Early determination of potential critical quality attributes of therapeutic antibodies in developability studies through surface plasmon resonance-based relative binding activity assessment.

Precise measurement of the binding activity changes of therapeutic antibodies is important to determine the potential critical quality attributes (CQAs) in developability assessment at the early stage of antibody development. Here, we report a surface plasmon resonance (SPR)-based relative binding activity method, which incorporates both binding affinity and binding response and allows us to determine relative binding activity of antibodies with high accuracy and precision. We applied the SPR-based relative binding activity method in multiple forced degradation studies of antibody developability assessment. The current developability assessment strategy provided comprehensive, precise characterization of antibody binding activity in the stability studies, enabling us to perform correlation analysis and establish the structure-function relationship between relative binding activity and quality attributes. The impact of a given quality attribute on binding activity could be confidently determined without isolating antibody variants. We identified several potential CQAs, including Asp isomerization, Asn deamidation, and fragmentation. Some potential CQAs affected binding affinity of antibody and resulted in a reduction of binding activity. Certain potential CQAs impaired antibody binding to antigen and led to a loss of binding activity. A few potential CQAs could influence both binding affinity and binding response and cause a substantial decrease in antibody binding activity. Specifically, we identified low abundance Asn33 deamidation in the light chain complementarity-determining region as a potential CQA, in which all the stressed antibody samples showed Asn33 deamidation abundances ranging from 4.2% to 27.5% and a mild binding affinity change from 1.76 nM to 2.16 nM.

Type 2 diabetes attributable to ambient particulate matter pollution: a global burden study from 1990 to 2019.

Background: This study assesses the changes over time and geographical locations in the disease burden of type 2 diabetes (T2D) attributed to ambient particulate matter pollution (APMP) from 1990 to 2019 in 204 countries and regions with different socio-demographic indexes (SDI). Methods: The Global Burden of Diseases Study 2019 (GBD2019) database was used to analyze the global burden of T2D attributed to APMP. This study evaluated both the age-standardized death rate (ASDR) and disability-adjusted life years (DALYs) related to T2D, comparing data from 1990 to 2019. Estimated Annual Percentage Changes (EAPCs) were also utilized to investigate the trends over the 30-year study period. Results: The global age-standardized DALY rate and ASDR exhibited an increasing trend, with an EAPC of 2.21 (95% CI: 2.15 to 2.27) and 1.50 (95% CI: 1.43 to 1.58), respectively. This rise was most notable among older adult populations, men, regions in Africa and Asia, as well as low-middle SDI regions. In 2019, the ASDR for T2D caused by APMP was recorded at 2.47 per 100,000 population, while the DALY rate stood at 108.98 per 100,000 population. Males and countries with middle SDI levels displayed significantly high age-standardized death and DALY rates, particularly noticeable in Southern Sub-Saharan Africa. Conversely, regions with high SDI levels like High-income North America demonstrated decreasing trends. Conclusion: This study reveals a significant increase in T2D worldwide as a result of APMP from 1990 to 2019, with a particular emphasis on its impact on men, the older adult, and regions with low to middle SDI levels. These results underscore the urgent necessity for implementing policies aimed at addressing air pollution in order to reduce the prevalence of T2D, especially in the areas most heavily affected.

Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.

Background: Multiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment. Methods: A total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors. Results: We found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p <.01). Conclusion: These data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.

A study on the impact of ESG rating on green technology innovation in enterprises: An empirical study based on informal environmental governance.

Improving corporate green technology innovation is a key link in achieving green transformation and development. Compared with formal environmental regulations that force companies to carry out green innovation passively, ESG ratings under soft environmental regulations can better stimulate the internal motivation of companies. This study uses the ESG ratings of listed companies published for the first time by SynTao Green Finance as an exogenous impact. Taking China's A-share listed companies from 2011 to 2022 as a research sample, the multi-period differences-in-differences model was used to empirically test the impact of ESG rating soft supervision on corporate green technology innovation. The results show that the impact of ESG rating events as a soft market regulation has a significant positive impact on the improvement of corporate green technology innovation. This conclusion still holds after a series of robustness tests. Meanwhile, enterprise digital transformation plays a positive regulatory role. The heterogeneity test shows that the green technology innovation of state-owned enterprises is more affected by ESG ratings. Mechanism research has found that ESG rating events promote corporate green technology innovation by easing corporate financing constraints and reducing managerial myopia. Further research found that under the influence of the external environment, intensified market competition and increased attention from the capital market are also conducive to the improvement of corporate green technology innovation. This study strengthens the corporate ESG concept under the guidance of green development and provides empirical evidence for promoting corporate green transformation.

Mid-long term boundary dynamic optimization of open-pit coal mine considering coal price fluctuation.

The delineation of the open-pit mining boundary, particularly in the context of medium to long-term planning, forms the foundation of mining design. However, due to the non-linear and dynamic nature of the economic and technical parameters influencing boundary delineation, determining the optimal mining boundary can be exceedingly challenging. Currently, most boundary optimization methods assume that block parameters remain fixed, which results in enterprises assuming a certain level of risk when facing changes in internal and external conditions. In this regard, this paper introduces the concept of "achievement degree" to reflect the risk associated with the results of boundary design. Using coal prices as an example, this article applies the predicted coal price curve to boundary optimization adjustments by specifying the "achievement degree" requirements for various time periods, thereby facilitating risk-controlled and economically optimal boundary decisions. Taking the illustrative case of an idealized small-scale inclined coal seam open-pit mine, adjustments to the boundary closely track variations in coal prices, further enhancing returns. The results demonstrate that the method proposed in this paper can increase overall revenue by approximately 51.15% within the forecast period, while effectively managing risks.

Study on coupling coordination degree of urbanization and ecological environment in Chengdu-Chongqing economic circle from 2002 to 2018.

Under the western development strategy of China, the urbanization process of Chengdu-Chongqing economic circle (CCEC) develops rapidly, but also brings a series of ecological and environmental problems. Understanding the coordination mechanism that links urbanization and the ecological environment (UE) is crucial for promoting environmental conservation and sustainable development. Using the comprehensive nighttime light index (CNLI) and the remote sensing ecological index (RSEI), this study objectively evaluated the urbanization level and ecological environment quality and used the modified coupling coordination degree model to quantify the coupling coordination of UE for the two indexes. The results show that (1) the urbanization level of each city in the CCEC presents an increasing trend year by year, showing a circle distribution pattern with the twin cities as the core. (2) The overall eco-environmental quality presents a fluctuating upward trend, and in recent years, it has been significantly improved, showing a spatial pattern of higher in the surrounding areas and lower in the middle. (3) The overall coupling coordination of the study area is developing, and the upward trend is larger in recent years. The spatial distribution is centered on the "double nucleus," and the distribution of circles is characterized by a gradual decrease from the inside to the outside; the coupling coordination of cities in the northwest and east of the CCEC is decreasing, and that of cities in the northeast and southwest is increasing. During the study period, a total of 5 cities have reached the level of coordinated development, while most other cities are in the stage of uncoordinated development, mainly due to the lagging development of urbanization. CCEC still needs to strengthen the construction of urbanization, in order to improve the degree of coordination between economic development and ecological environment.

Risk assessment, fitness cost, cross-resistance, and mechanism of tetraniliprole resistance in the rice stem borer, Chilo suppressalis.

The rice stem borer (RSB), Chilo suppressalis, a notorious rice pest in China, has evolved a high resistance level to commonly used insecticides. Tetraniliprole, a new anthranilic diamide insecticide, effectively controls multiple pests, including RSB. However, the potential resistance risk of RSB to tetraniliprole is still unknown. In this study, the tetraniliprole-selection (Tet-R) strain was obtained through 10 continuous generations of selection with tetraniliprole 30% lethal concentration (LC30 ). The realized heritability (h2 ) of the Tet-R strain was 0.387, indicating that resistance of RSB to tetraniliprole developed rapidly under the continuous selection of tetraniliprole. The Tet-R strain had a high fitness cost (relative fitness = 0.53). We established the susceptibility baseline of RSB to tetraniliprole (lethal concentration at LC50  = 0.727 mg/L) and investigated the resistance level of 6 field populations to tetraniliprole. All tested strains that had resistance to chlorantraniliprole exhibited moderate- to high-level resistance to tetraniliprole (resistance ratio = 27.7-806.8). Detection of ryanodine receptor (RyR) mutations showed that the Y4667C, Y4667D, I4758M, and Y4891F mutations were present in tested RSB field populations. RyR mutations were responsible for the cross-resistance between tetraniliprole and chlorantraniliprole. Further, the clustered regularly interspaced palindromic repeats (CRISPR) / CRISPR-associated protein 9-mediated genome-modified flies were used to study the contribution of RyR mutations to tetraniliprole resistance. The order of contribution of a single RyR mutation to tetraniliprole resistance was Y4667D > G4915E > Y4667C ≈ I4758M > Y4891F. In addition, the I4758M and Y4667C double mutations conferred higher tetraniliprole resistance than single Y4667C mutations. These results can guide resistance management practices for diamides in RSB and other arthropods.

Three main dimensions reflected by national SDG performance.

Unraveling the complexity of the 17 interacting sustainable development goals (SDGs) is crucial for their achievement. Empirically revealing the dimensions of the SDGs helps generalize the dominant features of SDGs and better understand their drivers. Here, using a database of 166 countries' progress toward achieving each individual SDG, we found that about 70% of the variability of national SDG performance can be captured by three dimensions: socioeconomic development at the expense of resource and climate, the environment, and development at the expense of equality. Moreover, these dimensions are mainly affected by the economy; as gross domestic product (GDP) per capita increases, the first dimension increases monotonically, the environment dimension decreases and then increases, and the inequality dimension increases and then decreases. Our findings indicate a dim prospect of eventually achieving all SDGs because of the conflicts between economic growth and resource and climate goals under the current development paradigm, highlighting the importance of sustainable transformation.

China's pilot free trade zone and green high-quality development: an empirical study from the perspective of green finance.

Achieving the integration of green and economic development has become the key to promoting high-quality economic development in China. In the context of dual-cycle development, a higher level of opening up will be an important prerequisite for achieving this goal. This article uses panel data from 30 provinces in China from 2008 to 2020 as the research sample and takes the establishment of the Pilot Free Trade Zone (PFTZ) as a quasi-natural experiment. By constructing the Green Dual-Cycle High-Quality Development (GDHD) indicator system, taking green finance as the starting point, industrial structure adjustment and technological innovation ability as the perspective of differentiation. This article uses the difference-in-differences method and the intermediary effect model to explore the impact of PFTZ on regional green high-quality development. The study found that establishing PFTZ has significantly promoted regional green high-quality development, and this effect remains valid under multiple robustness tests. Further research has found that, under the advantages of green finance in PFTZ, regional industrial restructuring and the improvement of green innovation capabilities are the main ways to achieve this goal. This study provides a new development path for China's PFTZ to promote high-quality regional development better.

China's green data center development:Policies and carbon reduction technology path.

Data center is a very important infrastructure to support the development of information technology, and its development and increment are very remarkable. However, with the rapid and large-scale development of data centers, the problem of energy consumption turns to be also very prominent. Under the background of global carbon peak and carbon neutrality, developing green and low-carbon data centers has become an inevitable trend. This paper reviews and analyzes the policies and their roles in promoting China's green development of data centers in the past 10 years, summarizes the current situation of the implementation of green data center projects in China and gives the changes of PUE limits of data centers under the policy constraints. Application of green technologies is an important measure for energy-saving and low-carbon development of data centers, so encouraging innovation and application of green technologies in data center is also a priority task in relevant policies. This paper points out the green and low-carbon technology system of data centers, further summarizes energy-saving and carbon-reducing technologies in IT equipment, cooling system, power supply and distribution system, lighting, intelligent operation and maintenance, and provides an outlook on the future green development of data centers.

Boundary optimization of inclined coal seam open-pit mine based on the ISSA-LSSVR coal price prediction method.

As an important link in the complex system engineering project of open pit mining, the quality of the boundary determines the performance of the project to a large extent. However, changes in economic indicators may raise doubts about the optimality of mining boundaries. In this article, a coal price time series forecasting model that considers some amount of redundancy is proposed, which combines an improved sparrow search algorithm (ISSA) and a least squares support vector regression machine regression (LSSVR) algorithm. The optimal values of the penalty factor and kernel function parameter of the LSSVR model are selected by ISSA, which improves the prediction accuracy and generalization performance of the forecasting model. A multistep decision optimization method under fluctuating coal price conditions is proposed, and the model prediction results are applied to the boundary optimization design process. Using the widely applied block model as the basis, a set of optimal production nested pits is obtained, allowing the realm design results to fit the coal price fluctuation trend and further enhance enterprise efficiency. The applicability and effectiveness of this method were verified by taking an ideal two-dimensional model and an inclined coal seam open-pit coal mine in Xinjiang as an example.

Distributed Multiagent Reinforcement Learning With Action Networks for Dynamic Economic Dispatch.

A new class of distributed multiagent reinforcement learning (MARL) algorithm suitable for problems with coupling constraints is proposed in this article to address the dynamic economic dispatch problem (DEDP) in smart grids. Specifically, the assumption made commonly in most existing results on the DEDP that the cost functions are known and/or convex is removed in this article. A distributed projection optimization algorithm is designed for the generation units to find the feasible power outputs satisfying the coupling constraints. By using a quadratic function to approximate the state-action value function of each generation unit, the approximate optimal solution of the original DEDP can be obtained by solving a convex optimization problem. Then, each action network utilizes a neural network (NN) to learn the relationship between the total power demand and the optimal power output of each generation unit, such that the algorithm obtains the generalization ability to predict the optimal power output distribution on an unseen total power demand. Furthermore, an improved experience replay mechanism is introduced into the action networks to improve the stability of the training process. Finally, the effectiveness and robustness of the proposed MARL algorithm are verified by simulation.

Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China.

Ambient PM2.5 pollution is a leading environmental health risk factor worldwide. The spatial resolution of PM2.5 concentrations and population strongly impacts PM2.5-related health impact estimates. However, long-term variations and regional differences in this impact have rarely been explored, particularly in China. Here, by aggregating satellite-derived PM2.5 concentration and population datasets at 1-km resolution in China to coarser resolutions (10, 50, and 100 km), we evaluated decadal changes in the impact of resolution on health assessments at national and local scales. For the sensitivity of population-weighted mean (PWM) PM2.5 concentrations to resolution, we found that the national PWM PM2.5 concentration decreased with coarser resolutions; this pattern was widely observed and was more obvious in southern and central China and the Sichuan Basin. The results showed that the sensitivity of national PWM PM2.5 concentrations to resolution continuously weakened from 2010 to 2020, likely due to a reduction in the spatial heterogeneity of PM2.5 concentrations in regions with high sensitivity. This weakness caused a large underestimation of the long-term trend of national PWM PM2.5 using a 100-km resolution, which was 7% lower than the trend at 1 km. Regarding the sensitivity of PM2.5-attributable mortality to resolution, most of China exhibited a pattern in which attributable mortality decreased with coarser resolution. The sensitivity of the estimated PM2.5-attributable mortality to resolution also weakened over time on a national scale and in most parts of China. Nevertheless, the weakness for mortality sensitivity was not as apparent as for PWM PM2.5 sensitivity. This was likely because different drivers played distinct roles in the temporal variation of the mortality sensitivity: population aging enhanced the sensitivity, and variations in PM2.5 concentrations and population distribution both weakened the sensitivity. However, the national attributable mortality trend at a 100-km resolution was still underestimated by 1.75% relative to the 1-km resolution.

Findings on agricultural cleaner production in the three Gorges Reservoir Area.

The 'rural revitalization strategy' is important to achieve sustainable development in the Three Gorges Reservoir Area (TGRA), the largest reservoir in China, located in the poverty alleviation region of the Qinling-Daba mountains, and characterized as mountainous and hilly dominated by backwards resettlements. Pig farming around the reservoir area is a key industry, accounting for 0.90% of the arable land area in the country, while the annual pig market represents a 1.37% share of the national figure. Here, 12 study sites were investigated on-site for understanding agricultural green development in the TGRA. We found two main prevailing models: one of ecological circulation (EC), based on animal husbandry and recycling. Of the 12 site, six cases of the ecological circulation model relied on pig husbandry, combined with cropping (grains/fruits/vegetables) by eco-industrial chains, such as pig-biogas-fruit (grain/vegetables), to prevent environmental pollution, while promoting agricultural economic growth by recycling fecal residues and wastewater (FSW) from pig-husbandry facilities to the fields. Our analysis predicted that a farm holding 10,000 pigs may save inorganic nitrogen and phosphorus fertilizers by as much as 74.36 and 11.15 ton·a-1, respectively. On the other hand, five cases of ecological models oriented towards agritourism provided tourists with high-quality ecological products while coordinating environment protection with economic development. In addition, 11 research points applied water and fertilizer integration system for the purpose of saving water. However, lack of sufficient supporting arable land made intensive pig farming a risk of ecological degradation. Green control technologies are rarely used leading to an increase in the type and amount of pesticides. Our study has theoretical and practical significance for decision-makers to promote agricultural cleaner production (ACP).

Primary assessment of the diversity of Omicron sublineages and the epidemiologic features of autumn/winter 2022 COVID-19 wave in Chinese mainland.

With the recent ongoing autumn/winter 2022 COVID-19 wave and the adjustment of public health control measures, there have been widespread SARS-CoV-2 infections in Chinese mainland. Here we have analyzed 369 viral genomes from recently diagnosed COVID-19 patients in Shanghai, identifying a large number of sublineages of the SARS-CoV-2 Omicron family. Phylogenetic analysis, coupled with contact history tracing, revealed simultaneous community transmission of two Omicron sublineages dominating the infections in some areas of China (BA.5.2 mainly in Guangzhou and Shanghai, and BF.7 mainly in Beijing) and two highly infectious sublineages recently imported from abroad (XBB and BQ.1). Publicly available data from August 31 to November 29, 2022 indicated an overall severe/critical case rate of 0.035% nationwide, while analysis of 5706 symptomatic patients treated at the Shanghai Public Health Center between September 1 and December 26, 2022 showed that 20 cases (0.35%) without comorbidities progressed into severe/critical conditions and 153 cases (2.68%) with COVID-19-exacerbated comorbidities progressed into severe/critical conditions. These observations shall alert healthcare providers to place more resources for the treatment of severe/critical cases. Furthermore, mathematical modeling predicts this autumn/winter wave might pass through major cities in China by the end of the year, whereas some middle and western provinces and rural areas would be hit by the upcoming infection wave in mid-to-late January 2023, and the duration and magnitude of upcoming outbreak could be dramatically enhanced by the extensive travels during the Spring Festival (January 21, 2023). Altogether, these preliminary data highlight the needs to allocate resources to early diagnosis and effective treatment of severe cases and the protection of vulnerable population, especially in the rural areas, to ensure the country's smooth exit from the ongoing pandemic and accelerate socio-economic recovery.

Global assessment of nature's contributions to people.

Synergistically maintain or enhance the numerous beneficial contributions of nature to the quality of human life is an important but challenging question for achieving Sustainable Development Goals. However, the spatiotemporal distributions of global nature's contributions to people (NCPs) and their interactions remain unclear. We built a rapid assessment indicator framework and produced the first spatially explicit assessment of all 18 NCPs at a global scale. The 18 global NCPs in 1992 and 2018 were globally assessed in 15,204 subbasins based on two spatial indicator dimensions, including nature's potential contribution and the actual contribution to people. The results show that most of the high NCP values are highly localized. From 1992 to 2018, 6 regulating NCPs, 3 material NCPs, and 2 nonmaterial NCPs declined; 29 regulating-material NCP combinations (54 in total) dominated 76% of the terrestrial area, and the area with few NCPs accounted for 22%; and synergistic relationships were more common than tradeoff relationships, while the relationships among regulating and material NCPs generally traded-off with each other. Transitional climate areas contained few NCPs and have strong tradeoff relationships. However, the high synergistic relationship among NCPs in low latitudes could be threatened by future climate change. These findings provide a general spatiotemporal understanding of global NCP distributions and can be used to interpret the biogeographic information in a functional way to support regional coordination and achieve landscape multifunctionality for the enhancement of human well-being.

Flexible and Robust Triboelectric Nanogenerators with Chemically Prepared Metal Electrodes and a Plastic Contact Interface Based on Low-Cost Pressure-Sensitive Adhesive.

Triboelectric nanogenerators (TENGs) are devices that can harvest energy from mechanical motions; such devices can be used to power wearable sensors and various low-power electronics. To increase the lifetime of the device, scientists mainly use the method of making TENG in a hard skeleton to simplify the complex possible relative movements between two triboelectric parts. However, the hard skeletons cannot be embedded in soft and lightweight clothing. To make matters worse, the materials used in the garments must be able to withstand high mechanical forces when worn, such as the pressure of more than 100 KPa exerted by body pressure or everyday knocks. Notably, the TENGs are usually made of fragile materials, such as vacuum-evaporated metal electrodes and nano-sized coatings, on the contact interface; these electrodes and coatings often chip or wear off under the action of external loads. In this work, we succeeded in creating a thin, light-weight, but extremely robust garment-integrated triboelectric nanogenerator (G-TENG) that can be embedded in clothing and pass the water wash test. First, we chemically deposited a durable electrode with flexible properties for G-TENG using a novel technique called polymer-assisted metal deposition (PAMD). The as-formed metal electrodes are firmly bonded to the plastic substrate by a sub-10 nm adhesive polymer brush and can withstand a pressure of 22.5 MPa and a tear force of 0.7 MPa. We then removed the traditionally used fragile nanoparticle materials and the non-durable poly-dimethylsiloxane (PDMS) layer at the triboelectric interface, and then used a cost-effective, durable and slightly flowable pressure-sensitive adhesive to form a plastic contact interface. Such a soft plastic interface can ensure full contact of the triboelectric materials, which is excellent in complex environments and ultimately improves the power generation efficiency of the devices. The as-formed low-cost energy harvesting device could become an industry standard for future smart clothing.

Prediction of wastewater discharge based on GRA-LSTM: a case study of Beijing.

Water resources, as one of the indispensable resources for urban development, have become an important factor limiting the sustainable development of cities. In order to promote sustainable urban development, Beijing has set the work task of reaching 99% of urban and rural wastewater treatment rate from 2020 to 2035. Accurate prediction of future wastewater discharge is essential to achieve the target. For this reason, this study takes Beijing as the research object and constructs a combined prediction model based on gray relational analysis and long- and short-term memory (GRA-LSTM). Firstly, gray relational analysis (GRA) is used to analyze the correlation of the experimental data indicators affecting the amount of wastewater discharged in order to obtain experimental data indicators with stronger correlation. Secondly, the long short-term memory (LSTM) model was used to learn the characteristics of the key impact indicators and obtain the optimal model parameters. The results showed that the mean absolute percentage error (MAPE) value of the combined GRA-LSTM model constructed in this study was 5.62%, and the prediction accuracy was higher than that of the other seven prediction models. Then, three scenarios with low, medium, and high dimensions were set to predict the wastewater discharge in Beijing from 2020 to 2035, and the prediction result that the wastewater discharge in Beijing will still continue to grow was obtained. Finally, in order to improve the water utilization rate and promote the sustainable development of the city, this study proposes relevant policy recommendations in terms of the unbalanced urban-rural development of Beijing's wastewater treatment capacity and the increase of recycled water usage.

Untangling the interactions among the Sustainable Development Goals in China.

Understanding the interactions (synergies and trade-offs) among the Sustainable Development Goals (SDGs) is crucial for enhancing policy coherence between different sectors. However, spatial differences in the SDG interactions and their temporal variations at the sub-national scale are still critical gaps that need to be urgently filled. Here, we assess the spatial and temporal variation of the SDG interactions in China based on the systematic classification framework of SDGs. The framework groups the seventeen SDGs into three categories, namely "Essential Needs", "Objectives", and "Governance". Spatially, we found that the SDGs in "Essential Needs" & "Objectives" and "Essential Needs" & "Governance" generally show trade-offs in the eastern provinces of China. Synergies among all three SDG categories are observed in some central and western China provinces, which implies that these regions conform to sustainable development patterns. In addition, temporally, the synergies of the three SDG categories have shown a weakening trend in the last decade, mainly due to the regional differences in the progress of SDG7 (Affordable and Clean Energy). Overall, our results identify the necessity for provinces to enhance the synergies between SDG12 (Responsible Production and Consumption) and other SDGs to tackle the trade-offs between the "Essential Needs" and "Objectives". Meanwhile, promoting the progress of SDG7 will also contribute to balanced development across provinces.

Does global positioning system-based navigation dependency make your sense of direction poor? A psychological assessment and eye-tracking study.

Background: Global positioning system (GPS)-based navigation apps are very useful in our lives. However, whether and how the usage of these apps affects spatial cognition and the sense of direction is still unclear. Methods: A total of 108 individuals were recruited and completed the GPS dependence, internet gaming behavior, and impulsivity test using scales. The eye-tracking-based general mental rotation (MR) task and target finding (TF; require individuals to find a target specified in a 3D street map in a rotated version of top 2D view map) task were used to assess their spatial cognition and the sense of direction. The correlation was used to relate GPS navigation usage, spatial cognition ability, and impulsivity. Subgroup analyses stratifying by gaming hours of individuals (< 2 h or ≥ 2 h) or maps (countryside or city) in TF task were performed. The moderating and mediating effect analyses were conducted to verify these relationships. Results: The GPS dependency score was nominal positively correlated with fixations in the TF task in the entire cohort (r = 0.202, unadjusted p = 0.036); it was significant in city (r = 0.254, p = 0.008) and gaming time of < 2 h (r = 0.459, p = 0.001) subgroups. The high-score (upper 30%) group of GPS dependency had more fixations on the original target building in the training area and indicative building in the test area than the low-score (lower 30%) group. GPS dependency was not associated with the correct rate and reaction time in the TF task or any of the indicators in the MR task (p > 0.05). The GPS dependency mediated the indirect effect of impulsivity on the fixations on TF. The internet gaming time moderated the association between GPS dependency and fixations on TF. Conclusion: The dependency on GPS-based navigation apps was associated with impaired spatial cognition but may not significantly affect the sense of direction.