OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner. Despite remarkable progress in recent years, a lack of systematic understanding persists due to the diverse settings, complex implementation, and difficult reproducibility. Without standardization, comparisons can be unfair and insights inconclusive. To address this dilemma, we propose OpenSTL, a comprehensive benchmark for spatio-temporal predictive learning that categorizes prevalent approaches into recurrent-based and recurrent-free models. OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and weather forecasting. Based on our observations, we provide a detailed analysis of how model architecture and dataset properties affect spatio-temporal predictive learning performance. Surprisingly, we find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models. Thus, we further extend the common MetaFormers to boost recurrent-free spatial-temporal predictive learning. We open-source the code and models at https://github.com/chengtan9907/OpenSTL.Comment: Accepted by NeurIPS 2023. 33 pages, 17 figures, 19 tables. Under review. For more details, please refer to https://github.com/chengtan9907/OpenST

Abnormal expression of an ADAR2 alternative splicing variant in gliomas downregulates adenosine-to-inosine RNA editing

BACKGROUND: RNA editing is catalyzed by adenosine deaminases acting on RNA (ADARs). ADAR2 is the main enzyme responsible for recoding editing in humans. Adenosine-to-inosine (A-to-I) editing at the Q/R site is reported to be decreased in gliomas; however, the expression of ADAR2 mRNA was not greatly affected. METHODS: We determined ADAR2 mRNA expression in human glioblastoma cell lines and in normal human glial cells by real-time RT-PCR. We also determined ADAR2 mRNA expression in 44 glioma tissues and normal white matter. After identifying an alternative splicing variant (ASV) of ADAR2 in gliomas, we performed sequencing. We then classified glioblastomas based on the presence (+) or absence (–) of the ASV to determine the correlations between ASV + and malignant features of glioblastomas, such as invasion, peritumoral brain edema, and survival time. RESULTS: There were no significant differences in ADAR2 mRNA expression among human glioblastoma cell lines or in gliomas compared with normal white matter (all p > 0.05). The ASV, which contained a 47-nucleotide insertion in the ADAR2 mRNA transcript, was detected in the U251 and BT325 cell lines, and in some glioma tissues. The expression rate of ASV differed among gliomas of different grades. ASV + glioblastomas were more malignant than ASV – glioblastomas. CONCLUSIONS: ADAR2 is a family of enzymes in which ASVs result in differences in enzymatic activity. The ADAR2 ASV may be correlated with the invasiveness of gliomas. Identification of the mechanistic characterization of ADAR2 ASV may have future potential for individualized molecular targeted-therapy for glioma

Genomic analyses provide insights into peach local adaptation and responses to climate change

The environment has constantly shaped plant genomes, but the genetic bases underlying how plants adapt to environmental influences remain largely unknown. We constructed a high-density genomic variation map of 263 geographically representative peach landraces and wild relatives. A combination of whole-genome selection scans and genome-wide environmental association studies (GWEAS) was performed to reveal the genomic bases of peach adaptation to diverse climates. A total of 2092 selective sweeps that underlie local adaptation to both mild and extreme climates were identified, including 339 sweeps conferring genomic pattern of adaptation to high altitudes. Using genome-wide environmental association studies (GWEAS), a total of 2755 genomic loci strongly associated with 51 specific environmental variables were detected. The molecular mechanism underlying adaptive evolution of high drought, strong UVB, cold hardiness, sugar content, flesh color, and bloom date were revealed. Finally, based on 30 yr of observation, a candidate gene associated with bloom date advance, representing peach responses to global warming, was identified. Collectively, our study provides insights into molecular bases of how environments have shaped peach genomes by natural selection and adds candidate genes for future studies on evolutionary genetics, adaptation to climate changes, and breeding.info:eu-repo/semantics/publishedVersio

Circulation of reassortant influenza A(H7N9) viruses in poultry and humans, Guangdong Province, China, 2013.

Influenza A(H7N9) virus emerged in eastern China in February 2013 and continues to circulate in this region, but its ecology is poorly understood. In April 2013, the Guangdong Provincial Center for Disease Control and Prevention (CDC) implemented environmental and human syndromic surveillance for the virus. Environmental samples from poultry markets in 21 city CDCs (n=8,942) and respiratory samples from persons with influenza-like illness or pneumonia (n=32,342) were tested; viruses isolated from 6 environmental samples and 16 patients were sequenced. Sequence analysis showed co-circulation of 4 influenza A(H7N9) virus strains that evolved by reassortment with avian influenza A(H9N2) viruses circulating in this region. In addition, an increase in human cases starting in late 2013 coincided with an increase in influenza A H7 virus isolates detected by environmental surveillance. Co-circulation of multiple avian influenza viruses that can infect humans highlights the need for increased surveillance of poultry and potential environmental sources.This study was financially supported by 12th five-year-major-projects of China’s Ministry of Public Health. Grant No: 2012zx10004-213 and by the PREDICT Surveillance Animal Human Interface Project of GVF. Grant No: Gvf: 06-09-057-02.This is the accepted version. It'll be replaced with the final pdf when it's available

Metabolism of urban wastewater: Ecological network analysis for Guangdong Province, China

Wastewater discharge is a burden on environmentally sustainable development, especially in the water-deficient area. Existing Studies on wastewater discharge is not comprehensive for lacking analysis of mutual flow and necessary components. In this study, a wastewater metabolism input-output model is developed to achieve sustainable development through a novel perspective to depict the industrial wastewater flow among sectors. Since chemical oxygen demand and ammonia nitrogen are indicators for studying the degree of wastewater pollution, this paper also considers their wastewater to make the research synthetic and systematic. A case study of Guangdong Province, China, is conducted to further illustrate the potential benefits of the model in investigation of the sectors interactions. The results show that the wastewater discharge of Guangdong Province is considerable, with industrial wastewater, chemical oxygen demand wastewater and ammonia-nitrogen wastewater being 7.53 billion tons, 852 thousand tons and 69 thousand tons respectively. Some typical sectors have been distinguished based on ecological network analysis and input-output analysis for mitigating wastewater discharge, such as electronic equipment manufacture, chemical materials and paper manufacture, and tertiary industry. The implementation of the “Replace Subsidies with Rewards” policy is conducive to the discharge reduction of the system

Dynamic wastewater-induced research based on input-output analysis for Guangdong Province, China

Large amounts of wastewater discharge have emerged as a burden in the process of industrialization and urbanization. In this study, a dynamic wastewater-induced input-output model is developed to systematically analyze the related situation. The developed model is applied to Guangdong Province, China to analyze its prominent characteristics from 2002 to 2015. Combining input-output analysis, ecological network analysis and structural decomposition analysis, the developed model reveals issues of direct and indirect discharges, relationships among various discharges, and driving forces of wastewater discharges. It is uncovered that Primary Manufacturing and Advanced Manufacturing dominate the system because of significant temporal and spatial variations in wastewater discharge. In addition, Manufacturing of paper, computer and machinery and Services are the key industries responsible for large amounts of wastewater discharge and unhealthy source-discharge relationships. The largest wastewater discharge occurred in 2005 and indirect wastewater discharge is the main form. Furthermore, final demand is found to be the biggest driving force of wastewater discharge. Finally, a three-phase policy implementation system implemented in stages proposes solutions to wastewater problems

Inter-regional carbon flows embodied in electricity transmission: network simulation for energy-carbon nexus

Energy use and CO2 emissions are inextricably linked. Energy utilization leads to an increase in CO2 emissions, which will in turn limit the formulation of energy policies and stability of energy systems. A provincial-scale Energy-Carbon Nexus Model is established to shed insight into the complicated system interactions among provinces. Specifically, different power generation types are considered to quantify the inter-provincial transfers of CO2 embodied in electricity transmission through the Multiregional Input-Output Analysis. Ecological Network Analysis is used to describe the integral mutual relationships between provinces and distinguish the control intensity of each province from different CO2 flows directions. Five new Energy-carbon emission factors are first performed to provide a more accurate assessment of the province's emissions capacity from different perspectives. Based on the theoretical basis of energy-carbon nexus, the emission reduction simulations considering energy substitution policy can be conducted to forecast the changes of provincial responsibility under different interventions. Results show that some provinces (e.g., Beijing) depend heavily on the supply of other provinces because of their low self-sufficiency rate in electricity, while some provinces (e.g., Guangdong) have high self-sufficiency rate and still emit more CO2 to other provinces to promote their own development. The importance of East China to the system cannot be ignored, but it should also undertake more responsibility for reducing emissions. However, the pace of development in Shandong will slow down because it mainly relies on coal power generation to indirectly promote the development of other provinces. What's more, importing electricity to achieve emission reduction may result in a rebound in indirect emissions and have a negative impact on the region's use of its own energy resources. This paper offers a new way to reveal details of energy-carbon interrelations across provinces and the achievements could provide references for formulating CO2 reduction policies of China electricity trading

Network analysis of different types of food flows: Establishing the interaction between food flows and economic flows

Food security is performed as an important issue, which is directly related to human survival, social progress and environmental protection. The aim of this paper is to establish a holistic and new food network model that is capable of exploring the nature of food flows in response to the regulation of sectoral activities from a practical perspective. A case study for Guangdong Province, China is conducted to illustrate the influence of different food types on urban food system by combining Input Output Analysis and Ecological Network Analysis. In detail, the applicability of these methods is first extended to food element. The study on complex food system involving ecology, society, and the environment is the first time to use network analysis to quantify the urban metabolic processes. In addition, the Value intensity of flow (VIF) is first introduced to re-establish the relationship between food flows and economic flows. The results show that the indirect food flows have a huge impact on food system. Food processing (FO) and Accommodation and catering services (AC) are the most important sectors to promote and support the development of other sectors. The food type has great impacts on pulling force and the level of commercial value, while it does not affect the flows of commercial value. As a raw material in many industries, sugarcane affects the metabolic relationships between sector

Multi-Dimensional Hypothetical Fuzzy Risk Simulation model for Greenhouse Gas mitigation policy development

Changing climate is one of the most challenging environment issues worldwide. The objective of this paper is to develop a Multi-Dimensional Hypothetical Fuzzy Risk Simulation Model to facilitate the Greenhouse Gases mitigation policy development and multi-dimensional risk simulation. In detail, the comprehensive performances of various industries are evaluated and analyzed through Hypothetical Extraction Method. The preferences of decision-makers are considered through Analytic Hierarchy Process and Fuzzy Technique for Order Preference by Similarities to Ideal Solution method to develop the optimized Greenhouse Gases mitigation policies. The multi-dimensional risks of optimized Greenhouse Gases mitigation policies are simulated through RAS method. A detailed case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of the proposed model and support the Greenhouse Gases mitigation policy development. It is found that Electric power generation, transmission and distribution sector is the key industry in Saskatchewan. The government supports are suggested to be allocated to the Electric power generation, transmission and distribution sector, since it will benefit the province from environmental, economic, and urban metabolic perspectives

Dynamic analysis of industrial solid waste metabolism at aggregated and disaggregated levels

Greater efforts should be made for the prevention and treatment of industrial solid waste (ISW). This study models an integrated ISW metabolism framework to explore socio-economic factors of ISW production changes for a case study of Guangdong province, China during 2002–2015. In detail, index decomposition analysis is innovatively conducted to quantify the relative contribution of socio-economic factors to ISW production changes. Waste metabolism input-output analysis is used to reveal internal structure of the system. More specifically, based on a new four-way classification method, components' dependence on the system are determined by linkage analysis. Using environmental responsibility analysis, their environmental responsibilities are assessed. The results show that decreasing ISW generation intensity and further optimizing industrial structure is the only way to Guangdong's ISW reduction at an aggregated sector-level. At a disaggregated sector-level, mining (M) is a key sector and should focus on income-based ISW regulations. Energy and materials transformation (ET) roles as a direct producer and has strong linkages to other sectors. As the largest final consumer, the others (OS) sector should consider consuming less ISW-intensive commodities