicochemical characteristics and defluidization using data mining methods. Initially, 50 types of TCM were decocted and assessed for their potential influence on defluidization using a set of 11 physical properties and 10 chemical components, utilizing the loss rate as an evaluation index. Subsequently, the random forest (RF) and Apriori algorithms were utilized to uncover intricate association rules among physicochemical characteristics and defluidization. The RF algorithm analysis revealed the top 8 critical factors associated with defluidization. These factors include physical properties like glass transition temperature (Tg) and dynamic surface tension (DST) of DST100ms, DST1000ms, DST10ms and conductivity, in addition to chemical components such as fructose, glucose and protein contents. The results from Apriori algorithm demonstrated that lower Tg and conductivity were associated with an increased risk of defluidization, resulting in a higher loss rate. Moreover, DST100ms, DST1000ms and DST10ms exhibited a contrasting trend in the physical properties Specifically, defluidization probability increases when Tg and conductivity dip below 29.04℃ and 6.21 ms/m respectively, coupled with DST10ms, DST100ms and DST1000ms values exceeding 70.40 mN/m, 66.66 mN/m and 61.58 mN/m, respectively. Moreover, an elevated content of low molecular weight saccharides was associated with a higher occurrence of defluidization, accompanied by an increased loss rate. In contrast, protein content displayed an opposite trend regarding chemical properties. Precisely, the defluidization likelihood amplifies when fructose and glucose contents surpass 20.35 mg/g and 34.05 mg/g respectively, and protein concentration is less than 1.63 mg/g. Finally, evaluation criteria for defluidization were proposed based on these results, which could be used to avoid this situation during the granulation process. This study demonstrated that the RF and Apriori algorithms are effective data mining methods capable of uncovering key factors affecting defluidization.

Analysis of the hospitalization costs of surgical patients with congenital heart disease in the plateau region of western China, 2010-2019.

Background: Congenital heart disease (CHD) is one of the most common birth defects and consumes a substantial amount of health care resources. CHD leads to heavy economic burdens for families. However, there are limited data regarding the utilization of healthcare resources for CHD. The objectives of this study were to evaluate the composition, changing trends, and factors affecting hospitalization costs for patients with CHD in the western highlands area of China over a 10-year period. Methods: We conducted a study using the International Quality Improvement Collaborative for Congenital Heart Surgery (IQIC) database and information management system of The First Hospital of Lanzhou University between January 2010 and December 2019. Results: Among 3,087 patients hospitalized for CHD surgery, annual CHD hospitalization costs saw an increasing trend over the 10-year period, with an average growth rate of 4.6% per year. The major contributors to the hospitalization costs were surgery, surgical material, and drug costs. Length of stay (ß=0.203; 0.379; 0.474, P<0.01), age at hospitalization (ß=0.293, P<0.01), proportion of surgery (ß=0.090; -0.102; -0.122; -0.110, P<0.01) and drug costs (ß=-0.114; -0.147; -0.069, P<0.01), and use of traditional Chinese medicine (ß=0.141, P<0.01) were independent factors affecting average hospitalization costs. Conclusions: The financial burden of patients with CHD in the Chinese western highland region is high. Independent of inflation, CHD hospitalization costs are increasing. Measures taken by medical institutions to control the increase in drug costs, and to shorten the length of stay may be expected to have positive effects on reducing the financial burden of individuals with CHD and their families.

Application of machine learning models in predicting insomnia severity: an integrative approach with constitution of traditional Chinese medicine.

Objective: This study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia. Methods: We analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors. Results: The RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions. Conclusion: The results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

Assessing the suitability and dynamics of three medicinal Sambucus species in China under current and future climate scenarios.

Climate change exerts profound influences on the ecological environments on a global scale, leading to habitat destruction and altering distribution patterns for numerous plant species. Traditional Chinese medicinal plants, such as those belonging to the Sambucus genus, have been extensively utilized for several centuries to treat fractures, rheumatism, and inflammation. However, our understanding of their geographic distribution and climatic adaptation within China still needs to be improved. In this study, we screened the optimal predictive model (random forest model) to predict the potential suitable distribution of three Sambucus species (Sambucus adnata, Sambucus javanica, and Sambucus williamsii) across China under both current and future climate scenarios. Moreover, we identified key climate factors that influence their potential distributions. Our findings revealed that S. adnata and S. javanica are predominantly shaped by temperature seasonality and mean diurnal range, respectively, whereas S. williamsii is significantly affected by the precipitation of the wettest month. Currently, S. williamsii is primarily distributed in north and central south China (covering 9.57 × 105 km2), S. javanica is prevalent in the south and east regions (covering 6.41×105 km2), and S. adnata predominantly thrives in the southwest China (covering 1.99×105 km2). Under future climate change scenarios, it is anticipated that S. adnata may migrate to higher latitudes while S. javanica may shift to lower latitudes. However, potentially suitable areas for S. williamsii may contract under certain scenarios for the years 2050 and 2090, with an expansion trend under the SSP585 scenario for the year 2090. Our study emphasizes the importance of climatic variables in influencing the potential geographic distribution of Sambucus species. These findings provide valuable theoretical insights for the preservation, cultivation, and utilization of Sambucus medicinal plant resources in the context of ongoing climate change.

Co-inoculation of phosphate-solubilizing bacteria and phosphate accumulating bacteria in phosphorus-enriched composting regulates phosphorus transformation by facilitating polyphosphate formation.

This study aimed to explore the impact of co-inoculating phosphate-solubilizing bacteria (PSB) and phosphate accumulating bacteria (PAB) on phosphorus forms transformation, microbial biomass phosphorus (MBP) and polyphosphate (Poly-P) accumulation, bacterial community composition in composting, using high throughput sequencing, PICRUSt 2, network analysis, structural equation model (SEM) and random forest (RF) analysis. The results demonstrated PSB-PAB co-inoculation (T1) reduced Olsen-P content (1.4 g) but had higher levels of MBP (74.2 mg/kg) and Poly-P (419 A.U.) compared to PSB-only (T0). The mantel test revealed a significantly positive correlation between bacterial diversity and both bioavailable P and MBP. Halocella was identified as a key genus related to Poly-P synthesis by network analysis. SEM and RF analysis showed that pH and bacterial community had the most influence on Poly-P synthesis, and PICRUSt 2 analysis revealed inoculation of PAB increased ppk gene abundance in T1. Thus, PSB-PAB co-inoculation provides a new idea for phosphorus management.

Assessment of Carbon Sequestration Capacity of E. ulmoides in Ruyang County and Its Ecological Suitability Zoning Based on Satellite Images of GF-6.

Eucommia ulmoides Oliver. (E. ulmoides) is a species of small tree native to China. It is a valuable medicinal herb that can be used to treat Alzheimer's disease, diabetes, hypertension, and other diseases. In addition, E. ulmoides is a source of rubber. It has both medicinal and ecological value. As ecological problems become increasingly prominent, accurate information on the cultivated area of E. ulmoides is important for understanding the carbon sequestration capacity and ecological suitability zoning of E. ulmoides. In previous tree mapping studies, no studies on the spectral characteristics of E. ulmoides and its remote sensing mapping have been seen. We use Ruyang County, Henan Province, China, as the study area. Firstly, using the 2021 Gao Fen-6 (GF-6) Wide Field of View (WFV) time series images covering the different growth stages of E. ulmoides based on the participation of red-edge bands, several band combination schemes were constructed. The optimal time window to identify E. ulmoides was selected by calculating the separability of E. ulmoides from other land cover types for different schemes. Secondly, a random forest algorithm based on several band combination schemes was investigated to map the E. ulmoides planting areas in Ruyang County. Thirdly, the annual NPP values of E. ulmoides were estimated using an improved Carnegie Ames Stanford Approach (CASA) to a light energy utilization model, which, in turn, was used to assess the carbon sequestration capacity. Finally, the ecologically suitable distribution zone of E. ulmoides under near current and future (2041-2060) climatic conditions was predicted using the MaxEnt model. The results showed that the participation of the red-edge band of the GF-6 data in the classification could effectively improve the recognition accuracy of E. ulmoides, making its overall accuracy reach 96.62%; the high NPP value of E. ulmoides was mainly concentrated in the south of Ruyang County, with a total annual carbon sequestration of 540.104835 t CM-2·a-1. The ecological suitability zone of E. ulmoides can be divided into four classes: unsuitable area, low suitable area, medium suitable area, and high suitable area. The method proposed in this paper applies to the real-time monitoring of E. ulmoides, highlighting its potential ecological value and providing theoretical reference and data support for the reasonable layout of E. ulmoides.

Simulation and prediction for the spatial heterogeneity of soil selenium bioavailability at different stratigraphic scales.

Stratigraphic lithology strongly influences the spatial heterogeneity of soil available selenium (ASe), however, it is often neglected in regional simulation. Therefore, taking the Jiangjin District, where the soil is richer in selenium (Se), as the research area, the changes of soil ASe at different spatial scales have been simulated by combining Geodetector and three popular models (Multiple linear regression (MLR), Random forest (RF) and BP neural network (BPN)). The results showed that modelling with 'Formation' as the spatial scale could reduce the influence of stratum lithology difference on the spatial heterogeneity of soil ASe and improve the model's prediction accuracy. Compared with the MLR (R2 = 0.52, root mean squares error (RMSE) = 13.217 µg kg-1) and BPN (R2 = 0.55, RMSE = 13.79 µg kg-1), the RF (R2 = 0.67, RMSE = 10.85 µg kg-1) exhibited higher R2 and smaller RMSE, and the simulation effect of soil ASe is the best in the Middle Jurassic Shaximiao Formation (J2s). The outcomes of variable importance analysis revealed that soil total selenium (TSe) and soil organic matter (SOM) were the imperative factors for predicting ASe. The scenario simulation prediction showed that in the next 40 years, due to the combined influence of SOM and pH, the content of ASe in soil developed in the J2s would decrease from 40.8 µg kg-1 to 37.8 µg kg-1, a 7.8 percent drop. The main areas of soil ASe loss were in the western farming areas. The ASe content in dry land and paddy fields decreased by 12.0% and 4.9%, respectively. Therefore, long-term agricultural production activities would lead to soil ASe loss. The present results could provide a new scheme for the simulation and prediction of regional soil ASe, which is helpful for scientific planning, utilization of selenium-rich soil resources, and development of regional agricultural economy.

Study on the planning and influential factors of the safe width of riparian buffer zones in the upper and middle reaches of the Ziwu River, China.

In this study, we employed the random forest model to identify the riparian buffer zone in the upper and middle reaches of the Ziwu River, used the Soil and Water Assessment Tool (SWAT) to simulate and calculate the nonpoint source pollution load in the riparian buffer zone, and used empirical formulas to estimate the pollutant concentration when surface runoff passes the edge of the riparian buffer zone. Moreover, through correlation analysis, we identified the main factors that affect the safe width of the riparian buffer zone. By combining these factors with the characteristic parameters of the riparian buffer zone and the water quality demand, we analyzed and calculated the safe width of the riparian buffer zone. Our findings are as follows: ① the simulated values of the SWAT model were highly consistent with the measured values. Specifically, the calibration and verification results of the hydrological station achieved Ens ≥ 0.65, RE < ± 15%, and R2 ≥ 0.85, while the overall total nitrogen and total phosphorus loads achieved Ens ≥ 0.65, RE < ± 15%, and R2 > 0.65. ② We found that the total nitrogen (TN) and total phosphorus (TP) loads in the riparian buffer zone gradually increased from upstream to downstream. Among these loads, the normal season had the largest TN and TP concentrations reaching the edge of the riparian buffer zone, while the dry season had the minimum concentrations. ③ The factors affecting the safe width of the riparian buffer zone included the connectivity, slope of the buffer zone, cultivated land area, and regional population density. For the effective protection of water quality, it is recommended that the upstream, midstream, and downstream buffer zones be at least 77.9 m, 33.37 m, and 60.25 m wide, respectively.

Fertilization as the most critical factor affecting yield response and agronomic efficiency of phosphorus in Chinese rice production: evidence from multi-location field trials.

BACKGROUND: Efficient utilization of phosphorus (P) has been a major challenge for sustainable agriculture. However, the responses of fertilizer rate, region, soil properties, cropping systems and genotypes to P have not been investigated comprehensively and systematically. RESULTS: A comprehensive analysis of 9863 fertilizer-P experiments on rice cultivation in China showed that rice yield  increased first and then fell down with the addition of P fertilizer, and the highest yield of 7963 kg ha-1 was observed under 100% P treatment. Under 100% P treatment, the yield response of applied P (YRP ) and agronomic efficiency of applied P (AEP ) were 12.8% and 30.1 kg ha-1 , respectively. Lower soil pH (< 5.5) and organic matter (< 30.0 g kg-1 ) were associated with lower YRP and AEP . By contrast, soil available P < 25.0 mg kg-1 resulted in decreased YRP (15.3 to 11.4%) and AEP (32.3 kg kg-1 to 26.2 kg kg-1 ), whereas soil available P > 25.0 mg kg-1 maintained the relatively stable YRP and AEP . Also, the YRP and AEP were significantly higher for single-cropping rice compared to other cropping systems. Moreover, the rice genotypes such as 'Longdun', 'Kendao' and 'Jigeng' had higher YRP and AEP than the average value. Overall, the fertilizer-P rate was the primary factor affecting YRP and AEP , and the recommended P fertilizer rate can be reduced by 9-21 kg P ha-1 compared to existing expert recommendations. CONCLUSION: The present study highlights the role of fertilizer-P rate in maximizing the YRP and AEP , thereby providing a strong basis for future fertilizer management in rice cultivation systems. © 2023 Society of Chemical Industry.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products.

BACKGROUND: The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions. PURPOSE: This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products. METHODS: Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products. RESULTS: The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products. CONCLUSION: This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation.

High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a certain extent, for predictions and interpretation. The objectives of this study were (1) to evaluate six different machine learning models for gap-filling in a high-frequency nitrate and total phosphorus concentration time series, (2) to showcase the potential added value (and limitations) of machine learning to interpret underlying processes, and (3) to study the limits of machine learning algorithms for predictions outside the training period. We used a 4-year high-frequency dataset from a ditch draining one intensive dairy farm in the east of The Netherlands. Continuous time series of precipitation, evapotranspiration, groundwater levels, discharge, turbidity, and nitrate or total phosphorus were used as predictors for total phosphorus and nitrate concentrations respectively. Our results showed that the random forest algorithm had the best performance to fill in data-gaps, with R2 higher than 0.92 and short computation times. The feature importance helped understanding the changes in transport processes linked to water conservation measures and rain variability. Applying the machine learning model outside the training period resulted in a low performance, largely due to system changes (manure surplus and water conservation) which were not included as predictors. This study offers a valuable and novel example of how to use and interpret machine learning models for post-processing high-frequency water quality data.

[Prediction of Cadmium Uptake Factor in Wheat Based on Machine Learning].

Applying machine learning methods to resolve the cadmium (Cd) uptake characteristics of regional soil-wheat systems can contribute to the accuracy and rationality of risk decisions. Based on a regional survey, we constructed a Freundlich-type transfer equation, random forest (RF) model, and neural network (BPNN) model to predict wheat Cd enrichment factor (BCF-Cd); verified the prediction accuracy; and assessed the uncertainty of different models. The results showed that both RF (R2=0.583) and BPNN (R2=0.490) were better than the Freundlich transfer equation (R2=0.410). The RF and BPNN were further trained repeatedly, and the results showed that the mean absolute error (MAE) and root mean square error (RMSE) of RF and BPNN were close to each other. Additionally, the accuracy and stability of RF (R2=0.527-0.601) was higher than that of BPNN (R2=0.432-0.661). Feature importance analysis showed that multiple factors led to the heterogeneity of wheat BCF-Cd, in which soil phosphorus (P) and zinc (Zn) were the key variables affecting the change in wheat BCF-Cd. Parameter optimization can further improve the accuracy, stability, and generalization ability of the model.

Historic and Contemporary Surface Disposal of Produced Water Likely Inputs Arsenic and Selenium to Surficial Aquifers.

Oil and gas development generates large amounts of wastewater (i.e., produced water), which in California has been partially disposed of in unlined percolation/evaporation ponds since the mid-20th century. Although produced water is known to contain multiple environmental contaminants (e.g., radium and trace metals), prior to 2015, detailed chemical characterizations of pondwaters were the exception rather than the norm. Using a state-run database, we synthesized samples (n = 1688) collected from produced water ponds within the southern San Joaquin Valley of California, one of the most productive agricultural regions in the world, to examine regional trends in pondwater arsenic and selenium concentrations. We filled crucial knowledge gaps resulting from historical pondwater monitoring by constructing random forest regression models using commonly measured analytes (boron, chloride, and total dissolved solids) and geospatial data (e.g., soil physiochemical data) to predict arsenic and selenium concentrations in historical samples. Our analysis suggests that both arsenic and selenium levels are elevated in pondwaters and thus this disposal practice may have contributed substantial amounts of arsenic and selenium to aquifers having beneficial uses. We further use our models to identify areas where additional monitoring infrastructure would better constrain the extent of legacy contamination and potential threats to groundwater quality.