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Black carbon (BC) is considered the second largest anthropogenic climate forcer, but the radiative effects of BC are highly correlated with its combustion sources. On-road vehicles are an important source of anthropogenic BC. However, there are major uncertainties in the estimates of the BC emissions from on-road light-duty passenger vehicles (LDPVs), and results obtained with the portable emissions measurement system (PEMS) method are particularly lacking. We developed a PEMS platform and evaluated the on-road BC emissions from ten in-use LDPVs. We demonstrated that the BC emission factors (EFs) of gasoline direction injection (GDI) engine vehicles range from 1.10 to 1.56 mg·km−1, which are higher than the EFs of port fuel injection (PFI) engine vehicles (0.10–0.17 mg·km−1) by a factor of 11. The BC emissions during the cold-start phase contributed 2%–33% to the total emissions. A strong correlation (R2 = 0.70) was observed between the relative BC EFs and average vehicle speed, indicating that traffic congestion alleviation could effectively mitigate BC emissions. Moreover, BC and particle number (PN) emissions were linearly correlated (R2 = 0.90), and compared to PFI engine vehicles, the instantaneous PN-to-BC emission rates of GDI engine vehicles were less sensitive to vehicle-specific power-to-velocity (VSPV) increase in all speed ranges.

BACKGROUND: We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery. METHODS: Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley’s additive explanations. RESULTS: A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley’s additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction. CONCLUSIONS: Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.

Fusobacterium nucleatum infection plays vital roles in colorectal cancer (CRC) progression. Overexpression of microRNA-4717-3p (miR-4717) was reported to be upregulated in F. nucleatum positive CRC tissues, however, the underlying mechanism is unknown. In this study, we found that miR-4717 promoted CRC cell proliferation in vitro and growth of CRC in vivo following F. nucleatum infection. MicroRNA-4717 suppressed the expression of mitogen-activated protein kinase kinase 4 (MAP2K4), a tumor suppressor, by directly targeting its 3'-UTR. Furthermore, we confirmed that methyltransferase-like 3 (METTL3)-dependent m

Perivalvular leakage (PVL) after valve replacement is an awkward complication, and is liable to recur if re-replacement of a prosthetic valve is attempted. A 58-year-old male patient received initial mitral valve replacement 17 years earlier and recently developed PVL in mitral position. In this case we attempted an alternative way to repair mitral PVL through a mini-thoracotomy with thoracoscopic assistance without valve re-replacement surgery. The patient recovered uneventfully and was discharged on the day 7 after operation. Echocardiography revealed no regurgitation or new PVL developing during the 3 months follow-up.

Asia Pacific (AP) is the largest regional vehicle market and accounted for 48% of global sales in 2019. Air quality is a pressing issue in many AP countries and together with increased vehicle sales has led to intense scrutiny of vehicle emissions. The heterogeneity of socio-economic features and transportation patterns in AP countries has resulted in different emission levels and control policies. We present an assessment of the historical and future emissions of on-road transportation and strategies to tackle emission challenges. First, we collected historical country-level population, economic development, vehicle ownership, and transportation policy data from 1900 to 2020, and forecast future development of on-road transportation activity (both passenger and freight) based on its historical relationship with socio-economic development through 2050. We considered major countries (China, India, Japan, South Korea, Australia) individually and other AP countries as a group. Second, we generated a series of emission control scenarios with various stringency levels after a comprehensive review of vehicle control measures implemented in AP countries. The control packages included transportation mode shifts, pollutant emission standards, fuel consumption standards, fuel and powertrain diversification, improvement in fuel quality, and economic and transportation policies. Localized emission factors for greenhouse gases (GHGs) and criteria air pollutants (carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM)) were collected and estimated in line with the emission control measures. Third, we estimated historical and future emissions of AP on-road transportation from 1900 to 2050. The results showed that major air pollutants (NOx, CO, and PM2.5) from on-road vehicles peaked in 2000–2010 and are now declining despite increasing vehicle population. Control of GHGs is more challenging than for criteria air pollutants. In our reference scenario where existing policies and emission standards are implemented and new technologies are adopted according to national plans, road transportation GHG emissions in AP peak in approximately 2040.

Highlights • BAI score of respondents here is higher than those in previous studies with Chinese • Quarantined people presented the highest BAI score and incidence of anxiety • People in high epidemic area showed higher BAI score and incidence of anxiety • All factors impacted respondents’ anxiety level significantly, except gender, Background COVID-19 outbreak happened last December in China and is still continuing. Here, we reported effects of COVID-19 outbreak on the mood of general public and ascertained impacts of psychosocial factors on the plague-related emotional measures. Methods During Feb. 4-6, 2020, a self-reported questionnaire Beck Anxiety Inventory (BAI) was disseminated to general public via Wechat, along with a sociodemographic information sheet. BAI score and incidences of moderate and severe anxiety in subgroups of respondents were compared. Multiple linear and logistic regressions were done for correlation analysis and to identify factors predictive of anxiety. Results Averaged BAI score of all respondents is higher than those of general public in two previous studies. The people quarantined for probable COVID-19 infection presented higher BAI score and incidences of moderate and severe anxiety relative to non-quarantined respondents. People in high epidemic area showed higher BAI score and incidences of moderate and severe anxiety compared to those in low epidemic area. Significant associations existed between anxiety level of the respondents and each of the investigated factors, except for gender. Quarantine was the predictor with a highest OR, followed by divorced/widow. The other factors showed smaller but significant effects on the anxiety level of respondents. Limitations This cross-sectional study was unable to track the emotional changes in the respondents over time. It had a relatively small sample and involved some of emotional measures only. Conclusion These data are of help in planning psychological interventions for the different subpopulations in general public during and after COVID-19 outbreak.

The development of mobile Internet, smartphones, and location-based services has enabled ridesourcing, which pools vehicles and drivers to provide on-demand travel services. As an alternative transportation option, ridesourcing has significant impacts on urban travel. However, the unique mobility pattern of ridesourcing and its impact on vehicle electrification have not been well studied. To address this gap, this paper presents a comparative, big-data-driven framework to characterize the ridesourcing mobility pattern, and evaluate the acceptance potential of electric vehicles for ridesourcing in comparison with other types of vehicle use. Multi-temporal resolution ridesourcing trips are extracted from raw GPS trajectories. The patterns of three urban travel (household, ridesourcing, and taxis) are extracted from GPS trajectories in Beijing, and compared. The electrification potentials of these types of travel under different charging levels are then evaluated. The results demonstrate that mobility patterns of household, ridesourcing, and taxi drivers are similar when a single trip is considered but differ significantly when total vehicle travel is considered. We show that potential acceptance of electric vehicles decreases significantly from household to ridesourcing and taxi vehicle use. These findings provide useful insights into of the role vehicle electrification can play in sustainability of urban personal transportation across a range of drivers.

Energy security and climate change are critical concerns in the present era. The booming of the vehicle population has worsened the environment and has caused severe air pollution problems, especially in urban areas. The utilization of hydrogen-enriched compressed natural gas in internal combustion engines shows abundant prospects for improved performance and reduced on-road emissions of greenhouse gases and air pollutants. This study aims to provide an insight to well-to-wheels environmental implications of the 20%HCNG fuel mixture in terms of total energy use, and greenhouse gas emissions per megajoules (MJ) of thermal energy output. The well-to-tank (WTT) impacts were evaluated using GREET 1 (2017). GREET 1 is a fuel cycle modeling tool developed by ‘Argonne national laboratory.’ GREET® is extensively used by researchers worldwide to analytically simulate energy use and emission output of various vehicle and fuel combinations. This study uses 12 prospective pathways of gaseous hydrogen production for analysis purposes. In the tank-to-wheels (TTW) phase, the 20%HCNG@EEV reduces the brake specific energy consumption (BSEC) by approximately 5%, and also decreases GHG emissions by 14% compared with 0%HCNG@EURO3. For simplicity, EURO5 is entitled as ‘EEV’ and has been excluded in most of the discussion, only highlighting ‘EEV,’ which abbreviates as ‘Enhanced Environmentally friendly Vehicles.’ For the entire well-to-wheels phase, this research work shows that all of the 20%HCNG@EEV pathways have lower total energy use and GHG emissions than 0%HCNG@EURO3 except the two pathways, such as grid electricity-to-hydrogen (without CO2 sequestration) and coal gasification-to-hydrogen (without CO2 sequestration). The WTW total energy and GHG emissions reduced by approximately 14% and 13%, respectively, with 20%HCNG@EEV based on the coke oven gas pathway compared with 0%HCNG@EURO3. It is essential to note that the use of cleaner feedstock for hydrogen, such as power-to-gas (P2G), biomass, coke oven gas (by-product), and natural gas shows tremendous prospects for realizing and practicing sustainable ‘hydrogen economy’ in China. Further technological advancement and reduction in total costs of HCNG utilization in powertrains will increase the number of HCNG vehicles decreasing the burden of air pollution, climate change, and energy crisis threats.

Electric vehicles (EVs) play a crucial role in addressing climate change and urban air quality concerns. China has emerged as the global largest EV market with 1.2 million EVs sold in 2018. This study established a novel life cycle energy use and emission inventory collecting up-to-date data including the electricity generation mix, emission controls in the power and industrial sectors, and the energy use in the fuel transport to estimate the well-to-wheels (WTW) greenhouse gas (GHG), and air pollutant emissions for battery electric vehicles (BEVs) and gasoline passenger vehicles in China. The results show that an average BEV has 35% lower WTW GHG emissions than an average gasoline car. BEVs reduce volatile organic compounds (VOCs) and nitrogen oxides (NOX) emissions by 98% and 34%, respectively, but have comparable or slightly higher primary fine particulate matter (PM2.5) and sulfur dioxide (SO2) emissions. Compact and small-size vehicles generally have lower GHG and air pollutant emissions than mid- and large-size vehicles. Class A vehicles contribute the most in the absolute amount of GHG and air pollutant emissions and therefore have the biggest potential for emission reduction. Our results suggest that global policymakers should continue to promote the transition to clean power sources, emission control, and fuel economy regulations, which are critical to enhancing emission mitigation benefits of BEVs. We also suggest EV development strategies should be formulated targeting vehicle class with the biggest emission mitigation potentials.

China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development. Wide adoption of electric vehicles can contribute to mitigate climate change and air pollution. Here the authors develop various fleet electrification scenarios in China to evaluate the associated air quality and health impacts to inform sound policies.

The aim of this study was to explore the difference in activated partial thromboplastin time (APTT) levels in patients with tuberculous and non-tuberculous pleural effusion (TPE and non-TPE) and its possible mechanism to provide a new direction for the diagnosis of pleural effusion (PE).A total of 61 patients diagnosed with tuberculous pleurisy with pleural effusion at Shunde Hospital of Southern Medical University from July 2013 to September 2020 were selected as the observation group (tuberculosis group). Another 89 patients (45 with malignant pleural effusion (MPE) and 44 with parapneumonic pleural effusion (PPE) composed the control group. The adenosine deaminase (ADA) level in pleural fluid and plasma APTT level were measured in the two groups.The levels of APTT and ADA in the TPE group were significantly higher than the control group, and were 40.03 (37.00, 42.60) (s) and 55.00 (47.00, 69.25) (U/L) for TPE, 29.50 (25.45, 34.20) (s) and 11.90 (9.15, 19.05) (U/L) for malignant pleural effusion (MPE) and 31.35 (27.43, 35.76) (s) and 15.15 (7.40, 35.00) (U/L) for parapneumonic pleural effusion (PPE), respectively.The level of plasma APTT has certain significance in differentiating tuberculous pleural effusion from nontuberculous pleural effusion.

In order to solve the problems of low recognition accuracy for motor imagery EEG signals, this paper presents a feature extraction and classification algorithm based on decision tree and CSP-SVM. Firstly, we select the fixed frequency of signals ranging from 8 to 30 Hz. Secondly, multiple spatial filters are constructed by using the one versus the rest common spatial pattern (OVR-CSP) and extract the feature vectors. Support vector machine (SVM) is employed to classify the feature vectors so that the best spatial filter is selected. We build the first branch of decision tree with the spatial filter selected and SVM. Then, OVR-CSP and SVM are used to build the branches of the decision tree repeatedly. Finally, 2005 BCI competition IIIa data set is used to validate the effect of the proposed algorithm. The results show that the highest accuracy of the proposed algorithm can reach 94.27% which proves the effectiveness of the algorithm.

Aim: This research aimed to elucidate the prognosis values of prognostic nutritional index (PNI) and systemic immune-inflammation index (SII) and clinical characteristics in NSCLC patients with brain metastases (BM) underwent radiotherapy. Materials & methods: Cut-off points of hematological indicators were determined by receiver operating characteristic curve. Overall survival was evaluated by Kaplan–Meier method and Cox proportional hazards model. Results: We retrospectively analyzed 214 patients from January 2009 to December 2018. The result demonstrated the independent prognostic values of PNI (hazard ratio: 0.600; p = 0.004) and SII (hazard ratio: 1.486; p = 0.019). The remaining clinicopathologic factors, including brain radiotherapy modality, smoking history, numbers of brain metastases, intracranial symptoms and Radiation Therapy Oncology Group – recursive partitioning analysis, were independently related to survival (p

Increased E-commerce and demand for contactless delivery during the COVID-19 pandemic have fueled interest in robotic package delivery. We evaluate life cycle greenhouse gas (GHG) emissions for automated suburban ground delivery systems consisting of a vehicle (last-mile) and a robot (final-50-feet). Small and large cargo vans (125 and 350 cubic feet; V125 and V350) with an internal combustion engine (ICEV) and battery electric (BEV) powertrains were assessed for three delivery scenarios: (i) conventional, human-driven vehicle with human delivery; (ii) partially automated, human-driven vehicle with robot delivery; and (iii) fully automated, connected automated vehicle (CAV) with robot delivery. The robot's contribution to life cycle GHG emissions is small (2-6%). Compared to the conventional scenario, full automation results in similar GHG emissions for the V350-ICEV but 10% higher for the V125-BEV. Conventional delivery with a V125-BEV provides the lowest GHG emissions, 167 g CO2e/package, while partially automated delivery with a V350-ICEV generates the most at 486 g CO2e/package. Fuel economy and delivery density are key parameters, and electrification of the vehicle and carbon intensity of the electricity have a large impact. CAV power requirements and efficiency benefits largely offset each other, and automation has a moderate impact on life cycle GHG emissions.

Both Fusobacterium nucleatum (F. nucleatum) and long non-coding RNA (lncRNA) EVADR are associated with colorectal cancer (CRC), but their relationship with CRC metastasis and the mechanisms by which EVADR promotes CRC metastasis are poorly understood. Here, we report that F. nucleatum promotes colorectal cancer cell metastasis to the liver and lung and that it can be detected in CRC-metastasis colonization in mouse models. Furthermore, F. nucleatum upregulates the expression of EVADR, which can increase the metastatic ability of CRC cells in vivo and in vitro. Mechanistically, elevated EVADR serves as a modular scaffold for the Y-box binding protein 1 (YBX1) to directly enhance the translation of epithelial-mesenchymal transition (EMT)-related factors, such as Snail, Slug, and Zeb1. These findings suggest that EVADR induced by F. nucleatum promotes colorectal cancer metastasis through YBX1-dependent translation. The EVADR-YBX1 axis may be useful for the prevention and treatment of patients with F. nucleatum-associated CRC metastasis.

Macrophages are dominant innate immune cells. They demonstrate remarkable heterogeneity and plasticity that are essential for homeostasis and host defense. The heterogeneity of tissue macrophages is shaped by the ontogeny, tissue factors, and environmental signals, a pattern in a tissue-associated latitudinal manner. At the same time, macrophages have long been considered as mainly plastic cells. These cells respond to stimulation quickly and in a stimulus-specific way by utilizing a longitudinal cascaded activation, including coordination of signal transducer, epigenetic elements, and transcription factors, conclusively determine the macrophage phenotypes and functions. With the development of cutting-edge technologies, such as fate-mapping, single-cell transcriptomics, ipsc platform, nanotherapeutic materials, etc., our understanding of macrophage biology and the roles in the pathogenesis of diseases is much advanced. This review summarizes recent progress on the latitudinal and longitudinal regulation of tissue macrophages in inflammatory diseases. The latitudinal regulation covers the tissue macrophage origins, tissue factors, and environmental signals, reflecting the macrophage heterogeneity. The longitudinal regulation focuses on how multiple factors shape the phenotypes and functions of macrophage subsets to gain plasticity in inflammatory diseases (i.e., inflammatory bowel disease). In addition, how to target macrophages as a potential therapeutic approach and cutting edge-technologies for tissue macrophage study are also discussed in this review.

The inadequate representativeness of driving cycles used by regulatory lab tests is one significant factor leading to the large discrepancy between real-world fuel consumption and type-approval levels. Onboard measurement devices have been used in previous researches to collect vehicle activity data but the amount of data is sometimes limited. With second-by-second GPS trajectory data of 459 private passenger cars, covering over 17,000 sampling days, this study used big-data mining techniques to study the variations in real-world driving cycles. A Markov chain method was developed to generate typical driving cycles that have representative features of real-world driving. As a case study, two typical cycles, Off-peak cycle, and Peak cycle are constructed from six sub-cycles representing different road types and travel periods. The travel dynamics indicated the New European Driving Cycle (NEDC) would be too mild to represent real-world driving in China. The simulation results of vehicle fuel consumption showed that different driving cycles could lead to different lab-to-road gaps when comparing with NEDC type-approval levels. For example, the fuel consumption (median value) of Off-peak cycle and Peak cycle were higher than the NEDC type-approval level by 29.3% and 37.5%, respectively. This study highlights the importance of addressing real-world features in improving future fuel economy regulations. The practical approach to generate representative driving cycles ensured by massive travel profiles can be employed reliably for fuel consumption and exhaust emission assessment in the future.

Preheating technology is widely used to improve the emission or efficiency of combustors, such as diesel engines and gasifiers operating at high pressures. Soot is an unwanted by-product, but its formation is unavoidable in high-pressure diffusion combustion. In this study, the effect of preheating temperature on polycyclic aromatic hydrocarbon (PAH) and soot formation in methane/air co-flow flames was comprehensively investigated in the pressure range of 1–5 bar. The temperature of inlet gas ranges from 295 K to 573 K. The soot, PAH, and OH* concentrations were obtained using planar laser-induced incandescence, planar laser induced fluorescence, and chemiluminescence techniques, respectively. The experimental results reveal that soot and PAH formation is greatly enhanced at higher pressure or with a higher preheating temperature of inlet gas. At a fixed preheating temperature, the peak/integrated soot volume fraction follows a power law with pressure. As pressure increases, the enhancement of soot formation by preheating temperature is suppressed. As the preheating temperature is raised from 295 K to 573 K, the integrated soot volume fraction is increased by 33.7 times at 1.5 bar, but the difference narrows to 2.3 times at 5 bar. OH* signal increases with preheating temperature at 1 bar, but the difference becomes indistinguishable at higher pressure. Further, the experimental results were utilized to evaluate the soot modeling, PAH and soot formation under experimental conditions are examined using four different kinetic mechanisms. While the soot trend along different pressure and preheating temperature is qualitatively captured, the quantitative predictions vary depending on the mechanisms. Specifically, KAUST and Narayanaswamy-Blanquart-Pitsch (NBP) mechanisms overpredict the soot volume fraction while DRL and Appel-Bockhorn-Frenklach (ABF) mechanisms underpredicts the soot volume fraction. In terms of the PAH spatial distribution, only DRL and ABF mechanisms show the ability to capture the experimental observations, that is the peak PAH appears in the flame centerline. The reaction pathway analysis indicates both fuel-pyrolysis chemistry and PAH growth chemistry should be accounted for the discrepancy.

Additional file 1: Figure S1. The confirmation of expected amplicon size of the primer pairs. The primer pairs were tested before qRT-PCR, using cDNA by standard PCR reaction with SimpliAmp Thermal Cycler (Thermofisher). The amplicons sizes were verified in 2% agarose gel electrophoresis. Figure S2. Melt curves of the Bip, CHOP and actin genes. Figure S3. Amplification efficiencies of the qRT-PCR primers designed in the study. Figure S4. The primary images for the cropped blots in Figs. 3c, 4d and 5h. Figure S5. The primary images for the cropped gels in Figs. 4e and 5i. Figure S6. The primary confocal data for the cropped images in Figs. 4b and 5d.

Background Burkholderia pseudomallei, a facultative intracellular bacterium, is the aetiological agent of melioidosis that is responsible for up to 40% sepsis-related mortality in epidemic areas. However, no effective vaccine is available currently, and the drug resistance is also a major problem in the treatment of melioidosis. Therefore, finding new clinical treatment strategies in melioidosis is extremely urgent. Results We demonstrated that tauroursodeoxycholic acid (TUDCA), a clinically available endoplasmic reticulum (ER) stress inhibitor, can promote B. pseudomallei clearance both in vivo and in vitro. In this study, we investigated the effects of TUDCA on the survival of melioidosis mice, and found that treatment with TUDCA significantly decreased intracellular survival of B. pseudomallei. Mechanistically, we found that B. pseudomallei induced apoptosis and activated IRE1 and PERK signaling ways of ER stress in RAW264.7 macrophages. TUDCA treatment could reduce B. pseudomallei-induced ER stress in vitro, and TUDCA is protective in vivo. Conclusion Taken together, our study has demonstrated that B. pseudomallei infection results in ER stress-induced apoptosis, and TUDCA enhances the clearance of B. pseudomallei by inhibiting ER stress-induced apoptosis both in vivo and in vitro, suggesting that TUDCA could be used as a potentially alternative treatment for melioidosis.