Bioaerosols in the coastal region of Qingdao: Community diversity, impact factors and synergistic effect

Atmospheric bioaerosols are influenced by multiple factors, including physical, chemical, and biotic interactions, and pose a significant threat to the public health and the environment. The nonnegligible truth however is that the primary driver of the changes in bioaerosol community diversity remains unknown. In this study, putative biological association (PBA) was obtained by constructing an ecological network. The relationship between meteorological conditions, atmospheric pollutants, water-soluble inorganic ions, PBA and bioaerosol community diversity was analyzed using random forest regression (RFR)-An ensemble learning algorithm based on a decision tree that performs regression tasks by constructing multiple decision trees and integrating the predicted results, and the contribution of different rich species to PBA was predicted. The species richness, evenness and diversity varied significantly in different seasons, with the highest in summer, followed by autumn and spring, and was lowest in winter. The RFR suggested that the explanation rate of alpha diversity increased significantly from 73.74 % to 85.21 % after accounting for the response of the PBA to diversity. The PBA, temperature, air pollution, and marine source air masses were the most crucial factors driving community diversity. PBA, particularly putative positive association (PPA), had the highest significance in diversity. We found that under changing external conditions, abundant taxa tend to cooperate to resist external pressure, thereby promoting PPA. In contrast, rare taxa were more responsive to the putative negative association because of their sensitivity to environmental changes. The results of this research provided scientific advance in the understanding of the dynamic and temporal changes in bioaerosols, as well as support for the prevention and control of microbial contamination of the atmosphere.

Antiphospholipid antibodies are enriched post-acute COVID-19 but do not modulate the thrombotic risk

BACKGROUND AND OBJECTIVES

COVID-19-associated coagulopathy, shown to increase the risk for the occurrence of thromboses and microthromboses, displays phenotypic features of the antiphospholipid syndrome (APS), a prototype antibody-mediated autoimmune disease. Several groups have reported elevated levels of criteria and non-criteria antiphospholipid antibodies (aPL), assumed to cause APS, during acute or post-acute COVID-19. However, disease heterogeneity of COVID-19 is accompanied by heterogeneity in molecular signatures, including aberrant cytokine profiles and an increased occurrence of autoantibodies. Moreover, little is known about the association between autoantibodies and the clinical events. Here, we first aim to characterise the antiphospholipid antibody, anti-SARS-CoV-2 antibody, and the cytokine profiles in a diverse collective of COVID-19 patients (disease severity: asymptomatic to intensive care), using vaccinated individuals and influenza patients as comparisons. We then aim to assess whether the presence of aPL in COVID-19 is associated with an increased incidence of thrombotic events in COVID-19.

METHODS AND RESULTS

We conducted anti-SARS-CoV-2 IgG and IgA microELISA and IgG, IgA, and IgM antiphospholipid line immunoassay (LIA) against 10 criteria and non-criteria antigens in 155 plasma samples of 124 individuals, and we measured 16 cytokines and chemokines in 112 plasma samples. We additionally employed clinical and demographic parameters to conduct multivariable regression analyses within multiple paradigms. In line with recent results, we find that IgM autoantibodies against annexin V (AnV), β2-glycoprotein I (β2GPI), and prothrombin (PT) are enriched upon infection with SARS-CoV-2. There was no evidence for seroconversion from IgM to IgG or IgA. PT, β2GPI, and AnV IgM as well as cardiolipin (CL) IgG antiphospholipid levels were significantly elevated in the COVID-19 but not in the influenza or control groups. They were associated predominantly with the strength of the anti-SARS-CoV-2 antibody titres and the major correlate for thromboses was SARS-CoV-2 disease severity.

CONCLUSION

While we have recapitulated previous findings, we conclude that the presence of the aPL, most notably PT, β2GPI, AnV IgM, and CL IgG in COVID-19 are not associated with a higher incidence of thrombotic events.

Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain

Mountain soils have received significant attention due to their profound influence on ecological processes and environmental factors. However, mapping these soils in digital soil mapping technique encounters several challenges, including high local variability, non-linear relationships between environmental covariates and soil properties, limited accessibility in complex topographical settings, and the absence of universally applicable covariates for soil formation. To address these issues, this study integrates soil-forming factors of the scorpan model to map soil organic carbon (SOC) and soil texture in the mid-Himalayas. By considering over 100 environmental covariates, with a focus on terrain parameters relevant to mountainous environments, the study aims to enhance the accuracy of ML regression models through augmentation techniques that overcome data insufficiency. Using augmented soil observations and covariates, a non-parametric random forest regression model is trained and applied to predict soil variables across the study area, generating a continuous fine-resolution map. The model's performance, evaluated against an unknown dataset, was significant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively. Furthermore, a sensitivity analysis of the environmental covariates and their impact on the model revealed that all the soil-forming factors make a significant contribution to the model's effectiveness. The insights gained from this research contribute to a better understanding of mountain soils and facilitate the development of effective conservation and sustainable management strategies for mountainous regions.

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.

Detection of spatiotemporal changes in ecological quality in the Chinese mainland: Trends and attributes

Global climate change and revegetation programs have significantly changed the ecological quality (EQ) in the Chinese mainland after 1999. Monitoring and assessing the changes in the regional EQ and analyzing their drivers are crucial for ensuring ecological restoration and rehabilitation. However, it is challenging to carry out a long-term and large-scale quantitative assessment of the EQ of a region based on traditional field investigations and experiment methods alone; notably, in previous studies, the effects of carbon and water cycles and human activities on the variations in EQ have not been studied comprehensively. Therefore, in addition to remote sensing data and principal component analysis, we used the remote sensing-based ecological index (RSEI), to assess the EQ changes in the Chinese mainland during 2000-2021. Additionally, we also analyzed the impacts of carbon and water cycles and anthropological activities on the changes in the RSEI. The main conclusions of this study were: since the beginning of the 21st century, we observed a fluctuating upward trend in the EQ changes in the Chinese mainland and eight climatic regions. From 2000 to 2021, in terms of the EQ, North China (NN) portrayed the highest increase rate (2.02 × 10^-3 year^-1, P < 0.05). There was a breaking point in 2011, the EQ in the region experienced a change, from a downward trend to an upward one. Northwest China, Northeast China, and NN portrayed an overall significant increasing trend in the RSEI, whereas the southwest part of the Southwest Yungui Plateau (YG) and a part of the plain region of the Changjiang (Yangtze) River (CJ) river region portrayed a significant decreasing trend in the EQ. Overall, the carbon and water cycles and human activities played a pivotal role in determining the spatial patterns and trends of the EQ in the Chinese mainland. In particular, the self-calibrating Palmer Drought Severity Index, actual evapotranspiration (AET), gross primary productivity (GPP), and soil water content (Soil_w) were identified as the key drivers of the RSEI. In the central and western Qinghai-Tibetan Plateau (QZ) and the northwest region of NW, the changes in RSEI were dominated by AET; however, in central NN, southeastern QZ, northern YG, and central NE, the changes were driven by GPP, and in the southeast region of NW, south region of NE, northern region of NN, middle YG region, and a part of the middle CJ region, the changes were driven by Soil_w. The population-density-related change in the RSEI was positive in the northern regions (NN and NW) but negative in the southern regions (SE), whereas the RSEI change related to ecosystem services was positive in the NE, NW, QZ, and YG regions. These results are beneficial for the adaptive management and protection of the environment and the realization of green and sustainable developmental strategies in the Chinese mainland.

Integration of target, suspect, and nontarget screening with risk modeling for per- and polyfluoroalkyl substances prioritization in surface waters

Though thousands of per- and polyfluoroalkyl substances (PFAS) have been on the global market, most research focused on only a small fraction, potentially resulting in underestimated environmental risks. Here, we used complementary target, suspect, and nontarget screening for quantifying and identifying the target and nontarget PFAS, respectively, and developed a risk model considering their specific properties to prioritize the PFAS in surface waters. Thirty-three PFAS were identified in surface water in the Chaobai river, Beijing. The suspect and nontarget screening by Orbitrap displayed a sensitivity of > 77%, indicating its good performance in identifying the PFAS in samples. We used triple quadrupole (QqQ) under multiple-reaction monitoring for quantifying PFAS with authentic standards due to its potentially high sensitivity. To quantify the nontarget PFAS without authentic standards, we trained a random forest regression model which presented the differences up to only 2.7 times between measured and predicted response factors (RFs). The maximum/minimum RF in each PFAS class was as high as 1.2-10.0 in Orbitrap and 1.7-22.3 in QqQ. A risk-based prioritization approach was developed to rank the identified PFAS, and four PFAS (i.e., perfluorooctanoic acid, hydrogenated perfluorohexanoic acid, bistriflimide, 6:2 fluorotelomer carboxylic acid) were flagged with high priority (risk index > 0.1) for remediation and management. Our study highlighted the importance of a quantification strategy during environmental scrutiny of PFAS, especially for nontarget PFAS without standards.

Impact assessment of human activities on resources of juvenile horseshoe crabs in Hainan coastal areas, China

The booming coastal zone economy poses increasing anthropogenic threats to marine life and habitats. Using the endangered living fossil horseshoe crab (HSC) as an example, we quantified the intensity of various anthropogenic pressures along the coast of Hainan Island, China, and for the first time assessed their impact on the distribution of juvenile HSCs through a field survey, remote sensing, spatial geographic modeling, and machine learning methods. The results indicate that the Danzhou Bay needs to be protected as a priority based on species and anthropogenic pressure information. Aquaculture and port activities dramatically impact the density of HSCs and therefore be managed priority. Finally, a threshold effect between total, coastal residential, and beach pressure and the density of juvenile HSCs were detected, which indicates the need for a balance between development and conservation as well as the designation of suitable sites for the construction of marine protected areas.

Does managerial short-termism always matter in a firm's corporate social responsibility performance? Evidence from China

Using data on Chinese A-share listed firms from 2008 to 2017, we explore how corporate social responsibility (CSR) performance is affected by managerial short-termism and what factors influence the association between the two. First, by employing text analysis in conjunction with machine learning, we construct a new managerial short-termism indicator. Using panel fixed models, we find that managerial short-termism has an adverse impact on CSR performance, and the results are consistent in a series of robustness checks. The heterogeneous test results show that the negative effect is significant only for firms with lower internal corporate governance, for firms in less competitive industries, for firms with less analyst attention, and for state-owned enterprises (SOEs). Additionally, a better institutional environment weakens the negative impact of managerial short-termism on CSR performance. The findings shed light on policy implications for emerging countries.

Preconditioning of clinical data for intraocular lens formula constant optimisation using Random Forest Quantile Regression Trees

PURPOSE

To implement a fully data driven strategy for identifying outliers in clinical datasets used for formula constant optimisation, in order to achieve proper formula predicted refraction after cataract surgery, and to assess the capabilities of this outlier detection method.

METHODS

2 clinical datasets (DS1/DS2: N = 888/403) of eyes treated with a monofocal aspherical intraocular lens (Hoya XY1/Johnson&Johnson Vision Z9003) containing preoperative biometric data, power of the lens implant and postoperative spherical equivalent (SEQ) were transferred to us for formula constant optimisation. Original datasets were used to generate baseline formula constants. A random forest quantile regression algorithm was set up using bootstrap resampling with replacement. Quantile regression trees were grown and the 25% and 75% quantile, and the interquartile range were extracted from SEQ and formula predicted refraction REF for the SRKT, Haigis and Castrop formulae. Fences were defined from the quantiles and data points outside the fences were marked and removed as outliers before recalculating the formula constants.

RESULTS

NB = 1000 bootstrap samples were derived from both datasets, and random forest quantile regression trees were grown to model SEQ versus REF and to estimate the median and 25% and 75% quantiles. The fence boundaries were defined as being from 25% quantile - 1.5·IQR to 75% quantile + 1.5·IQR, with data points outside the fence being marked as outliers. In total, for DS1 and DS2, 25/27/32 and 4/5/4 data points were identified as outliers for the SRKT/Haigis/Castrop formulae respectively. The respective root mean squared formula prediction errors for the three formulae were slightly reduced from: 0.4370 dpt;0.4449 dpt/0.3625 dpt;0.4056 dpt/and 0.3376 dpt;0.3532 dpt to: 0.4271 dpt;0.4348 dpt/0.3528 dpt;0.3952 dpt/0.3277 dpt;0.3432 dpt for DS1;DS2.

CONCLUSION

We were able to prove that with random forest quantile regression trees a fully data driven outlier identification strategy acting in the response space is achievable. In a real life scenario this strategy has to be complemented by an outlier identification method acting in the parameter space for a proper qualification of datasets prior to formula constant optimisation.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R² = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R² = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches

With the gradual declining streamflow gauging stations in many world-rivers, emphasis is given nowadays to develop remote sensing (RS)-based approaches as the next-generation hydrometry for estimating riverine ecological flow regimes (EFR). For constructing EFR based on daily-streamflow data in scantily-gauged reaches, use of RS techniques in narrow flow-width tropical rain-fed rivers is constrained with the non-availability of finer spatial satellite data at daily scale. To address these limitations, this study proposes a novel framework that integrates the enhanced spatiotemporal adaptive reflectance fusion (FUS) of the 250 m × 1-day resolution Aqua-MODIS and 30 m × 1-day resolution Landsat satellite-based remote sensing images in the near-infrared region with the machine learning algorithms. These developed frameworks are named as Artificial Neural Network-based ANNFUS, Random Forest Regression-based RFRFUS, and Support Vector Regression-based SVRFUS models, which were tested for daily-scale streamflow estimation in a typical Brahmani River Basin, India. The results reveal that by addressing the linear and nonlinear dynamism between the streamflow and satellite signals, all the developed models could simulate the streamflow very well with the Nash-Sutcliffe efficiency>0.8, Kling-Gupta efficiency>0.8, relative root mean square error (rRMSE) of 0.051-0.12, and normalized RMSE of 0.23-0.36. However, for reproducing the high, median, and low streamflow regimes, the SVRFUS model was found to be the best with the NSE>0.85 and KGE>0.8. Conclusively, the proposed approach is found to have the potential to be replicated in other world-river basins to estimate ecological flow regimes at defunct gauging stations facilitating the basin-scale aquatic environmental management.

Comprehensive efficiency evaluation of wastewater treatment plants in northeast Qinghai-Tibet Plateau using slack-based data envelopment analysis

Comprehensive efficiency analysis of wastewater treatment plants (WWPTs) in the alpine region with harsh environment and poor techniques as well as managing experience could provide targeted and effective improvement evidences for local wastewater treatment industry and help to improve the water quality of downstream reaches. In this paper, slack-based data envelopment analysis (SBM-DEA) was adopted to assess the operating efficiencies of WWPTs in northeast Qinghai-Tibet Plateau (QTP). Results showed that the average efficiency score for all WWPTs was 0.608, and 32.5% of WWPTs were efficient. Some WWPTs had large improvement potentials in operating costs and pollutant removal rates. Lowering expenditures and promoting facility construction for WWPTs to overcome the climate difficulties and improve management level was necessary according to their improvement potentials. In addition, the relative importance of the quantitative influential factors to efficiencies scores calculated by random forest regression (RFR) indicated that design capacity and temperature were important quantitative factors affecting the performance of WWPTs. Furthermore, geographical location and design capacity also had significant influence on the comprehensive efficiency of WWPTs verified by Kruskal-Wallis test. Our results highlight the importance of facilities upgrading, scientific management for WWPTs. And the relative improvement suggestions on overcoming the high and cold environment should also be considered for the efficient operations of WWTPs as well as the protection the aquatic environment.

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO₂ hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO₂). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO₂ system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSE_T = 0.0954, MAE_T = 0.0811, R_T² = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSE_T = 0.1244, MAE_T = 0.1106, R_T² = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.

Drought and water-use efficiency are dominant environmental factors affecting greenness in the Yellow River Basin, China

Revegetation is accelerating globally because of its benefits in terms of ecosystem restoration, desertification prevention, and warming mitigation. The Yellow River Basin (YRB), as an ecological barrier in northern China, has implemented revegetation projects (such as the 'Grain for Green' program) for over two decades. However, a consensus on whether a significant change in greenness has been achieved and to what extent have environmental factors contributed to this change, as well as their importance ranking, is lacking. Leaf area index (LAI) is a critical indicator for estimating global greenness and projecting the dynamics of climate change. Herein, we apply four methods (Geodetector, random forest, multiple linear regression, and structural equation models) to explore the contribution of different environmental factors to greenness using the LAI in the YRB. We found that greenness has been increasing (greening over 67.22% (p < 0.05; 47.7%) of the YRB) with great spatial heterogeneity in the entire basin since 2000. Specifically, the greening process differed with elevation and slope. Temperature vegetation dryness index (TVDI) and water-use efficiency (WUE) dominated the greening; however, the three subregions evaluated revealed differing performance. In the upstream region, LAI increased by 0.031 y^-1. The primary positive factors of greening change were WUE and the annual highest value of daily minimum temperature; the negative factors were TVDI and the highest number of consecutive days when precipitation <1 mm. In the midstream region, LAI increased by 0.025 y^-1; greenness was mainly affected by the negative interaction of TVDI and the positive interaction of WUE. Annual maximum consecutive 5-day precipitation and annual count when daily minimum temperature < 0 °C had a great indirect impact on greenness, mainly through TVDI and WUE. In the downstream region, LAI increased by 0.045 y^-1, and the main driving factors were the annual lowest value of daily minimum temperature with a negative influence and the annual lowest value of daily maximum temperature with a positive influence. In addition, we found that the effect of the interaction of any two driving factors on greenness was greater than or equal to the single effect of a driving factor. This study concludes that drought and WUE are important predictors to evaluate the greenness in arid and semi-arid regions. We emphasise that the selection and assessment of greenness factors should follow a scientific and rigorous process rather than experience, and increased attention should be paid to the interaction of multiple factors. Furthermore, the perspective of system analysis will deepen our understanding of vegetation change in a vulnerable ecosystem.

Hyperspectral retrievals of suspended sediment using cluster-based machine learning regression in shallow waters

Remote sensing of suspended sediment in shallow waters is challenging because of the increased optical variability of the water, resulting from the influence of suspended matter in the water column and the heterogeneous bottom properties. To overcome this limitation, in this study, we developed a novel framework called cluster-based machine learning regression for optical variability (CMR-OV), using the Gaussian mixture model (GMM) clustering technique and a random forest regressor (RFR). We evaluated the model using an optically complex dataset from a field-scale experiment. This experiment was conducted with four sediment types injected into an experimental meandering channel divided into two reaches with submerged vegetation and a natural sand bottom. We obtained high-resolution hyperspectral images using unmanned aerial vehicles (UAVs) and measured the in situ suspended sediment concentration using laser diffraction sensors. Based on optical similarity, we used CMR-OV to divide the hyperspectral dataset into several clusters. Then, we built separate RFR models for each cluster using the corresponding spectral bands that were selected using recursive feature elimination (RFE). Thus, we found that the proposed CMR-OV yielded superior results compared to the conventional RFR model, decreasing the total error score by 10.81%. The optical spectral bands of each cluster were distinguished from each other, indicating that the datasets that were spectrally discriminated from clustering enhanced the performance of the estimator. By comparing the clustered spectral dataset and physical factors, we proved the bottom type was the most critical factor in separating the clusters, even though the variability in the sediment properties also induced substantial spectral changes. Our findings demonstrated that CMR-OV accurately reproduced the spatiotemporal distribution of suspended sediment under optically complex conditions by addressing the heterogeneity of bottom reflectance in shallow water.

Ecological management model for the improvement of soil fertility through the regulation of rare microbial taxa in tea (Camellia sinensis L.) plantation soils

Agricultural management is essential to enhance soil ecosystem service function through optimizing soil physical conditions and improving nutrient supply, which is predominantly regulated by soil microorganisms. Several studies have focused on soil biodiversity and function in tea plantation systems. However, the effects of different agriculture managements on soil fertility and microbes remain poorly characterized, especially for what concerns perennial agroecosystems. In this study, 40 soil samples were collected from 10 tea plantation sites in China to explore the effects of ecological and conventional managements on soil fertility, as well as on microbial diversity, community composition, and co-occurrence network. Compared with conventional management, ecological management was found to significantly enhance soil fertility, microbial diversity, and microbial network complexity. Additionally, a significant difference in community composition was clearly observed under the two agriculture managements, especially for rare microbial taxa, whose relative abundance significantly increased under ecological management. Random forest modeling revealed that rare taxa (e.g., Rokubacteria and Mortierellomycota), rather than dominant microbial taxa (e.g., Proteobacteria and Ascomycota), were key variables for predicting soil fertility. This indicates that rare taxa might play a fundamental role in biological processes. Overall, our results suggest that ecological management is more efficient than conventional management in regulating rare microbial taxa and maintaining a good soil fertility in tea plantation systems.

An exploratory analysis of forme fruste keratoconus sensitivity diagnostic parameters

PURPOSE

To secondary statistical analysis of the Pentacam or Corvis ST parameters from literatures, and to obtain more sensitive diagnostic parameters for clinical keratoconus (CKC) and forme fruste keratoconus (FFKC), respectively.

METHODS

The parameters and the corresponding area of ROC curve (AUC) in previous studies were extracted and screened to obtain the database of CKC (Data-CKC) and FFKC (Data-FFKC), respectively. Two different importance evaluation methods (%IncMSE and IncNodePurity) of random forest were used to preliminary select the important parameters. Then, based on the partial dependency analysis, the sensitive diagnostic parameters that had promotion to the diagnostic performance were obtained. Data-FFKC was analyzed in the same way. Finally, a diagnostic test meta-analysis on the sensitive parameter of interest was conducted to verify the reliability of the above analysis methods.

RESULTS

There were 88 parameters with 766 records in Data-CKC, 57 parameters with 346 records in Data-FFKC. Based on two importance evaluation methods, 60 important parameters were obtained, of which 20 were further screened as sensitive parameters of keratoconus, and most of these parameters were related to the thinnest point of cornea. The stiffness parameter at first applanation (SPA1) was the only Corvis ST output parameter sensitive to FFKC except the Tomographic and Biomechanical Index and the Corvis Biomechanical Parameter (CBI). A total of 4 records were included in the meta-analysis of diagnostic tests on SPA1. The results showed that there was threshold effect, but no significant heterogeneity (I² = 33%), and the area under the SROC curve was 0.87 (95% CI, 0.84-0.90).

CONCLUSIONS

For the diagnosis of FFKC, the sensitivity of SPA1 is not inferior to the well-known CBI, and may be the earliest Corvis ST output parameter to reflect the changes of corneal biomechanics during keratoconus progression. The elevation parameters based on the typical position of the thinnest point of corneal thickness are of great significance for the diagnosis of keratoconus.

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation

A systematic understanding of the spatial distribution of water quality is critical for successful watershed management; however, the limited number of physical monitoring stations has restricted the evaluation of spatial water quality distribution and the identification of features impacting the water quality. To fill this gap, we developed a modeling process that employed the random forest regression (RFR) to model the water quality distribution for the Taihu Lake basin in Zhejiang Province, China, and adopted the Shapley Additive exPlanations (SHAP) method to interpret the underlying driving forces. We first used RFR to model three water quality parameters: permanganate index (COD_Mn), total phosphorus (TP), and total nitrogen (TN), based on 16 watershed features. We then applied the built models to generate water quality distribution maps for the basin, with the COD_Mn ranging from 1.39 to 6.40 mg/L, TP from 0.02 to 0.23 mg/L, and TN from 1.43 to 4.27 mg/L. These maps showed generally consistent patterns among the COD_Mn, TN, and TP with minor differences in the spatial distribution. The SHAP analysis showed that the TN was mainly affected by agricultural non-point sources, while the COD_Mn and TP were affected by agricultural and domestic sources. Due to differences in sewage collection and treatment between urban and rural areas, the water quality in highly populated urban areas was better than that in rural areas, which led to an unexpected positive relationship between water quality and population density. Overall, with the RFR models and SHAP interpretation, we obtained a continuous distribution pattern of the water quality and identified its driving forces in the basin. These findings provided important information to assist water quality restoration projects.

Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

BACKGROUND

Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador.

RESULTS

Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R² of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R² of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature.

CONCLUSIONS

Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

Prediction of water quality parameters using machine learning models: a case study of the Karun River, Iran

Accurate water quality predicting has an essential role in improving water management and pollution control. The machine learning models have been successfully implemented for modelling total dissolved solids (TDS), sodium absorption ratio (SAR) and total hardness (TH) content in aquatic ecosystems with insufficient data. However, due to multiple pollution sources and complex behaviours of pollutants, these models' effect in predicting TDS, SAR, and TH levels in the Karun River system is still unclear. Given this problem, multiple linear regression (MLR), M5P model tree, support vector regression (SVR) and random forest regression (RFR) models were used to predict TDS, SAR and TH variables in the four stations in the Karun River for 1999-2019 period. Initially, to reduce the number of input variables, the principal component analysis (PCA) technique was used. The developed models are valued in terms of the coefficient of determination (R²) and the root mean square error (RMSE). Base on the PCA, it was found that sodium (Na), chloride (Cl) and TH and Na and Cl are the most influential inputs on TDS and SAR, respectively, while calcium (Ca) and magnesium (Mg) are the most effective on TH. The results indicated that RFR, SVR and MLR models had the lowest error in predicting TDS, SAR and TH, respectively, in all stations. RFR model had the highest performance for predicting TDS (R²= 0.98, RMSE= 70.50 mg l^-1), SVR model for predicting SAR (R²= 0.99, RMSE= 0.04) and MLR model for predicting TH (R²= 0.99, RMSE= 1.54 mg l^-1) in Darkhovin station. The comparison of the results indicated that the machine learning models could satisfactorily estimate the TDS, SAR and TH for all stations.

Relationship between land surface temperature and urbanization in Vilnius district

The present study aims to evaluate the effect of vegetation on land surface temperature (LST) in different land uses and covers in Vilnius district in 1999 and 2019. To that end, in addition to mono-window and split-window algorithms that help estimate the LST, the variables digital elevation model (DEM), slope, heat load index (HLI), distances from the road and the water, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) affecting the surface temperature were used. Furthermore, the random forest regression (RFR) method was applied to evaluate the effect of the mentioned variables on the LST. The performance model was also assessed by using the mean absolute (MAE), mean squared (MSE), and root mean square error (RMSE). Based on the results, NDVI and NDWI indexes had the greatest impact on the temperature of Vilnius city, respectively. The study area images were categorized as built-up area, cropland, semi-forest land, dense forest land, water bodies, pastures, and green urban areas. It was found that the pastures in 1999 and the built-up class in 2019 received the highest temperature from the land surface and that the classes characterized by natural land cover such as forest land and agricultural and water bodies had a relatively low surface temperature. NDVI response curves in both 1999 and 2019 indicated that the higher the density of vegetation on the land surface, the lower the surface temperature. A lower rate of urbanization, a higher density of vegetation and consequently, a lower the temperature of the land surface were recorded for 1999 in comparison with 2019. Therefore, urbanization was demonstrated to play a significant role in changes in LULC and the increase in LST.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi

Modelling photochemical pollutants, such as ground level ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O₃, NO and NO₂ levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O₃ prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO₂ with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO₂ (r = 0.89) and O₃ (r = 0.85). In both regression techniques, increased uncertainty in modelling O₃ is attributed to it being a secondary pollutant, non-linear dependency on NO_x, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

Random forest regression analysis on combined role of meteorological indicators in disease dissemination in an Indian city: A case study of New Delhi

Meteorological parameters show a strong influence on disease transmission in urban localities. The combined influence of factors such as daily mean temperature, absolute humidity and average wind speed on the attack rate and mortality rate of COVID-19 rise in Delhi, India has been investigated in this case study. A Random forest regression algorithm has been utilized to compare the epidemiological and meteorological parameters. The performance of the model has been evaluated using statistical performance metrics. The random forest model shows a strong positive correlation between the predictor parameters on the attack rate (96.09%) and mortality rate (93.85%). On both the response variables, absolute humidity has been noted to be the variable of highest influence. In addition, both temperature and wind speed have shown moderate positive influence on the transmission and survival of coronavirus during the study period. The synergistic effect of absolute humidity with temperature and wind speed contributing towards the increase in the attack and mortality rate has been addressed. The inhibition to respiratory droplet evaporation, increment in droplet size due to hygroscopic effect and the enhanced duration of survival of coronavirus borne in respiratory droplets are attributed to the increase in coronavirus infection under the observed weather conditions.

Modeling estrogenic activity in streams throughout the Potomac and Chesapeake Bay watersheds

Endocrine-disrupting compounds (EDCs), specifically estrogenic endocrine-disrupting compounds, vary in concentration and composition in surface waters under the influence of different landscape sources and landcover gradients. Estrogenic activity in surface waters may lead to adverse effects in aquatic species at both individual and population levels, often observed through the presence of intersex and vitellogenin induction in male fish. In the Chesapeake Bay Watershed, located on the mid-Atlantic coast of the USA, intersex has been observed in several sub-watersheds where previous studies have identified specific landscape sources of EDCs in tandem with observed fish health effects. Previous work in the Potomac River Watershed (PRW), the largest basin within the Chesapeake Bay Watershed, was leveraged to build random forest regression models to predict estrogenic activity at unsampled reaches in both the Potomac River and larger Chesapeake Bay Watersheds (CBW). Model outputs including important variables, partial dependence plots, and predicted values of estrogenic activity at unsampled reaches provide insight into drivers of estrogenic activity at different seasons and scales. Using the US Environmental Protection Agency effects-based threshold of 1.0 ng/L 17 β-estradiol equivalents, catchments predicted to exceed this value were categorized as at risk for adverse effects from exposure to estrogenic compounds and evaluated relative to healthy watersheds and recreation access locations throughout the PRW. Results show immediate catchment scale models are more reliable than upstream models, and the best predictive variables differ by season and scale. A small percentage of healthy watersheds (< 13%) and public access sites were classified as at risk using the "Total" (annual) model in the CBW. This study is the first Potomac River Watershed assessment of estrogenic activity, providing a new foundation for future risk assessment and management design efforts, with additional context provided for the entire Chesapeake Bay Watershed.

Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement

BACKGROUND

Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.

METHODS

We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.

RESULTS

We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows' neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.

CONCLUSIONS

Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

SUPPLEMENTARY INFORMATION

Supplementary information accompanies this paper at 10.1186/s40462-020-00228-4.

A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

OBJECTIVE

Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process.

METHOD

As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured.

RESULTS

As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model.

CONCLUSION

Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.

The association and discordance between glycated hemoglobin A1c and glycated albumin, assessed using a blend of multiple linear regression and random forest regression

BACKGROUND

Due to a high prevalence of thalassemia in southwest China, the diagnostic value of glycated hemoglobin A1c (HbA1c) is limited in the local population. Glycated albumin (GA) must also be measured for glucose monitoring. We sought to explore the relationships between HbA1c and GA.

METHODS

We analyzed 3,414 participants and allocated to four groups: GA > 14% and HbA1c > 5.7% (group 1), GA > 14% and HbA1c ≤ 5.7% (group 2), GA ≤ 14% and HbA1c > 5.7% (group 3), and GA ≤ 14% and HbA1c ≤ 5.7% (group 4). We used stepwise multivariable logistic regression analysis to study the inconsistency of HbA1c and GA. Furthermore, we explored their association using multiple linear regression (MLR), random forest regression (RFR), and 3 blended models. Finally, we performed sensitivity analyses by changing the thresholds of HbA1c (6.5%) and GA (12% or 16%).

RESULTS

There were 934 participants in group 1, 86 in group 2, 964 in group 3, and 1,430 in group 4. Age, high-density lipoprotein-cholesterol concentration, and red blood cell count were associated with the discordance in HbA1c and GA values. We constructed an RFR model that included MLR predictions as independent variables and could explain 97.80% of the variance in HbA1c in the training set, and 91.65% in the cross-validation set. Our results remained robust in 3 sensitivity analyses.

CONCLUSIONS

HbA1c and GA values are inconsistent in the population we studied. A model that blends MLR and RFR can be used to correct HbA1c values when conflicting HbA1c and GA values are encountered in patients.

Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras

BACKGROUND

Unmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management.

METHOD

In this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial-temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated. Random forest (RF) model for AGB estimation as well as the PB was then developed. Furthermore, variable importance and sensitivity analysis of UAV variables were performed to study the potential of improving model robustness and prediction accuracies.

RESULTS

It was found that the canopy height extracted from the CSM (Hcsm) exhibited a high correlation with the ground-measured canopy height, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. We also observed that several VIs were highly correlated with AGB, and the modified normalized difference spectral index extracted from the multispectral image achieved the highest correlation. RF model with fusing RGB and multispectral image data substantially improved the prediction results of AGB and PB with the prediction of root mean square error (RMSEP) reduced by 8.33-16.00%. The best prediction results for AGB and PB were achieved with the coefficient of determination (r2), the RMSEP and relative RMSE (RRMSE) of 0.90, 0.21 kg/m2 and 14.05%, and 0.68, 0.10 kg/m2 and 12.11%, respectively. In addition, the result confirmed that the sensitivity analysis could simplify the prediction model without reducing the prediction accuracy.

CONCLUSION

These findings demonstrate the feasibility of applying lightweight UAV with dual image-frame snapshot cameras for rice biomass estimation, and its potential for high throughput analysis of plant growth-related traits in precision agriculture as well as the advanced breeding program.

Localization of Ventricular Activation Origin from the 12-Lead ECG: A Comparison of Linear Regression with Non-Linear Methods of Machine Learning

We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present study sought to investigate whether machine learning-namely, random-forest regression (RFR) and support-vector regression (SVR)-can improve the localization accuracy compared to MLR. For 38 patients the 12-lead ECG was acquired during LV endocardial pacing at 1012 sites with known coordinates exported from an electroanatomic mapping system; each pacing site was then registered to a generic LV endocardial surface subdivided into 16 segments tessellated into 238 triangles. ECGs were reduced to one variable per lead, consisting of 120-ms time integral of the QRS. To compare three regression models, the entire dataset ([Formula: see text]) was partitioned at random into a design set with 80% and a test set with the remaining 20% of the entire set, and the localization error-measured as geodesic distance on the generic LV surface-was assessed. Bootstrap method with replacement, using 1000 resampling trials, estimated each model's error distribution for the left-out sample ([Formula: see text]). In the design set ([Formula: see text]), the mean accuracy was 8.8, 12.1, and 12.9 mm, respectively for SVR, RVR and MLR. In the test set ([Formula: see text]), the mean value of the localization error in the SVR model was consistently lower than the other two models, both in comparison with the MLR (11.4 vs. 12.5 mm), and with the RFR (11.4 vs. 12.0 mm); the RFR model was also better than the MLR model for estimating localization accuracy (12.0 vs. 12.5 mm). The bootstrap method with 1,000 trials confirmed that the SVR and RFR models had significantly higher predictive accurate than the MLR in the bootstrap assessment with the left-out sample (SVR vs. MLR ([Formula: see text]), RFR vs. MLR ([Formula: see text])). The performance comparison of regression models showed that a modest improvement in localization accuracy can be achieved by SVR and RFR models, in comparison with MLR. The "population" coefficients generated by the optimized SVR model from our dataset are superior to the previously-derived "population" coefficients generated by the MLR model and can supersede them to improve the localization of ventricular activation on the generic LV endocardial surface.

Potamodromous brown trout movements in the North of the Iberian Peninsula: Modelling past, present and future based on continuous fishway monitoring

Brown trout uses river flow and thermal regimens as main stimuli for initiating and maintaining behavioral reactions such as migration and spawning. Therefore, anthropogenic alterations on these factors may have strong impacts on its populations. The aim of this work is to understand these consequences by assessing potamodromous brown trout movements in the past and present, and to model future responses. For this, brown trout movements in a fishway in the Marin River (Bidasoa basin, Northern Iberian Peninsula) have been monitored from 2008 to 2017. Random forest regression has been used to assess the influence of environmental variables on brown trout movements and to model the response under hypothetical climatic and hydrological scenarios. Results show that brown trout uses the fishway during the whole year, with more upstream movements during the spawning season. The model is able to predict accurately the timing and number of migrants. Its use under hypothetical climate change and flow regulation scenarios shows a delay in the migration time. Therefore, modelling using large time series can be a powerful tool to define management and conservation strategies and prepare compensation measures for future scenarios.

Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning

INTRODUCTION

The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric approach.

METHODS

Cross sectional data collection included completion of a survey, cognitive testing, and non-fasting blood from 66 participants. Data were analyzed using random forest regression to identify the most significant predictors for each of the cognitive test scores.

RESULTS

A different cytokine profile predicted each cognitive test. Adjusted R² for each model ranged from 0.71-0.77 (p's < 9.50^-10). The relationships between all the cytokine predictors and cognitive test scores were non-linear.

CONCLUSIONS

Our findings are unique to the field of CRCI and suggest non-linear cytokine specificity to neural networks underlying cognitive functions assessed in this study.

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C₁₀ and C₃₅. Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R²) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg^-1, and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R²=0.63, RMSEP=107.54mgkg^-1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils.

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images

PURPOSE

Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images.

METHODS

In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage.

RESULTS

Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found.

CONCLUSIONS

In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach.

Robust anatomical landmark detection with application to MR brain image registration

Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy.