Decreasing resilience of China's coupled nitrogen-phosphorus cycling network requires urgent action.

The coupled nature of the nitrogen (N) and phosphorus (P) cycling networks is of critical importance for sustainable food systems. Here we use material flow and ecological network analysis methods to map the N-P-coupled cycling network in China and evaluate its resilience. Results show a drop in resilience between 1980 and 2020, with further decreases expected by 2060 across different socio-economic pathways. Under a clean energy scenario with additional N and P demand, the resilience of the N-P-coupled cycling network would suffer considerably, especially in the N layer. China's socio-economic system may also see greater N emissions to the environment, thus disturbing the N cycle and amplifying the conflict between energy and food systems given the scarcity of P. Our findings on scenario-specific synergies and trade-offs can aid the management of N- and P-cycling networks in China by reducing chemical fertilizer use and food waste, for example.

Unstable decoupling of CO2 emissions from sectoral economic growth calls for decarbonization policies based on multi-perspective accounting: a case study of Zhejiang, China.

Decoupling CO2 emissions from economic growth is an important cornerstone of a country's decarbonization policy. Existing research in this avenue has mainly focused on decoupling the production-based (PB) CO2 emissions from economic growth at the national level, while decoupling the consumption-based (CB) and income-based (IB) CO2 emissions from economic growth, especially at the sectoral level, has received less attention. Using China's Zhejiang province as a case study, we conduct one of the first studies focused on decoupling the PB, CB, and IB CO2 emissions from economic growth at the sectoral level. Our results reveal that (1) during 2002-2017, the sectoral decoupling level varies greatly among the three different perspectives; (2) most of the examined sectors were in unstable decoupling states while some sectors even reverted to coupling states; and (3) the drivers of CO2 emissions at the sectoral level reveal important factors, such as emissions intensity, production structure, and final demand structure, that contribute toward decarbonization. Our study demonstrates to policymakers how utilizing a multi-perspective evaluation of the decoupling of emissions from sectoral economic growth can improve the accuracy of decarbonization policies and identify critical sectors toward CO2 reduction objectives.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Quantifying spatio-temporal variation in aquaculture production areas in Satkhira, Bangladesh using geospatial and social survey.

Despite Bangladesh being one of the leading countries in aquaculture food production worldwide, there is a considerable lack of updated scientific information about aquaculture activities in remote sites, making it difficult to manage sustainably. This study explored the use of geospatial and field data to monitor spatio-temporal changes in aquaculture production sites in the Satkhira district from 2017-2019. We used Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) to locate aquaculture ponds based on the terrain elevation and slope. Radar backscatter information from the Sentinel-1 satellite, and different water indices derived from Sentinel-2 were used to assess the spatio-temporal extents of aquaculture areas. An image segmentation algorithm was applied to detect aquaculture ponds based on backscattering intensity, size and shape characteristics. Our results show that the highest number of aquaculture ponds were observed in January, with a size of more than 30,000 ha. Object-based image classification of Sentinel-1 data showed an overall accuracy above 80%. The key factors responsible for the variation in aquaculture were investigated using field surveys. We noticed that despite a significant number of aquaculture ponds in the study area, shrimp production and export are decreasing because of a lack of infrastructure, poor governance, and lack of awareness in the local communities. The result of this study can provide in-depth information about aquaculture areas, which is vital for policymakers and environmental administrators for successful aquaculture management in Satkhira, Bangladesh and other countries with similar issues.

A versatile active learning workflow for optimization of genetic and metabolic networks.

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Quantifying the synergy and trade-offs among economy-energy-environment-social targets: A perspective of industrial restructuring.

Protecting our environment while maintaining economic growth, requires a delicate balance among interlinked sustainable development policies. In this paper, we examine China's economic industries, including a high-resolution of the country's electricity sector during 2020-2030, using a multi-objective optimization model based on Input-Output analysis. This model, investigates the synergy and trade-offs of sustainable development goals in maximizing employment and GDP while minimizing energy and water consumption, CO2 emissions, and five major pollutants to advance a sustainable industrial structure adjustment pathway for China. Our results reveal that there exists both synergies and trade-offs among multiple objectives, e.g., synergy among goals of minimizing air pollutant emissions and trade-offs between minimizing energy consumption and maximizing employment. Through the planned industrial restructuring period (2020-2030), the GDP, employment, carbon emission, and energy consumption will increase respectively by, 96.1%, 7.2%, 16.8%, 16.8%, and 6.3%, while pollutant emissions would decrease. Moreover, our research indicates that energy and water conservation should be prioritized in industrial structure adjustment strategies and policies. Our model demonstrates how the synergies and trade-offs among multiple policy targets can empower policy-makers, especially in developing nations, to make more informed and optimized industrial structure adjustment policies for sustainable development.

Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis.

Peatlands in Indonesia are subject to subsidence in recent years, resulting in significant soil organic carbon loss. Their degradation is responsible for several environmental issues; however, understanding the causes of peatland subsidence is of prime concern for implementing mitigation measures. Here, we employed time-series Small BAseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) using ALOS PALSAR-2 images to assess the relationship between subsidence rates and land use/land cover (LULC) change (including drainage periods) derived from decadal Landsat data (1972-2019). Overall, the study area subsided with a mean rate of -2.646 ± 1.839 cm/year in 2018-2019. The subsidence rates slowed over time, with significant subsidence decreases in peatlands after being drained for 9 years. We found that the long-time persistence of vegetated areas leads to subsidence deceleration. The relatively lower subsidence rates are in areas that changed to rubber/mixed plantations. Further, the potential of subsidence prediction was assessed using Random Forest (RF) regression based on LULC change, distance from peat edge, and elevation. With an R2 of 0.532 (RMSE = 0.594 cm/year), this machine learning method potentially enlarges the spatial coverage of InSAR method for the higher frequency SAR data (such as Sentinel-1) that mainly have limited coverage due to decorrelation in vegetated areas. According to feature importance in the RF model, the contribution of LULC change (including drainage period) to the subsidence model is comparable with distance from peat edge and elevation. Other uncertainties are from unexplained factors related to drainage and peat condition, which need to be accounted for as well. This work shows the significance of decadal LULC change analysis to supplement InSAR measurement in tropical peatland subsidence monitoring programs.

Socio-economic drivers of rising CO2 emissions at the sectoral and sub-regional levels in the Yangtze River Economic Belt.

As the world's largest inland shipping channel, the Yangtze River Economic Belt (YREB) is strategic to China's sustainable development where policymakers are increasingly emphasizing not only this region's economic development but also its CO2 emission reduction targets. To achieve emission targets in the YREB region, it is essential to identify the driving forces of its CO2 emissions. However, existing studies are not very refined and only examine the overall effects of drivers on CO2 emission changes, while neglecting the sub-regional and sectoral level effects across China. More refined research, therefore, will provide better-targeted policies for emission reduction relevant to regional levels such as the YREB region. Towards this end, this paper integrates the methods of structural decomposition analysis and attribution analysis to demonstrate the driving forces, at both sub-regional and sectoral levels, for YREB's emission changes from 2002 to 2012. Our results reveal the following: (1) Jiangsu Province has always been the main regional source of increasing CO2 emissions in the YREB, accounting for more than 20% of total CO2 emission growth. (2) The Electricity and Heat Production and Supply sector is responsible for most of the increases in CO2 emissions, both in 2002-2007 (609.8 Mt, 54.8%) and 2007-2012 (287.6 Mt, 34%). (3) During the period of 2007-2012, changes in per capita final demand were the primary driving force for the increases in CO2 emissions, while changes in CO2 emission intensity were the largest driving force for decreasing CO2 emissions, respectively accounting for 179.9% and -119.4% of total emission changes in the YREB region. (4) Moreover, the effect of emission intensity mainly exists in the Electricity and Heat Production and Supply sector in the Jiangsu and Zhejiang provinces, accounting for respectively, 10% and 10.4% of the total effects across all economic sectors. Considering the diverse impacts of driving forces in different sub-regions and economic sectors, policymakers should apply more refined measures to utilize varying driving forces in different sub-regions and economic sectors towards sustainable development.

Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012.

The resilience of the phosphorus (P) cycling network is critical to ecosystem functioning and human activities. Although P cycling pathways have been previously mapped, a knowledge gap remains in evaluating the P network's ability to withstand shocks or disturbances. Applying principles of mass balance and ecological network analysis, we examine the network resilience of P cycling in China from 1600 to 2012. The results show that changes in network resilience have shifted from being driven by natural P flows for food production to being driven by industrial P flows for chemical fertilizer production. Urbanization has intensified the one-way journey of P, further deteriorating network resilience. Over 2000-2012, the network resilience of P cycling has decreased by 11% owing to dietary changes towards more animal-based foods. A trade-off between network resilience improvement and increasing food trade is also observed. These findings can support policy decisions for enhanced P cycling network resilience in China.

Redundancy, Diversity, and Modularity in Network Resilience: Applications for International Trade and Implications for Public Policy.

Sustainability is increasingly concerned with the complex interactions between nature and society, and we need to seek solutions towards the challenges that threaten humanity's collective wellbeing. Towards this end, it is critical to advance the application of research examining the dynamic interactions of the components of complex social-ecological systems and their emerging properties. A key research area is on advancing tools and strategies relevant to the evaluation and strengthening of resilience. Redundancy, diversity, and modularity are important characteristics of resilience with a high potential for application in various critical social-ecological systems. This paper provides a critical overview of the theoretical underpinnings of modularity and redundancy and their application in measuring resilience of trade networks with implications for public policy and institutional design.

Utilizing geospatial information to implement SDGs and monitor their Progress.

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.

Agricultural water policy reforms in China: a representative look at Zhangye City, Gansu Province, China.

Water resources are essential for agricultural production in the grain-producing region of China, and water shortage could significantly affect the production and international trade of agricultural products. China is placing effort in new policies to effectively respond to changes in water resources due to changes in land use/land cover as well as climatic variations. This research investigates the changes in land, water, and the awareness of farmer vis-à-vis the implementation of water-saving policies in Zhangye City, an experimental site for pilot programs of water resources management in China. This research indicates that the water saved through water-saving programs and changes in cropping structure (2.2 × 108 m3 a-1) is perhaps lower than the newly increased water withdrawal through corporate-led land reclamation (3.7 × 108 m3 a-1). Most critically, the groundwater withdrawal has increased. In addition, our survey suggests that local government is facing a dilemma of water conservation and agricultural development. Therefore, the enforcement of the ban on farmland reclamation and irrigation water quotas in our study area is revealed to be relatively loose. In this vein, the engagement of local stakeholders in water governance is essential for the future sustainable management of water resources.

Network structure impacts global commodity trade growth and resilience.

Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks' redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks.

Evaluating the evolution of the Heihe River basin using the ecological network analysis: Efficiency, resilience, and implications for water resource management policy.

One of the most critical challenges in the anthropocentric age is the sustainable management of the planet's increasingly strained water resources. In this avenue, there is a need to advance holistic approaches and objective tools which allow policy makers to better evaluate system-level properties and trade-offs of water resources. This research contributes to the expanding literature in this area by examining the changes to system-level network configurations of the middle reaches of the Heihe River basin from 2000 to 2009. Specifically, through the ecological network analysis (ENA) approach, this research examines changes to the system-level properties of efficiency, redundancy, and evaluates the trade-offs to the resiliency of ecosystem water services of the middle reaches of the Heihe River basin. Our results indicate that while the efficiency of the middle reaches has increased from 2000 to 2009 by 6% and 78% more water is released to the lower reaches, the redundancy of the system has also decreased by 6%. The lower level of redundancy, particularly due to the changes in the groundwater body levels, has critical long-term consequences for the resilience of the water ecosystem services of the middle reaches. In consideration of these holistic trade-offs, two hypothetical alternative scenarios, based on water recycling and saving strategies, are developed to improve the long-term health and resilience of the water system.

Epithelioid hemangioendothelioma of the oral cavity: a case report.

Epithelioid hemangioendothelioma is a rare vascular neoplasm which exhibits the potential for malignancy and recurrence as well as the ability to metastasize. Although numerous sites of involvement are possible, these tumors most commonly arise in soft tissues, lung, liver, bone, and lymph nodes. In this report, we describe a case of oral epithelioid hemangioendothelioma in a child. This tumor appeared as exophytic ulcerated painless masses in the maxillary and mandibular gingiva. Histologically, the tumor was composed of a proliferation of tumor cells arranged in nests, cords, and short strands. Epithelioid cells exhibited abundant eosinophilic cytoplasm with nuclear and cellular pleomorphism and intra-cytoplasmic vacuoles.