Decoding anterior-posterior axis emergence among mouse, monkey, and human embryos.

Anterior-posterior axis formation regulated by the distal visceral endoderm (DVE) and anterior visceral endoderm (AVE) is essential for peri-implantation embryogenesis. However, the principles of the origin and specialization of DVE and AVE remain elusive. Here, with single-cell transcriptome analysis and pseudotime prediction, we show that DVE and AVE independently originate from the specialized primary endoderm in mouse blastocysts. Along distinct developmental paths, these two lineages, respectively, undergo four representative states with stage-specific transcriptional patterns around implantation. Further comparative analysis shows that AVE, but not DVE, is detected in human and non-human primate embryos, defining differences in polarity formation across species. Moreover, stem cell-assembled human blastoids lack DVE or AVE precursors, implying that additional induction of stem cells with DVE/AVE potential could promote the current embryo-like models and their post-implantation growth. Our work provides insight into understanding of embryonic polarity formation and early mammalian development.

Applications of machine learning in tumor-associated macrophages.

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM's traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment.

Purpose: Although the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans. Method: A DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared. Result: 20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum. Conclusion: In this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.

PURPOSE: Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly time-intensive online treatment planning process and the high dose gradient around the HRCTV. This study aims to apply a self-configured ensemble method for fast and reproducible auto-segmentation of OARs and HRCTVs in gynecological cancer. MATERIALS AND METHODS: We applied nnU-Net (no new U-Net), an automatically adapted deep convolutional neural network based on U-Net, to segment the bladder, rectum and HRCTV on CT images in gynecological cancer. In nnU-Net, three architectures, including 2D U-Net, 3D U-Net and 3D-Cascade U-Net, were trained and finally ensembled. 207 cases were randomly chosen for training, and 30 for testing. Quantitative evaluation used well-established image segmentation metrics, including dice similarity coefficient (DSC), 95% Hausdorff distance (HD95%), and average surface distance (ASD). Qualitative analysis of automated segmentation results was performed visually by two radiation oncologists. The dosimetric evaluation was performed by comparing the dose-volume parameters of both predicted segmentation and human contouring. RESULTS: nnU-Net obtained high qualitative and quantitative segmentation accuracy on the test dataset and performed better than previously reported methods in bladder and rectum segmentation. In quantitative evaluation, 3D-Cascade achieved the best performance in the bladder (DSC: 0.936 ± 0.051, HD95%: 3.503 ± 1.956, ASD: 0.944 ± 0.503), rectum (DSC: 0.831 ± 0.074, HD95%: 7.579 ± 5.857, ASD: 3.6 ± 3.485), and HRCTV (DSC: 0.836 ± 0.07, HD95%: 7.42 ± 5.023, ASD: 2.094 ± 1.311). According to the qualitative evaluation, over 76% of the test data set had no or minor visually detectable errors in segmentation. CONCLUSION: This work showed nnU-Net's superiority in segmenting OARs and HRCTV in gynecological brachytherapy cases in our center, among which 3D-Cascade shows the highest accuracy in segmentation across different applicators and patient anatomy.

BRN2 as a key gene drives the early primate telencephalon development.

Evolutionary mutations in primate-specific genes drove primate cortex expansion. However, whether conserved genes with previously unidentified functions also play a key role in primate brain expansion remains unknown. Here, we focus on BRN2 (POU3F2), a gene encoding a neural transcription factor commonly expressed in both primates and mice. Compared to the limited effects on mouse brain development, BRN2 biallelic knockout in cynomolgus monkeys (Macaca fascicularis) is lethal before midgestation. Histology analysis and single-cell transcriptome show that BRN2 deficiency decreases RGC expansion, induces precocious differentiation, and alters the trajectory of neurogenesis in the telencephalon. BRN2, serving as an upstream factor, controls specification and differentiation of ganglionic eminences. In addition, we identified the conserved function of BRN2 in cynomolgus monkeys to human RGCs. BRN2 may function by directly regulating SOX2 and STAT3 and maintaining HOPX. Our findings reveal a previously unknown mechanism that BRN2, a conserved gene, drives early primate telencephalon development by gaining novel mechanistic functions.

Evaluating China's regional energy and environmental efficiency by considering three internal parallel industries.

With the rapid development of China's economy, high energy consumption and high pollution emission have become serious problems. To solve these problems, many studies have been done to evaluate energy and environmental efficiency, as the results can provide valuable information to improve performance. However, the previous research mainly evaluates China's regional energy and environmental efficiency by considering each region's industry as a whole system, ignoring the internal structure. In reality, each region mainly includes three parallel types of industry: primary, secondary, and tertiary. Therefore, this paper provides a parallel data envelopment analysis (DEA) approach to evaluate China's regional energy and environment efficiency by considering these parallel industrial systems. The following findings can be obtained based on the empirical results: (1) the overall energy efficiency of China is low, and the inefficiency of the economic system is mainly sourced from the lower energy and environmental performance of the primary industry and the tertiary industry. (2) the introduction of the environmental variable (CO2) leads to the increase of some backward areas' efficiencies. (3) the energy efficiency of each provincial region is different, and most of them have their own inefficient industries. (4) the total factor productivity of China is declining, mainly because of the decline of technical efficiency.

Impacts of renewable electricity standard and Renewable Energy Certificates on renewable energy investments and carbon emissions.

Accelerating the development of renewable energy is seen as an effective way for achieving the goals of carbon peak and carbon neutrality. The polices of Renewable Electricity Standard (RES) and Renewable Energy Certificates (REC) play increasing and important roles in developing renewable energy. In this paper, we develop an analytical model to analyze the impacts of the interaction of RES and REC polices on the renewable energy investment levels of an electricity generation firm and the carbon emissions. Our analysis reveals several interesting insights. First, we find that the green tags price under REC policy has a non-monotonic effect on the renewable energy investment, which highly depends on the quota (i.e., the required percentage of renewable electricity consumption on total electricity consumption) under the RES policy. Specifically, when the quota in RES policy is set too high, an increase in the green tags price will increase renewable energy investment; otherwise it will reduce the electricity generation firm's incentive to invest in renewable energy. Second, we show that the green tags price also has a non-monotonic effect on the carbon emissions. Specifically, when the quota in RES policy is set small enough, an increase in the green tags price will decrease the carbon emission. However, when the quota in RES policy is high enough, an increase in the green tags price will increase the carbon emission.

Effects of different oxygen conditions on pollutants removal and the abundances of tetracycline resistance genes in activated sludge systems.

The individual and combined effects of tetracycline (TC) and divalent copper (Cu2+) on the performance of activated sludge systems and the abundances of tetracycline resistance genes (TRGs) in activated sludge, under both aerobic and anaerobic conditions, were studied. Activated sludge systems received TC (0.2 mg L-1) and Cu2+ (5 mg L-1) separately or jointly under either aerobic or anaerobic conditions. The addition of TC did not affect the performance of activated sludge systems and the addition of Cu2+ and mixed TC/Cu2+ inhibited biological phosphorus removal. The TC removal efficiencies in systems under aerobic and anaerobic conditions were 98.4%-99.7% and 96.8%-99.9%, respectively, and Cu2+ promoted TC removal in activated sludge systems. The TC degradation product was 4-epitetracycline (ETC) in activated sludge systems under both aerobic and anaerobic conditions. The total relative abundances of TRGs (tetA, tetC, tetE, tetM, tetO, tetW, tetX and tetB(P)) in activated sludge showed opposite development trends under the two oxygen conditions and aerobic condition was beneficial to the attenuation of high-risk TRGs. The results of this study might improve evaluation of the combined effects of antibiotics and heavy metals on wastewater biological treatment systems.

High-throughput mapping of a whole rhesus monkey brain at micrometer resolution.

Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm3 voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.

The efficacy of moxibustion and acupuncture therapy for ankylosing spondylitis: A protocol for an overview of systematic reviews and meta-analysis.

BACKGROUND: Ankylosing spondylitis is a complex and progressive autoimmune inflammatory disease with a worldwide prevalence ranging up to 0.9%. Several systematic reviews and meta-analyses of traditional Chinese medicine alternative therapies, such as acupuncture or moxibustion, have demonstrated the effectiveness of moxibustion and acupuncture in the treatment of ankylosing spondylitis. However, there is no relevant literature to comprehensively evaluate the evidence. The purpose of this overview is to synthesize and evaluate the reliability of evidence generated in the systematic review (SR) and meta-analysis of moxibustion and acupuncture as a primary or complementary therapy for patients with ankylosing spondylitis. METHODS: PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure, Chinese VIP Information, Wanfang Database, and Chinese Biomedical Literature Database were searched for systematic reviews and meta-analysis that review the efficacy of acupuncture or moxibustion as the primary treatment for patients with Ankylosing Spondylitis. The literature published before August 2020 will be selected. Additionally, the relevant SRs and meta-analyses that unpublished or ongoing will be searched in PROSPERO and INPLASY. The methodological guidelines for overviews will be used to review and extract data by 2 reviewers, and their will do it independently. Methodology quality will be analyzed by the assessment of multiple systematic reviews-2and the risk of bias by POBIS. For the included studies, we will adopt the following results as primary evaluation indicators: effective rate, visual analogue scale and bath AS disease activity index. Reviewers will assess the certainty of evidence by Grading of Recommendations Assessment, Development and Evaluation. RESULTS: The results will be published in a peer-reviewed journal. CONCLUSION: This overview will provide comprehensive evidence of moxibustion and acupuncture for patients with Ankylosing Spondylitis.

Little needle-scalpel for piriformis syndrome: A protocol for systematic review and meta-analysis.

BACKGROUND: Piriformis syndrome (PS) is a condition in which the sciatic nerve is compressed when passing through the inferior mouth of the piriformis muscle, mainly caused by pain in one hip and leg. In severe cases, patients may experience severe buttock and lower limb pain, discomfort, difficulty walking, and claudication. It is estimated that the annual incidence of low back pain and sciatica is about 40 million cases, and the annual incidence of piriformis syndrome is about 2.4 million cases. The aim of this systematic review is to assess the effectiveness and safety of Little needle-scalpel therapy for Piriformis syndrome. METHODS: Two reviewers will electronically search the following databases: the Cochrane Central Register of Controlled Trials (CENTRAL); PubMed; EMBASE; China National Knowledge Infrastructure (CNKI); Chinese Biomedical Literature Database (CBM); Chinese Scientific Journal Database (VIP database); and Wan-Fang Database from the inception, without restriction of publication status and languages. Additional searching including researches in progress, the reference lists, and the citation lists of identified publications. Study selection, data extraction, and assessment of study quality will be performed independently by 2 reviewers. If it is appropriate for a meta-analysis, RevMan 5.4 statistical software will be used; otherwise, a descriptive analysis will be conducted. Data will be synthesized by either the fixed-effects or random-effects model according to a heterogeneity test. The results will be presented as risk ratio (RR) with 95% confidence intervals (CIs) for dichotomous data and weight mean difference (WMD) or standard mean difference (SMD) 95% CIs for continuous data. RESULTS: This study will provide a comprehensive review of the available evidence for the treatment of Little needle-scalpel with piriformis syndrome. CONCLUSIONS: The conclusions of our study will provide an evidence to judge whether Little needle-scalpel is an effective and safe intervention for patients with piriformis syndrome. ETHICS AND DISSEMINATION: This systematic review will be disseminated in a peer-reviewed journal or presented at relevant conferences. It is not necessary for a formal ethical approval because the data are not individualized. TRIAL REGISTRATION NUMBER: INPLASY2020110092.

Impacts of the carbon emission trading system on China's carbon emission peak: a new data-driven approach.

Over the past four decades, China's extensive economic growth mode has led to substantial greenhouse gas emissions, and China has become the world's largest emitter since 2009. In order to alleviate the dual pressures from international climate negotiations and domestic environmental degradation, the Chinese government has pronounced it will reach its emission peak before 2030. However, through analyzing 12 scenarios, we found that it will be very difficult to meet this ambitious goal under the current widely used policies. With the trial implementation of China's carbon emission trading system (ETS), concerns arise over whether national ETS can accelerate the carbon peak process. In this paper, we propose a new proactive data envelopment analysis approach to investigate the impacts of national carbon ETS on carbon peak. Several important results are obtained. For example, we find that carbon ETS has a significant accelerating effect on carbon peak, which effect will advance the carbon peak by one to 2 years, and the corresponding peak values are reduced by 2.71-3 Gt. In addition, the setting of carbon price in the current Chinese pilot carbon market is found to be overly conservative. Last, our estimation on the carbon trading volume indicates that the ETS lacks vitality as the annual average carbon trading volume only represents approximately 4.3% of the total average carbon emissions. Based on these findings, several policy implications are suggested regarding the means by which China can more smoothly peak its carbon emissions before 2030 and implement national carbon ETS.

Inhibition of coronavirus infection by a synthetic STING agonist in primary human airway system.

The newly emerged severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) coronavirus initiated a pneumonia outbreak (COVID-19) that rapidly spread worldwide and quickly became a public health emergency of international concern; However to date, except Remdesivir, there are no clinically approved specific or effective medicines to prevent or treat COVID-19. Therefore, the development of novel treatments against coronavirus infections caused by the current SARS-CoV-2 virus, as well as other highly pathogenic human coronaviruses, represents an urgent unmet need. Stimulator of interferon genes (STING) plays a central role in host defense mechanisms against microbial infections. STING activation leads to the induction of both type I interferon and autophagy responses, which elicit strong inhibitory effect against the infections caused by a broad range of microbial pathogens. However, whether STING activation can impact infections from SARS-CoV-2 or other coronaviruses remains largely unknown. In this study, we investigated the anti-coronavirus activity triggered by STING activation. We discovered that dimeric amidobenzimidazole (diABZI), a synthetic small molecule STING receptor agonist, showed potent anti-coronavirus activity against both the common cold human coronavirus 229E (HCoV-229E) and SARS-CoV-2 in cell culture systems. In addition, we demonstrated that the antiviral activity of diABZI was dependent on the interferon pathway in HCoV-229E infected normal human fibroblast lung cells (MRC-5) and reconstituted primary human airway air-liquid interface (ALI) cultures. Furthermore, low-dose of diABZI treatment at 0.1 µM effectively reduced the SARS-CoV-2 viral load at the epithelial apical surface and prevented epithelial damage in the reconstituted primary human bronchial airway epithelial ALI system. Our findings have thus revealed the therapeutic potential of STING agonists, such as diABZI, as treatments for SARS-CoV-2 and other human coronavirus infections.

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain.

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg-Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

A synthetic STING agonist inhibits the replication of human parainfluenza virus 3 and rhinovirus 16 through distinct mechanisms.

Stimulator of interferon genes (STING), as a signaling hub in innate immunity, plays a central role for the effective initiation of host defense mechanisms against microbial infections. Upon binding of its ligand cyclic dinucleotides (CDNs) produced by the cyclic GMP-AMP synthase (cGAS) or invading bacteria, STING is activated, leading to the induction of both type I interferon responses and autophagy, which are critical for the control of certain microbial infections. RNA viruses, such as Parainfluenza virus (PIV) and Rhinovirus (HRV), are among the leading causes of respiratory infections that affect human health without effective treatments. Activation of STING pathway may provide a new therapeutic approach fighting against these viruses. However, the role of STING in the control of RNA virus infection remains largely unexplored. In this study, using dimeric amidobenzimidazole (diABZI), a newly discovered synthetic small molecule STING receptor agonist with much higher potency than CDNs, we found that activation of STING elicits potent antiviral effects against parainfluenza virus type 3 (PIV3) and human rhinovirus 16 (HRV16), two representative respiratory viral pathogens. Notably, while anti-PIV3 activity was depend on the induction of type I interferon responses through TANK-binding kinase 1 (TBK1), anti-HRV16 activity required the induction of autophagy-related gene 5 (ATG5)-dependent autophagy, indicating that two distinct antiviral mechanisms are engaged upon STING activation. Antiviral activity and individual specific pathway was further confirmed in infected primary bronchial epithelial cells. Our findings thus demonstrate the distinct antiviral mechanisms triggered by STING agonist and uncover the potential of therapeutic effect against different viruses.

Dynamic wetting of solid-liquid-liquid system by molecular kinetic theory.

HYPOTHESIS: The mechanisms of dynamic wetting of solid-liquid-liquid (SLL) system, especially the viscosity effects of two liquids, can be investigated by the molecular kinetic theory (MKT). METHODS: The molecular kinetic theory combined with published data was used to study the roles of a fluid viscosity and a solid surface in dynamic wetting. FINDINGS: First, the MKT on dynamic wetting was introduced and its limitation was analyzed. Second, a viscosity effect and a solid surface effect were considered. The viscosity effect was divided into three parts for the first time, including two pure liquid zones and a mixing zone. Third, a coefficient activation free energy model was proposed, considering the effects of mixing liquids and a solid surface. Finally, the key parameters in the MKT and the application and validation of the coefficient activation free energy model were discussed in detail. This model can explain the energy dissipation in a vicinity of a three-phase contact-line successfully in a SLL wetting system. This work sheds light on the physical mechanisms of fluid and solid surface properties on the dynamic wetting in a SLL system.

Analysis of resource allocation and environmental performance in China's three major urban agglomerations.

This study focuses on the analysis of optimal energy allocation and environmental performance of China's three major urban agglomerations. Specifically, this paper first uses a fixed-input DEA model to obtain the optimal allocation of energy input. Then, an evaluation model based on the optimal allocation of energy input is proposed to evaluate environmental performance. Finally, these models are applied to the empirical analysis of the three major urban agglomerations in China. This article mainly draws the following conclusions. First, energy is wasted in most cities in the Beijing-Tianjin-Hebei region. Second, from 2012 to 2016, the environmental performance of each urban agglomeration showed a downward trend and then an upward trend. Third, the overall environmental performance of the three major urban agglomerations is not high. Some specific regions, such as Handan, Anyang, Xingtai, and Baoding, behave not well in protecting the environment.

Performance evaluation and enrollment quota allocation for higher education institutions in China.

Higher education has always played an important role in promoting economic and social progress. With the rapid development of economy, China's higher education has entered a new stage and made a series of extraordinary achievements. However, the uneven development of higher education has become a critical factor restricting China's overall progress. In this paper, we propose Data Envelopment Analysis (DEA) approach to evaluate the performance of higher education resource utilization from the perspective of provincial level. In addition, we assess productivity changes of each province with the help of Malmquist Productivity Index (MPI). Further, by considering the enrollment quota as a special kind of resource, we construct a more reasonable allocation scheme, while keeping the total number of newly added enrollment quota unchanged in the next year. Finally, according to the results, some suggestions are put forward to help the Chinese government better solve the unbalanced development of higher education among provinces.

Modulation of Wnt and Activin/Nodal supports efficient derivation, cloning and suspension expansion of human pluripotent stem cells.

Various culture systems have been used to derive and maintain human pluripotent stem cells (hPSCs), but they are inefficient in sustaining cloning and suspension expansion of hPSCs. Through systematically modulating Wnt and Activin/Nodal signaling, we developed a defined medium (termed AIC), which enables efficient cloning and long-term expansion of hPSCs (AIC-hPSCs) through single-cell passage on feeders, matrix or in suspension (25-fold expansion in 4 days) and maintains genomic stability of hPSCs over extensive expansion. Moreover, the AIC medium supports efficient derivation of hPSCs from blastocysts or somatic cells under feeder-free conditions. Compared to conventional hPSCs, AIC-hPSCs have similar gene expression profiles but down-regulated differentiation genes and display higher metabolic activity. Additionally, the AIC medium shows a good compatibility for different hPSC lines under various culture conditions. Our study provides a robust culture system for derivation, cloning and suspension expansion of high-quality hPSCs that benefits GMP production and processing of therapeutic hPSC products.

Measuring performance of road transportation industry in China in terms of integrated environmental efficiency in view of Streaming Data.

Road transportation industry not only promotes economic development, but also brings many environmental issues in China. It is necessary to evaluate the integrated environmental efficiency of road transportation industry, including carbon dioxide emissions, traffic accident and traffic noise which is a Steaming Data. In this study, a parallel DEA model with sub-system preference is proposed to measuring the integrated environmental efficiency of road transportation industry in 2013-2017 considering various undesirable outputs. Then, the convergence analysis approach is employed to investigate the regional differences in terms of efficiency of road transportation. The empirical results show that the overall integrated environmental efficiency underperformed nationwide, and the passenger transportation sub-system outperformed freight transportation when the latter is restricted in some hours. From a regional perspective, the efficiency performances show downward trends in areas eastern, central and western. The passenger transportation sub-system outperforms in eastern area and the freight transportation outperforms in central area. Furthermore, the convergence analysis results indicate that the integrated environmental efficiency in eastern and western areas not only converged to their own stable levels but also that the differences in the integrated environmental efficiency between eastern and western areas are condensing. The contributions of this study lie in not only proposing a parallel DEA model with sub-system preference and considering the streaming data, but also providing suggestions to improving the road transportation industry in terms of integrated environmental efficiency. Finally, the research limitations and further research directions were conducted.