A comprehensive review of the environmental benefits of urban green spaces.

This detailed analysis highlights the numerous environmental benefits provided by urban green spaces, emphasizing their critical role in improving urban life quality and advancing sustainable development. The review delves into critical themes such as the impact of urban green spaces on human health, the complex interplay between urban ecology and sustainability, and the evaluation of ecosystem services using a comprehensive review of existing literature. The investigation thoroughly examines various aspects of green infrastructure, shedding light on its contributions to social cohesion, human well-being, and environmental sustainability in general. The analysis summarizes the study's findings and demonstrates the critical role of urban green spaces in urban ecology, which significantly mitigates environmental challenges. The intricate links between these green spaces and human health are thoroughly investigated, with benefits ranging from enhanced mental and physical well-being to comprehensive mental health. Furthermore, the analysis emphasizes how green spaces benefit urban development by increasing property values, boosting tourism, and creating job opportunities. The discussion also considers possible futures, emphasizing the integration of technology, the advancement of natural solutions, and the critical importance of prioritizing health and well-being in the design of urban green spaces. To ensure that urban green spaces are developed and maintained as essential components of resilient and sustainable urban environments, the assessment concludes with practical recommendations for communities, urban planners, and legislators.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Role of automated functional imaging and myocardial work in assessment of cardiac function in children with obstructive sleep apnea.

BACKGROUND: Early identification of abnormal left ventricular function in children with obstructive sleep apnea (OSA) is difficult using conventional echocardiographic indices and commonly used clinical markers of myocardial damage. We sought to investigate the value of automatic function imaging and myocardial work parameters in predicting early cardiac impairment in children having OSA with preserved left heart function and thereby identifying an optimal index for assessment. PATIENTS AND METHODS: Fifty-two children who presented with symptoms of nocturnal sleep snoring and open-mouth breathing and 34 healthy controls were enrolled in this study. Clinical characteristics and conventional echocardiographic data were collected, and image analysis was performed using two-dimensional speckle-tracking echocardiography to obtain left ventricular global longitudinal strain (GLS), post-systolic index, peak strain dispersion, global work index (GWI), global constructive work (GCW), global wasted work, and global work efficiency. RESULTS: Children with OSA had significantly lower GLS, GWI, and GCW than those without (P < 0.05). Additionally, GWI (ß = -32.87, 95% CI: -53.47 to -12.27), and GCW (ß = -35.09, 95% CI: -55.35 to -14.84) were found to correlate with the disease severity in the multiple linear regression mode, with worsening values observed as the severity of the disease increased. ROC curve analysis revealed that GCW was the best predictor of myocardial dysfunction, with an AUC of 0.809 (P < 0.001), and the best cutoff point for diagnosing myocardial damage in children with OSA was 1965.5 mmHg%, with a sensitivity of 92.5% and a specificity of 58.7%. CONCLUSIONS: GLS, GWI, and GCW were identified as predictors of myocardial dysfunction in children with OSA, with GCW being the best predictor.

Transformation of 6PPDQ during disinfection: Kinetics, products, and eco-toxicity assessment.

N-phenyl-N'-(1,3-dimethyl butyl)-p-phenylenediamine-quinone (6PPDQ) currently arouses broad concerns because of its acute lethality to coho salmon and rainbow trout at environmentally relevant concentrations and the wide occurrence in runoff-impacted water. Investigation on the fate and transformation of 6PPDQ in various treatment processes is necessary for its risk assessment and control. Here, we explored the transformation of 6PPDQ during disinfection with its precursor 6PPD as a reference, focusing on kinetics, products, and toxicity variation. 6PPDQ readily reacted with hypochlorite and chlorine dioxide with second-order rate constants of 2580 ± 143 M-1 s-1 and 614 ± 52 M-1 s-1 (pH 7.0 and 25 °C), which are slightly lower than the reactions of 6PPD. We tentatively identified thirteen transformation products for 6PPDQ and eight for 6PPD in reaction with the two disinfectants. It seems that the quinone ring of 6PPDQ and the p-phenylenediamine moiety of 6PPD are reactive sites. The transformation of these compounds probably proceeds through Cl-substitution, ring cleavage, hydroxylation, and amine oxidation and hydrolysis. Tests with zebrafish embryos revealed that the transformation products of 6PPDQ could have higher eco-toxicity than the parent compound, while the toxicity of the 6PPD products remained nearly unchanged. The increased toxicity of 6PPDQ during disinfection highlights the necessity to substantially reduce its content before the disinfection of runoff-impacted water.

Microwave deicing properties and carbon emissions assessment of asphalt mixtures containing steel slag towards resource conservation and waste reuse.

A large amount of solid waste, such as steel slag (SS), is generated annually. At the same time, the shortage of road construction materials is becoming a concern. In this study, to recycle and reuse SS as a substitute for natural aggregates to achieve resource conservation and sustainable development of roads were conducted. First, the electromagnetic performance of SS was explored to evaluate its wave-absorbing properties. Next, the effect of different SS contents on heating properties, surface temperature, heating uniformity, and ice melting time (IMT) were investigated. Finally, the carbon emissions assessment (CEA) of conventional asphalt mixture (CAM) and steel slag asphalt mixture (SSAM) was compared. Results indicated that SS has ferromagnetic behavior and higher electromagnetic parameters, showing better wave-absorbing properties than limestone. There were three stages during microwave heating (MH): ice melting, moisture emitting, and stabilization. In addition, heating uniformity tends to be poor with the increase of SS, and samples with 100 % content of SS have the highest standard deviation of 21.04 °C and 20.77 °C after 270 s at -10 °C and - 20 °C. Samples containing 50 % SS have the best microwave deicing properties, which can reduce the IMT by 28.57 % to 46.18 % at different initial freezing temperatures and ice thickness compared to CAM. Furthermore, CEA revealed that CAM and SSAM's carbon emissions over road construction's life cycle are similar (around 27,000 kg) and originate mainly from the mixing and raw material extraction phases. However, SSAM leads to better environmental and economic benefits and provides an exemplary resource conservation and waste reuse solution.

Assessment of Pulmonary Arteries Hemodynamics and Its Relationship With Cardiac Remodeling and Myocardial Fibrosis in Athletes With Four-Dimensional Flow MRI.

BACKGROUND: Exercise-induced cardiac remodeling (CR) and myocardial fibrosis (MF) can increase cardiovascular risk in athletes. Early detection of pulmonary arterial hemodynamics parameters among athletes may be beneficial in optimizing the frequency of clinical follow-ups. PURPOSE: To analyze the hemodynamics of pulmonary arteries and its relationship with CR and MF in athletes using four-dimensional (4D) flow MRI. STUDY TYPE: Prospective. POPULATION: One hundred twenty-one athletes (median age, 24 years; mean exercise per week 10 hours, for mean of 5 years) and twenty-one sedentary healthy controls (median age, 25 years; exercise per week <3 hours, irregular pattern). FIELD STRENGTH/SEQUENCE: True fast imaging with steady state free precession, time-resolved 3D Cartesian phase-contrast, and phase sensitive inversion recovery late gadolinium enhancement sequences at 3.0 T. ASSESSMENT: CR was defined as any cardiac parameters exceeding the 99th percentile upper reference limits, encompassing ventricular function, bi-atrium and bi-ventricle diameters, and ventricular wall thickness. MF was visually evaluated by three independent radiologists. 4D flow parameters were assessed in the main, right, and left pulmonary arteries (MPA, RPA, and LPA, respectively) and compared between different groups. Four machine learning (ML) models were developed to differentiate between athletes with and without CR and/or MF. STATISTICAL TESTS: Univariate analysis was used to compare groups. Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML models. RESULTS: Athletes had significantly higher WSSmax in the MPA, RPA, and LPA than controls. Athletes with CR and/or MF (N = 30) had significantly lower RPmax from MPA to RPA than those without (N = 91). Among the ML models, the gradient boosting machine model had the highest performance, with an AUC of 0.90. CONCLUSION: The pulmonary arterial hemodynamics parameters could differentiate CR and/or MF in athletes, which may be potential to assist in optimizing frequency of follow-up. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Erratum to "A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification".

[This corrects the article DOI: 10.34133/2022/9765307.].

Evaluation of Neighborhood-Level Disadvantage and Cognition in Mexican American and Non-Hispanic White Adults 50 Years and Older in the US.

Importance: Understanding how socioeconomic factors are associated with cognitive aging is important for addressing health disparities in Alzheimer disease. Objective: To examine the association of neighborhood disadvantage with cognition among a multiethnic cohort of older adults. Design, Setting, and Participants: In this cross-sectional study, data were collected between September 1, 2017, and May 31, 2022. Participants were from the Health and Aging Brain Study-Health Disparities, which is a community-based single-center study in the Dallas/Fort Worth area of Texas. A total of 1614 Mexican American and non-Hispanic White adults 50 years and older were included. Exposure: Neighborhood disadvantage for participants' current residence was measured by the validated Area Deprivation Index (ADI); ADI Texas state deciles were converted to quintiles, with quintile 1 representing the least disadvantaged area and quintile 5 the most disadvantaged area. Covariates included age, sex, and educational level. Main Outcomes and Measures: Performance on cognitive tests assessing memory, language, attention, processing speed, and executive functioning; measures included the Spanish-English Verbal Learning Test (SEVLT) Learning and Delayed Recall subscales; Wechsler Memory Scale, third edition (WMS-III) Digit Span Forward, Digit Span Backward, and Logical Memory 1 and 2 subscales; Trail Making Test (TMT) parts A and B; Digit Symbol Substitution Test (DSST); Letter Fluency; and Animal Naming. Raw scores were used for analyses. Associations between neighborhood disadvantage and neuropsychological performance were examined via demographically adjusted linear regression models stratified by ethnic group. Results: Among 1614 older adults (mean [SD] age, 66.3 [8.7] years; 980 women [60.7%]), 853 were Mexican American (mean [SD] age, 63.9 [7.9] years; 566 women [66.4%]), and 761 were non-Hispanic White (mean [SD] age, 69.1 [8.7] years; 414 women [54.4%]). Older Mexican American adults were more likely to reside in the most disadvantaged areas (ADI quintiles 3-5), with 280 individuals (32.8%) living in ADI quintile 5, whereas a large proportion of older non-Hispanic White adults resided in ADI quintile 1 (296 individuals [38.9%]). Mexican American individuals living in more disadvantaged areas had worse performance than those living in ADI quintile 1 on 7 of 11 cognitive tests, including SEVLT Learning (ADI quintile 5: ß = -2.50; 95% CI, -4.46 to -0.54), SEVLT Delayed Recall (eg, ADI quintile 3: ß = -1.11; 95% CI, -1.97 to -0.24), WMS-III Digit Span Forward (eg, ADI quintile 4: ß = -1.14; 95% CI, -1.60 to -0.67), TMT part A (ADI quintile 5: ß = 7.85; 95% CI, 1.28-14.42), TMT part B (eg, ADI quintile 5: ß = 31.5; 95% CI, 12.16-51.35), Letter Fluency (ADI quintile 4: ß = -2.91; 95% CI, -5.39 to -0.43), and DSST (eg, ADI quintile 5: ß = -4.45; 95% CI, -6.77 to -2.14). In contrast, only non-Hispanic White individuals living in ADI quintile 4 had worse performance than those living in ADI quintile 1 on 4 of 11 cognitive tests, including SEVLT Learning (ß = -2.35; 95% CI, -4.40 to -0.30), SEVLT Delayed Recall (ß = -0.95; 95% CI, -1.73 to -0.17), TMT part B (ß = 15.95; 95% CI, 2.47-29.44), and DSST (ß = -3.96; 95% CI, -6.49 to -1.43). Conclusions and Relevance: In this cross-sectional study, aging in a disadvantaged area was associated with worse cognitive functioning, particularly for older Mexican American adults. Future studies examining the implications of exposure to neighborhood disadvantage across the life span will be important for improving cognitive outcomes in diverse populations.

Spatial heterogeneity analysis of matching degree between endangered plant diversity and ecosystem services in Xishuangbanna.

Biodiversity and ecosystem services (ESs) are closely linked. Human activities have caused critical damage to the habitat and ecosystem function of organisms, leading to decline in global biodiversity and ecosystem services. To ensure sustainable development of local ecological environments, it is critical to analyze the spatial matching degree of biodiversity and ESs and identify ecologically vulnerable areas. Taking Xishuangbanna, southern China, as an example, we constructed a pixel-scale matching degree index to analyze the spatial matching degree of endangered plant diversity (EPD) and four ESs and classified the matching degree into low-low, low-high, high-low, and high-high four types. The results revealed a mismatch relationship of EPD and ESs in more than 70% of areas. Under the influence of altitude and land use/land cover (LULC) type, the matching degree of EPD and ESs showed obvious spatial heterogeneity. In low-altitude areas in the south of Xishuangbanna, EPD and ESs mainly showed mismatch, while high-altitude areas in the west had a better match. Natural forest was the main land cover in which EPD and ESs showed high-high match and its areal proportion was much larger than that of rubber plantation, tea plantation, and cropland. Our findings also stress the need to concentrate conservation efforts on areas exhibiting a low-low match relationship, indicative of potential ecological vulnerability. The pixel-scale spatial matching degree analysis framework developed in this study for EPD and ESs provides high-resolution maps with 30 m × 30 m pixel size, which can support the implementation of ecological protection measures and policy formulation, and has a wide range of applicability. This study provides valuable insights for the sustainable management of biodiversity and ESs, contributing to the strengthening of local ecological environment protection.

A multidisciplinary study on the social customs of the Tang Empire in the Medieval Ages.

Multidisciplinary research on human remains can provide important information about population dynamics, culture diffusion, as well as social organization and customs in history. In this study, multidisciplinary analyses were undertaken on a joint burial (M56) in the Shuangzhao cemetery of the Tang Dynasty (618-907 AD), one of the most prosperous dynasties in Chinese history, to shed light on the genetic profile and sociocultural aspects of this dynasty. The archaeological investigation suggested that this burial belonged to the Mid-Tang period and was used by common civilians. The osteological analysis identified the sex, age, and health status of the three individuals excavated from M56, who shared a similar diet inferred from the stable isotopic data. Genomic evidence revealed that these co-buried individuals had no genetic kinship but all belonged to the gene pool of the ancient populations in the Central Plains, represented by Yangshao and Longshan individuals, etc. Multiple lines of evidence, including archaeology, historic records, as well as chemical and genetic analyses, have indicated a very probable familial joint burial of husband and wives. Our study provides insights into the burial customs and social organization of the Tang Dynasty and reconstructs a scenario of civilian life in historic China.

Towards sustainable management of agricultural resources: A framework to assess the relationship between water, soil, economic factors, and grain production.

As the requirements for environmental protection and high-efficiency economies increase, grain production (GP) across the globe faces more stringent ecological constraints and economic pressure. Understanding the relationships between natural resources and economic and agricultural factors in grain-producing regions is paramount for ensuring global food security. This paper proposes a methodological framework to explore the relationships between water and soil resources (WSRs), economic input factors (EIFs), and GP. We employed the northeast region of China as a case study to advance our understanding of the factors driving the development of grain-producing capacity. We first constructed and calculated the region's water and soil comprehensive index (WSCI) to describe water-soil properties. We then used hotspot analysis to explore the spatial agglomeration characteristics of WSRs, EIFs, and GP. Finally, we used threshold regression analysis to understand the effects of EIFs and GP with the WSCI as the threshold variable. With the improvement of the WSCI, the positive impact of fertilizer and irrigation on GP shows a U-shaped curve in elasticity coefficients. The positive effect of agricultural machinery on GP decreases significantly, and the impact of labor input on GP is insignificant. These results provide new insights into the relationship between WSRs, EIFs, and GP and a reference for improving GP efficiency globally. This work thus contributes to advancing our capabilities to enable food security while considering aspects of sustainable agriculture in important grain-producing regions across the globe.

Effects of snow cover on urban light climate environment in the high latitudes of northeast China.

Light climate environment (LCE) has a significant impact on human health, behavioral characteristics, and the safety of life and property due to the high albedo of snow on the ground cover type, which in turn affects the regional climate and socio-economic development, but less relevant studies have been found. In this study, the effect of snow on daytime and nighttime light levels was quantified using comparative field observations and controlled experiments in artificial climate chambers, combined with analysis of variance and model fitting. The results of the study found that there was a significant difference between the presence and absence of snow on both daytime and nighttime light levels. During daytime, the ambient light level on the ground with snow is 5.68 times higher than without snow, an improvement of 12,711.06 Lux. At night, with moonlight, the nighttime illuminance with and without snow is 0.213 Lux and 0.01 Lux, respectively. When there is no moonlight, the snow has no significant effect on the light level. In addition, significant differences in LCE intensity with different snow depths, snow densities and black carbon (BC) pollution. At the same background light intensity, the LCE intensity varies significantly with increasing snow depth, snow density and BC pollution. The results reveals the quantitative impact of snow on LCE, providing scientific support for regional natural light energy use, human health and safety, urban environmental management and economic development.

A fully-automatic semi-supervised deep learning model for difficult airway assessment.

Background: Difficult airway conditions represent a substantial challenge for clinicians. Predicting such conditions is essential for subsequent treatment planning, but the reported diagnostic accuracies are still quite low. To overcome these challenges, we developed a rapid, non-invasive, cost-effective, and highly-accurate deep-learning approach to identify difficult airway conditions through photographic image analysis. Methods: For each of 1000 patients scheduled for elective surgery under general anesthesia, images were captured from 9 specific and different viewpoints. The collected image set was divided into training and testing subsets in the ratio of 8:2. We used a semi-supervised deep-learning method to train and test an AI model for difficult airway prediction. Results: We trained our semi-supervised deep-learning model using only 30% of the labeled training samples (with the remaining 70% used without labels). We evaluated the model performance using metrics of accuracy, sensitivity, specificity, F1-score, and the area under the ROC curve (AUC). The numerical values of these four metrics were found to be 90.00%, 89.58%, 90.13%, 81.13%, and 0.9435, respectively. For a fully-supervised learning scheme (with 100% of the labeled training samples used for model training), the corresponding values were 90.50%, 91.67%, 90.13%, 82.25%, and 0.9457, respectively. When three professional anesthesiologists conducted comprehensive evaluation, the corresponding results were 91.00%, 91.67%, 90.79%, 83.26%, and 0.9497, respectively. It can be seen that the semi-supervised deep learning model trained by us with only 30% labeled samples can achieve a comparable effect with the fully supervised learning model, but the sample labeling cost is smaller. Our method can achieve a good balance between performance and cost. At the same time, the results of the semi-supervised model trained with only 30% labeled samples were very close to the performance of human experts. Conclusions: To the best of our knowledge, our study is the first one to apply a semi-supervised deep-learning method in order to identify the difficulties of both mask ventilation and intubation. Our AI-based image analysis system can be used as an effective tool to identify patients with difficult airway conditions. Clinical trial registration: ChiCTR2100049879 (URL: http://www.chictr.org.cn).

Quantitative Assessment of Water Quality Improvement by Reducing External Loadings at Lake Erhai, Southwest China.

To quantitatively evaluate the effects on water quality improvement caused by reducing external loadings entering Lake Erhai through inflow rivers, a one-dimensional hydrodynamic and ecological model (DYRESM-CAEDYM) was set up to simulate the water quality and water level variations. The calibrated and validated model was used to conduct six scenarios for evaluating the water quality responses to different amounts of external loading reduction at Lake Erhai. The results show (1) the total nitrogen (TN) concentration of Lake Erhai will be higher than 0.5 mg/L without any watershed pollution control during April-November 2025, which cannot meet Grade II standard of the China Surface Water Environmental Quality Standards (GB3838-2002). (2) External loading reductions can significantly reduce the concentrations of nutrients and Chla at Lake Erhai. The effects of water quality improvement will be proportional to the reduction rate of external loading reductions. (3) Internal release might be an important source of pollution It needs to be seriously considered as well as external loading for mitigating the eutrophication at Lake Erhai in the future.

Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study.

The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models' performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.

Assessment of mandibular retromolar space in adults with regard to third molar eruption status.

OBJECTIVE: The aim of this study was to compare the difference in length and width of the mandibular retromolar space (RMS) stratified by the different eruption and impaction statuses of the third molars in patients with skeletal Class I malocclusion. MATERIALS AND METHODS: The right mandibular RMS in 186 adult patients categorized according to the different statuses of the third molar was analyzed by using cone-beam computed tomography (CBCT). The shortest distances between the inner lingual cortex of the mandibular body and second molar root were measured parallel to the posterior occlusal line (POL) at depths of 2, 4, 6, 8, and 10 mm (mandibular retromolar space length in root level, RLin2,4,6,8,10) on the axial slices with the cementoenamel junction (CEJ) as the reference level. The width of the RMS and second molar root was measured vertical to the POL at the terminal point of the molar distalization at depths of 2, 4, 6, 8, and 10 mm (width of the mandibular retromolar space, BW2,4,6,8,10/ width of the second molar distal root, TW2,4,6,8,10) from the CEJ. RESULTS: RL in different measurement planes was 2.72 ± 2.22 ~ 3.74 ± 2.26 for Group A, 5.27 ± 1.68 ~ 9.10 ± 2.04 for Group B, 1.94 ± 2.34 ~ 5.71 ± 4.37 for Group C, 1.83 ± 2.95 ~ 5.05 ± 4.24 for Group D, and 5.93 ± 3.97 ~ 10.52 ± 2.16 for Group E. The BW measurement results for A ~ E group were 9.71 ± 1.41 ~ 10.51 ± 1.81, 9.83 ± 1.39 ~ 12.55 ± 2.11, 9.96 ± 1.21 ~ 12.17 ± 1.62, 9.82 ± 1.47 ~ 12.28 ± 2.77, and 10.02 ± 1.20 ~ 12.75 ± 0.82, respectively. There was no significant difference between men and women in any measurements (P > 0.05). Patients with normal third molars erupted and those vertically impacted possessed larger RMS lengths than those in which the third molars were missing, horizontally impacted or mesially impacted (P < 0.05). In each measurement plane, TW was significantly smaller than BW (P < 0.05). CONCLUSIONS: Sex had no effect on the length or width of the mandibular RMS. Different statuses of third molars can also differentially affect the mandibular RMS. The mandibular RMS width is not a limit for mandibular molar distalization. CLINICAL RELEVANCE: When considering the distalization of mandibular molars, more attention should be directed to the lingual cortex of the mandible, and CBCT scans are recommended for patients who require significant mandibular molar distalization. The mandible buccal shelf and retromolar area maybe a safe zone to insert the miniscrew for molar distalization.

Characterization and health risk assessment of arsenic in natural waters of the Indus River Basin, Pakistan.

The elevated concentrations of arsenic in natural water are one of the major environmental threats to human health. However, the existing characteristics, controlling mechanisms, and associated risks of arsenic in natural waters in the Indus River Basin (IRB), Pakistan, are yet to be unequivocally understood. In this study, a total of 203 samples of surface water (SW), shallow groundwater (SGW), and deep groundwater (DGW) were collected from the IRB to assess the geochemical characteristics of arsenic and its associated health risks, as all three kinds of waters are the main sources of drinking and domestic usage. The results revealed that the arsenic concentrations in the SW, SGW, and DGW were in the ranges of 1.1-26.45, 1.05-44.44, and 0.67-41.09 µg L-1, respectively. Furthermore, the predominance of As (V) (97 %) over As(III) (3 %) confirmed that the desorption of As in oxidizing environments with elevated pH and Eh is the controlling mechanism. The hazard quotient of 11-45 % and 20-60 % samples and cancer risk of 26-64 % and 26-68 % samples indicated high health risks for the adults and children, respectively, suggesting an immense need for appropriate measures of reducing natural water arsenic concentrations in IRB from the human health perspectives.

The role of high-speed rail on green total factor productivity: evidence from Chinese cities.

Compared with other transportation facilities, high-speed rail (HSR) may be more beneficial to "green development." Based on a sample of 276 cities in China over 2005-2019, this study calculates the actual and change values of green total factor productivity (GTFP) with the stochastic frontier approach (SFA) and investigates the effect of HSR on GTFP using the time-varying difference-in-difference model and propensity score matching analysis. Our research results show a significantly positive effect of HSR on GTFP through reducing energy consumption and environmental pollution, as well as improving technological innovation. HSR-connected cities are found to have outperformed non-HSR-connected cities by 12.8% regarding GTFP in the sample period, and the effect is stronger for the eastern region and/or the central cities compared with the rest of the country. The research findings of this paper have important policy implications regarding China's HSR development and its impact on economic growth as well as the natural environment.

The next step for China's national park management: Integrating ecosystem services into space boundary delimitation.

The contradiction between ecological conservation and economic development posed significant challenges to the management of National Parks. From the perspective of Ecological Economics, the cause of the contradiction is the difficulty of creating monetary profits from biodiversity conservation, which is the primary target of National Parks. Integrating Ecosystem Services (ESs) into space boundary delimitation is the next step in National Park management since ESs are closely related to human well-being and can provide monetary benefits. Extending the boundary of the National Park to high-ES areas and promoting ES trading can help generate funds for ecological restoration. Using the Sanjiangyuan National Park (SNP) as an example, this study proposed integrating ESs into National Park delimitation for sustainable National Park management. It was found that the current SNP boundary provides insufficient coverage of high-ES areas, while most of the multiple ES supply areas were dispersed to SNP's southeast edge. The Core conservation area showed the most prominent contradiction between ecological conservation and economic development, resulting in many low-level ES sites in the Traditional use area failing to be included in the Restoration area for protection. Future approaches would be well-advised to re-adjust SNP boundary by expanding the ES hotspot areas on the southeastern edge of SNP, as well as expanding funding sources via ecological product trade and other tools to supplement the input for ecological restoration. Overall, this study can act as a reference for optimizing National Parks within and beyond China, and promote the understanding of the Ecological Economy and sustainable development.