Anthropogenic influences significantly modify the hydrochemical properties and material flow in riverine ecosystems across Asia, potentially accounting for 40-50% of global emissions. Despite the pervasive impact on Asian rivers, there is a paucity of studies investigating their correlation with carbon dioxide (CO2) emissions. In this study, we computed the partial pressure of CO2 (pCO2) using the carbonate equilibria-based model (pCO2SYS) and examined its correlation with hydrochemical parameters from historical records at 91 stations spanning 2013-2021 in the Ganga River. The investigation unveiled substantial spatial heterogeneity in the pCO2 across the Ganga River. The pCO2 concentration varied from 1321.76 μatm, 1130.98 μatm, and 1174.33 μatm in the upper, middle, and lower stretch, respectively, with a mean of 1185.29 μatm. Interestingly, the upper stretch exhibited elevated mean pCO2 and FCO2 levels (fugacity of CO2: 3.63 gm2d-1) compared to the middle and lower stretch, underscoring the intricate interplay between hydrochemistry and CO2 dynamics. In the context of pCO2 fluctuations, nitrate concentrations in the upper segment and levels of biological oxygen demand (BOD) and dissolved oxygen (DO) in the middle and lower segments are emerging as crucial explanatory factors. Furthermore, regression tree (RT) and importance analyses pinpointed biochemical oxygen demand (BOD) as the paramount factor influencing pCO2 variations across the Ganga River (n = 91). A robust negative correlation between BOD and FCO2 was also observed. The distinct longitudinal patterns of both parameters may induce a negative correlation between BOD and pCO2. Therefore, comprehensive studies are necessitated to decipher the underlying mechanisms governing this relationship. The present insights are instrumental in comprehending the potential of CO2 emissions in the Ganga River and facilitating riverine restoration and management. Our findings underscore the significance of incorporating South Asian rivers in the evaluation of the global carbon budget.

COVID-19 has swept the world, and the unprecedented decline in transit ridership has been noticed. However, little attention has been paid to the resilience of the transportation system, particularly in medium-sized cities. Drawing upon a light rail ridership dataset in Salt Lake County from 2017 to 2021, we develop a novel method to measure the vulnerability and resilience of transit ridership using a Bayesian structure time series model. The results show that government policies have a more significant impact than the number of COVID-19 cases on transit ridership. Regarding the built environment, a highly compact urban design might reduce the building coverage ratio and makes transit stations more vulnerable and less resilient. Furthermore, the high rate of minorities is the primary reason for the drops in transit ridership. The findings are valuable for understanding the vulnerability and resilience of transit ridership to pandemics for better coping strategies in the future.

Accurate subtyping of NSCLC into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is the cornerstone of NSCLC diagnosis. Cytology samples reveal higher rates of classification failures, that is, subtyping as non-small cell carcinoma-not otherwise specified (NSCC-NOS), as compared with histology specimens. This study aims to identify specific algorithms on the basis of known cytomorphologic features that aid accurate and successful subtyping of NSCLC on cytology.
A total of 13 expert cytopathologists participated anonymously in an online survey to subtype 119 NSCLC cytology cases (gold standard diagnoses being LUAD in 80 and LUSC in 39) enriched for nonkeratinizing LUSC. They selected from 23 predefined cytomorphologic features that they used in subtyping. Data were analyzed using machine learning algorithms on the basis of random forest method and regression trees.
From 1474 responses recorded, concordant cytology typing was achieved in 53.7% (792 of 1474) responses. NSCC-NOS rates on cytology were similar among gold standard LUAD (36%) and LUSC (38%) cases. Misclassification rates were higher in gold standard LUSC (17.6%) than gold standard LUAD (5.5%; p < 0.0001). Keratinization, when present, recognized LUSC with high accuracy. In its absence, the machine learning algorithms developed on the basis of experts\' choices were unable to reduce cytology NSCC-NOS rates without increasing misclassification rates.
Suboptimal recognition of LUSC in the absence of keratinization remains the major hurdle in improving cytology subtyping accuracy with such cases either failing classification (NSCC-NOS) or misclassifying as LUAD. NSCC-NOS seems to be an inevitable morphologic diagnosis emphasizing that ancillary immunochemistry is necessary to achieve accurate subtyping on cytology.

To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race.
Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes.
This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation.
Client data were collected by states during personal interviews at admission and discharge.
Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities.
The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment.

Acid phosphatase produced by plants and microbes plays a fundamental role in the recycling of soil phosphorus (P). A quantification of the spatial variation in potential acid phosphatase activity (AP) on large spatial scales and its drivers can help to reduce the uncertainty in our understanding of bio-availability of soil P. We applied two machine-learning methods (Random forests and back-propagation artificial networks) to simulate the spatial patterns of AP across Europe by scaling up 126 site observations of potential AP activity from field samples measured in the laboratory, using 12 environmental drivers as predictors. The back-propagation artificial network (BPN) method explained 58% of AP variability, more than the regression tree model (49%). In addition, BPN was able to identify the gradients in AP along three transects in Europe. Partial correlation analysis revealed that soil nutrients (total nitrogen, total P, and labile organic P) and climatic controls (annual precipitation, mean annual temperature, and temperature amplitude) were the dominant factors influencing AP variations in space. Higher AP occurred in regions with higher mean annual temperature, precipitation and higher soil total nitrogen. Soil TP and Po were non-monotonically correlated with modeled AP for Europe, indicating diffident strategies of P utilization by biomes in arid and humid area. This study helps to separate the influences of each factor on AP production and to reduce the uncertainty in estimating soil P availability. The BPN model trained with European data, however, could not produce a robust global map of AP due to the lack of representative measurements of AP for tropical regions. Filling this data gap will help us to understand the physiological basis of P-use strategies in natural soils.

River networks in subtropical agricultural hilly region become an inconvenient greenhouse gas (GHG, methane and nitrous oxide) source because of the influence of human activities, which has caused large uncertainties for refinement of national GHG inventories and their global budget. Based on field monitoring experiments at high temporal resolution, we employed regression tree and importance analysis to identify quantitatively factors that influence the diffusive flux of GHGs to provide a scientific basis for reducing GHG emissions and controlling regional carbon and nitrogen losses. The results indicate that significant spatiotemporal variation of methane (CH4) nitrous oxide (N2O) diffusion occurs in all the four reaches (W1, W2, W3 and W4) of Tuojia river networks. Among them, W1 contributed lowest CH4 (22.55 μg C m-2 h-1) and N2O (5.00 μg N m-2 h-1) diffusive flux than the other three (P < 0.05), while W4 offered highest CH4 (166.15 μg C m-2 h-1) and N2O (30.47 μg N m-2 h-1) diffusive flux but with no statistically significant difference between W2 and W3 due to homogeneous extraneous nutrition loading into the two reaches. W4 also contributed largest cumulative flux of CH4 (14.55 kg C ha-1 yr-1) and N2O (2.69 kg N ha-1 yr-1) in Tuojia River networks (P < 0.05). Furthermore, the regression tree and importance analysis indicate that, in the anaerobic environment, dissolved oxygen saturation controlled the production and diffusion for both CH4 and N2O. The findings of this investigation highlighted that decision support tools provide an effective pathway to enhance the GHG mitigation technology research in agroecosystems and simultaneously shed light on the global campaign on refinement of national GHG inventories as well as regional nutrient management.

Increasingly frequent urban waterlogging disasters, which are mainly caused by the increase in impervious surfaces due to rapid urbanization, have attracted public attention. Green roofs are conducive to increasing the urban pervious surface area to control sources of runoff, which has great significance for the ecological environment. This study uses the green roof of the administrative building of Jinling Primary School in Nanjing as the study area. 76 rainfall-runoff events collected over 17 months (2016-06-2017-10) were used to calculate the comprehensive runoff control ability and factors influencing the green roof in the context of the site scale. Based on life cycle assessment theory, the benefits of stormwater regulation over its 30-year life cycle were quantitatively evaluated. The results show that:① The average retention of the green roof was 62.7%, which could have a significant impact on runoff and peak flow, reducing the runoff time and delaying the flood peak. ② The green roof has a strong ability to retain runoff during small and medium rainfall; however, this ability becomes low when the retention capacity is saturated or not fully recovered, even in small rainfall-runoff events. ③ The main factors affecting the retention ability of the green roof are the total rainfall, rainfall intensity, and water content of the growth substrate soil. ④ The green roof has great economic benefits, with a construction cost of about 12.51 yuan·m-3 and a return on investment of 0.41. The results of this study can provide an important scientific basis and decision-making reference for the planning and construction of green roofs and the promotion of related policies.

Randomized controlled trials (RCTs) usually enroll heterogeneous study population, and thus it is interesting to identify subgroups of patients for whom the treatment may be beneficial or harmful. A variety of methods have been developed to do such kind of post hoc analyses. Conventional generalized linear model is able to include prognostic variables as a main effect and predictive variables in an interaction with treatment variable. A statistically significant and large interaction effect usually indicates potential subgroups that may have different responses to the treatment. However, the conventional regression method requires to specify the interaction term, which requires knowledge of predictive variables or becomes infeasible when there is a large number of feature variables. The Least Absolute Shrinkage and Selection Operator (LASSO) method does variable selection by shrinking less clear effects (including interaction effects) to zero and in this way selects only certain variables and interactions for the model. There are many tree-based methods for subgroup identification. For example, model-based recursive partitioning incorporates parametric models such as generalized linear models into trees. The model incorporated is usually a simple model with only the treatment as covariate. Predictive and prognostic variables are found and incorporated automatically via the tree. The present article gives an overview of these methods and explains how to perform them using the free software environment for statistical computing R (version 3.3.2). A simulated dataset is employed for illustrating the performance of these methods.

Accumulations of heavy metals in urban soils are highly spatial heterogeneity and affected by multiple factors including soil properties, land use and pattern, population and climatic conditions. We studied accumulation risks of Cd, Cu, Pb and Zn in unban soils of Beijing and their influencing based on the regression tree analysis and a GIS-based overlay model. Result shows that Zinc causes the most extensive soil pollution and Cu result in the most acute soil pollution. The soil\'s organic carbon content and CEC and population growth are the most significant factors affecting heavy metal accumulation. Other influence factors in land use pattern, urban landscape, and wind speed also contributed, but less pronounced. The soils in areas with higher degree of urbanization and surrounded by intense vehicular traffics have higher accumulation risk of Cd, Cu, Pb, and Zn.