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As a kind of renewable and high oxygen content fuel, polyoxymethylene dimethyl ether (PODE) can be added in diesel to realize energy saving and emissions reduction. To evaluate the combustion and emission characteristics of a diesel engine fueled with diesel and diesel/PODE mixtures, exhaust gas recirculation (EGR) and main-pilot injection strategies with various injection timings were applied. PODE was blended with diesel by volume to form mixtures which were marked as D100 (pure diesel), D90P10 (90% diesel + 10% PODE), and D80P20 (80% diesel + 20% PODE). The results showed that the ignition delay (ID) and combustion duration (CD) of D80P20 were the shortest because of the highest cetane number (CN) and high oxygen content of PODE, indicating more concentrated heat release. At low and medium loads, D80P20 achieved the highest peak heat release ratio (PHRR) and peak combustion temperature (PCT) among the three fuels, and it was 14.3% and 3.6% higher than those of D100. PODE blending with diesel can significantly reduce particulate matter (PM) and D80P20 has the lowest PM emissions at all loads. Compared with D100, both PM and nitrogen oxide (NOx) emissions of PODE blends decreased simultaneously with 20% EGR at all loads. With the increase of pilot-main interval, the ID and CD of all test fuels increased, while the NOx and PM emissions decreased. The conclusions of the present research provide a state of the application in light-duty engines fueled with diesel/PODE blends in future work.

Abstract Objective To develop a reproducible survival rabbit model for laryngotracheal stenosis (LTS). Methods Seventy New Zealand white (NZW) rabbits were randomly divided into experimental groups (n = 30) and a control group (n = 40). In experimental groups, a nylon brush was inserted retrograde from the tracheotomy through the subglottis and rotated until a full layer circumferential mucosal injury to cartilage exposure, assisted by fiberoptic laryngoscopy (FOL) visualization. Experimental group 1 (n = 10), rotated 10 times; group 2 (n = 20), rotated 20 times. The control group underwent tracheotomy only without nylon brush scraping. The rabbits underwent FOL at 1st, 4th, 8th, and 12th week postinjury respectively to observe the formation of LTS. They were euthanized and the larynxes and tracheas were subjected to gross and histopathological examination at 12 weeks postinjury. Results The control group all survived, while five cases in experimental groups died from LTS and/or mucous plug. Histological observation showed that the control group had intact laryngotracheal mucosal epithelium without any stenosis; the experimental groups showed proliferation of fibroblasts and thickening of collagen fibers. The mean stenosis in control group was 9.31 ± 0.98%, while that in experimental group 1 was 32.78 ± 7.07% and 58.25 ± 8.96% in experimental group 2. The difference between the three groups was statistically significant (χ2 = 47.98, p

Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).

ALKBH5 is one of the demethylases involved in the regulation of RNA m6A modification. In addition to its role in the dynamic regulation of RNA m6A modification, ALKBH5 has been found to play important roles in various tissues fibrosis processes in recent years. However, the mechanisms and effects of ALKBH5 in fibrosis have been reported inconsistently. Multiple cell types, including parenchymal cells, immune cells (neutrophils and T cells), macrophages, endothelial cells, and fibroblasts, play roles in various stages of fibrosis. Therefore, this review analyzes the mechanisms by which ALKBH5 regulates these cells, its impact on their functions, and the outcomes of fibrosis. Furthermore, this review summarizes the role of ALKBH5 in fibrotic diseases such as pulmonary fibrosis, liver fibrosis, cardiac fibrosis, and renal fibrosis, and discusses various ALKBH5 inhibitors that have been discovered to date, exploring the potential of ALKBH5 as a clinical target for fibrosis.

Abstract Objectives To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. Methods A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. Results SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. Conclusion The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Critical relevance statement Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. Key points • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer. Graphical Abstract

BackgroundImmunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis.MethodIn this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients’ survival outcomes.ResultExperiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients.ConclusionTo the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.

Polyoxymethylene dimethyl ethers (PODE) are newly appeared promising oxygenated additive which can significantly reduce soot emissions of diesel engines. In this study, the spray and combustion characteristics of the blends of diesel with different PODE blending ratio of 0%, 20%, 50% and 100% by volume are investigated in a constant volume chamber (CVC) under 100 MPa, 120 MPa and 140 MPa injection pressures. The spray results show that liquid spray tip penetration (STP), average spray angle (ASA) and spray projected area (SPA) all decrease with PODE blending ratio under 4 MPa ambient pressure and 573 K ambient temperature. The addition of PODE in diesel can significantly reduce the soot formation and the integrated natural flame luminosity (INFL). Under 100 MPa injection pressure, the time integral sum KL in whole combustion duration of D100, P20, P50 and P100 are namely 107.35, 2.66, 0.27 and 0.08. When the PODE blending ratio are 50% and 100%, nearly no yellow flame can be observed in the combustion duration. The chemical structure of PODE plays an important role in reducing soot formation.

Abstract This study aimed to explore the prognostic impact of spatial distribution of tumor‐infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole‐slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0–3 by the Klintrup–Mäkinen (KM) grading method and this was compared to DL approaches. Inter‐rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 μm of the IM (f_im200) remained the most significant prognostic factor for progression‐free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001–0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001–0.645], p = 0.024). Inter‐rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.

IntroductionAcute and chronic otitis media (AOM and COM) are common middle ear infections that can lead to hearing loss and other complications. Recent research has shown that both macrophages and nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway are involved in the immune response to and the resolution of otitis media. However, the specific effects of Nrf2 on macrophages in the transition of AOM to COM are not well understood, and a practical approach to prevent this transition by targeting Nrf2/macrophages has not been established.MethodsIn an AOM mouse model using lipopolysaccharide (LPS) injection into the middle ear, middle ear effusion (OME)-macrophages were isolated and analyzed for Nrf2 expression. M2-like polarization of macrophages was induced by Nrf2 activation and its effects on inflammatory resolution were studied by examining inflammatory neutrophils and macrophages, proinflammatory cytokines, and oxidative levels. The survival of human middle ear epithelial cells (HMMECs) co-cultured with Nrf2-modified macrophages was also evaluated. Furthermore, restoration of Nrf2 in macrophages with adeno-associated virus (AAV) vectors was performed to determine the effect on the transition of AOM to COM in experimental mice.ResultsReduced Nrf2 in OME-macrophages during the recovery phase was associated with uncured AOM or its development into COM, demonstrated by persistent increases in inflammatory neutrophils and macrophages, proinflammatory cytokines, and oxidative levels. Nrf2 activation induced M2-like polarization of macrophages, which improved the survival of co-cultured HMMECs treated with LPS in vitro. Restoration of Nrf2 in OME-derived low-Nrf2-expressing macrophages with AAV vectors significantly inhibited the transition of AOM to COM in experimental mice.DiscussionNrf2 in macrophages plays a critical role in the immune response to and resolution of otitis media Restoration of Nrf2 expression in OME-macrophages could be a promising therapeutic approach to prevent the development of COM in AOM patients.

ObjectiveThe objectives of this study was to review the clinical features and surgical treatment outcomes of congenital second branchial cleft anomalies (CSBCAs) and to investigate the characteristic computed tomography (CT) findings of CSBCAs.MethodsWe conducted a retrospective study of 52 children who were referred to Shanghai Children's Hospital from October 2014 to December 2021 diagnosed as CSBCAs.ResultsThere were 36 males and 16 females. Of them, 35 patients were presented as having a skin pit at birth or discharge from the skin opening on the lateral neck, and 17 patients presented with an asymptomatic or painful mass. The typical CT features of CSBCAs included isolated and homogeneously hypodense cystic lesions surrounded by a uniformly thin, smooth wall. CSBCAs were generally located at the anteromedial border of the sternocleidomastoid muscle, posterior to the submandibular gland, and lateral to the carotid sheath. All patients were treated surgically and only one case underwent ipsilateral tonsillectomy. After a median follow-up of 30 (range 4–90) months, no recurrence or complications were observed.ConclusionsThe CSBCAs show some characteristic CT findings, which can help clinicians diagnose and plan surgical strategies. High ligation of the lesions is sufficient for complete excision of CSBCAs.

BackgroundIncreased rates of methicillin-resistant Staphylococcus aureus (MRSA) antibiotic resistance and the associated morbidity have increased dermatologists' attention to skin and soft tissue MRSA infections. However, the clinical characterization of MRSA skin and soft tissue infections (SSTIs) in Southwest China is lacking, which precludes optimal prevention and treatment of these infections.ObjectivesThis study was conducted to characterize the prevalence, clinical comorbidities and antibiotic susceptibility of MRSA isolates from SSTIs, including community-associated (CA) and healthcare-associated (HA) isolates.MethodsIn the Dermatology Inpatient Department of the First Affiliated Hospital of Guangxi Medical University, a retrospective study was conducted on data, including patient demographics and clinical information, from culture-confirmed S. aureus isolated from skin and soft tissue between January 1, 2015, and December 31, 2021. Isolate susceptibility to 13 antibiotics was determined using the Vitek 2 system.ResultsFrom among 864 S. aureus strains, we identified 283 MRSA (32.75%) isolates comprising 203 CA-MRSA and 80 HA-MRSA isolates. The average rate of CA-MRSA isolation for MRSA SSTIs was 71.73%. The HA-MRSA isolation rate for MRSA SSTIs increased significantly. HA-MRSA-infected patients were older. The most common dermatological presentation of CA-MRSA infection was staphylococcal scalded skin syndrome, while the comorbidity severe drug eruption was significantly associated with HA-MRSA infection. One CA-MRSA strain was resistant to linezolid, and one HA-MRSA strain had an intermediate phenotype for vancomycin; both strains had low sensitivity to clindamycin and erythromycin (3.70%~19.40%). However, HA-MRSA isolates were more susceptible to trimethoprim/sulfamethoxazole.ConclusionsCA-MRSA is a predominant pathogen causing SSTIs, and HA-MRSA infection incidence is increasing gradually. Both strains showed increasing antibiotic resistance. Our data on MRSA susceptibility may guide dermatologist antibiotic treatment decisions. Dermatologists should consider these identified comorbidities of MRSA SSTIs when patients are admitted and initiate early prevention and treatment of MRSA.

Lithium-ion batteries (LiBs) have been widely applied in electric vehicles (EVs) and energy storage devices. The battery thermal management system (BTMS) critically impacts the safety and degradation of LiBs. Phase change material (PCM) is a promising passive BTMS solution owing to its high latent heat and non-parasitic power consumption requirements. In this paper, paraffin (PA) as the PCM was embedded in the copper foam to enhance the heat dissipation of the cooling material. The thermal responses of the battery module were comparatively investigated under different thermal management solutions, including natural air, pure PCM, and copper foam-PCM. A battery module consisting of 16 thermal dummy cells (TDC) was designed, built, and calibrated to replace real commercial 21,700 NMC battery cells. The findings indicate that the proposed copper foam-PCM solution effectively enhances heat dissipation and improves the temperature uniformity of the battery module. For instance, in the condition of intensive operation (60% depth of discharge and 3C discharge), copper foam-PCM composite material reduces the maximum temperature rise from 57.4 °C to 51.4 °C (-10.4%) compared to pure PCM. At ambient temperatures of 25 °C and 35 °C, the temperature inhomogeneity of the battery module with copper foam-PCM is maintained within 5 °C and 2 °C, respectively. Besides, the effect of copper foam-PCM cooling on the cell-to-pack conversion efficiency was evaluated. The gravimetric cell-to-pack ratio (GCTP) and volumetric cell-to-pack ratio (VCTP) of the battery pack employing the proposed BTMS reached 53.1% and 45.6%, respectively.

Introduction Post-tonsillectomy haemorrhage (PTH) is the most common and significant life-threatening complication following tonsillectomy, especially in children. Coblation tonsillectomy (CTE) at low temperature is extensively used in China and has gradually replaced conventional tonsil dissection. However, risk of late PTH has been shown to increase with the use of hot instruments. The aim of this study is to detect post-CTE haemorrhage (PCTH) rates and analyse risk factors of PCTH in China, through a nationwide multicentre prospective study.Methods and analysis This investigator-initiated, prospective, multicentre clinical trial will involve children with tonsil disease who will undergo CTE from 22 research centres in different cities in China. All operations will be performed using the same technique of extracapsular tonsillectomy. Data will be collected for all patients enrolled in this study through a preoperative visit, intraoperative data and a postoperative visit. The measurement data conforming to a normal distribution will be expressed by means±SDs, and a Student’s t-test will be used for comparison. The comparison among groups of counting data will be expressed by percentage or rate, and a χ2 test will be used for comparison. Non-conditional logistic regression analysis will be used to analyse the preoperative, intraoperative and postoperative risk factors for haemorrhage rate after CTE. P

Cyclopentanone from lignocelluloses is a promising renewable biofuel and can be used as diesel alternative fuel. Cyclopentanone has excellent fuel properties and high intramolecular oxygen content, which has the potential in promoting combustion and reducing emissions. In this study, the effects of blending cyclopentanone with diesel on the combustion and emission of common rail diesel engine were investigated. The novelty of this study is to figure out which blending ratio is the most promising for diesel engine. Cyclopentanone is blended with diesel with 10% and 20% ratio by vol., noted as C10 and C20, respectively. Double injection strategy (pre-injection and main-injection) was adopted in this study. Compared with pure diesel, blending cyclopentanone can prolong the ignition delay, increase the peak heat release rate and increase the peak in-cylinder temperature. The emission tests indicate that blending cyclopentanone can effectively reduce the particle number concentrations (PNC) and particle volume concentrations (PVC) compared to diesel. Further, the number geometric mean diameters (NGMD) of cyclopentanone diesel blend fuels are lower than those of diesel and the reduction increases with cyclopentanone blending ratio. However, a little increase in NOx emission is observed. With the increase of EGR ratio, the NOx emissions of all test fuels decrease obviously, the PNCs and PVCs of diesel and C10 increase. PNCs and PVCs of C20 almost remain unchanged when the EGR ratio is no higher than 8%. Consequently, with 8% EGR ratio, C20 has the best effect in reducing both particle and NOx emissions and it has great potential for application on diesel vehicles.

ObjectiveTo explore the clinical characteristics and surgical treatment of children with bronchogenic cysts (BCs) in the head and neck region.MethodsA retrospective study of 10 pediatric patients with BCs in the head and neck region treated in Shanghai Children's Hospital during 2011 to 2022 was performed.ResultsBased on their pathological diagnosis, 10 patients with BCs in the head and neck were identified. The most common location was the neck (8 patients, 80%; 2 midline neck, 6 lateral neck), followed by the ventral tip of tongue (1 patient), and the posterior pharyngeal wall (1 patient). Misdiagnosed as lymphangioma in 5 cases, cyst in 3 cases, thyroglossal duct cyst (TGDC) in 2 cases and congenital pyriform sinus fistula (CPSF) in 1 case preoperative. The median follow-up period after surgery was 4.68 (range, 0.67–9.25) years. All 10 patients underwent complete resection without recurrence or other complications.ConclusionsAlthough extremely rare, BCs should be considered in the differential diagnosis of midline and lateral neck masses or intraoral cysts in children. Surgical excision is recommended in BCs, and the diagnosis is definitively confirmed by histopathology.

In the usage phase, diesel engines fuelled with diesel-biodiesel blends produced lower soot, HC and CO emissions. However, the environmental effects should include the greenhouse gas (GHG) emissions. GHG variations of biodiesel production from three generation feedstocks were analyzed based on the land use change (LUC). Adequate land use change could keep the biodiversity and did not cause the increase of GHG emissions. The choice for feedstocks of biodiesel in China was comprehensively analyzed based on the land use type, the precipitation and the oil contents and the cultivation conditions of plants. Then, the suggestions for biodiesel development in China were given. Results showed that China should thoroughly abandon the first generation feedstocks due to the lack of arable land. Jatropha curcas (J. curcas), Pistacia chinensis Bunge (P. chinensis), Comus wilsoniana (C. wilsoniana) and Xanthoceras sorbifolium Bunge (X. sorbifolium) were considered as the most promising feedstocks for biodiesel production. It is suggested to plant X. sorbifolium in sand lands in the north and northwest of China with less natural rainfall and the others in south and southwest of China. The concern for microalgae should be transmitted to engineering microalgae cultivated in wastewater. The microalgae biodiesel refinery should be developed together with wastewater proposal industry and coal fired power plants. Lastly, only natural gas-based or biomass-based methanol can be used for biodiesel production.

In order to further tap the fuel saving potential of hybrid heavy-duty trucks， energy management strategy based on dynamic programming is designed. Firstly， the quasi-static vehicle model of hybrid heavy-duty truck is established based on Matlab software platform， and the mathematical model of dynamic programming optimization control is built with the fuel economy taken as the objective function， the state of charge for the batteries taken as the state variable， and the torque distribution proportion coefficient of hybrid system taken as the control variable. Then， based on energy consumption test conditions of heavy-duty hybrid vehicles in China named C-WTVC， the simulation analysis of hybrid heavy-duty truck is carried out. Finally， based on the simulation results of dynamic programming， improved rules are extracted， and the C-WTVC simulation is carried out based on the joint simulation platform of AMESim and Simulink. The simulation results show that， compared with the rule-based energy management strategy， the energy management strategy based on dynamic programming can improve the fuel economy of hybrid heavy-duty trucks by 13.9%， and the control strategy based on improved rules can improve the fuel economy of hybrid heavy-duty trucks by 2.6%， which verifies the effectiveness of the improved rule-based strategy.

Abstract Background Primary small cell carcinoma of the bladder (SCCB) is a rare disease of the genitourinary tract and reported limitedly. SCCB is very aggressive and always mixed with other histologic components, but coexistence of SCCB and prostate adenocarcinoma is extremely rare. Cases presentation Two aged males (72 and 58 years) were included in this study. Both of them presented with gross hematuria as initial symptom. Magnetic resonance imaging (MRI) demonstrated protruding lesions in the urinary bladder. Pathological examination after radical cystectomy and prostatectomy showed the concurrence of SCCB and prostate adenocarcinoma. One patient died of liver and lung metastasis 8 months after surgery, and the other patient was still alive after 19 months of follow-up. Conclusion In this paper, we reported two unusual cases of coexistence of SCCB and prostate adenocarcinoma, and reviewed relative literatures with respect to the epidemiology, clinical features, pathologic features, diagnosis, treatment and prognosis of SCCB.

Biofuels have extensive available resources and have an immense potential as promising alternative fuels for automobile. The application advantages of biofuels are mainly reflected as particulate matter (PM) reduction, carbon neutral, greenhouse gases reduction, waste utilization, energy and economic security, and fuel pluralism. Based on the understanding of molecular structure effects of biofuels on soot formation and particles morphology, the effects of alcohols, ethers, esters and biodiesel on spray and combustion process in constant volume bomb in recent years are retrospectively analyzed in this paper. For the mixture, macromolecular ester fuels and polyoxymethylene dimethyl ether (PODE) are conducive to the improvement of liquid spray, while biodiesel, small molecules, dimethyl ether (DME) and alcohols are reversed. Alcohols are advantageous to the extension of mixing time and the increasing of vapor-phase mixture. Through the influence integrated assessment, alcohols show the best performance on the spray, atomization and combustion, while biodiesels show the worst. But in terms of combustion, PODE is the best choice without considering spray and atomization. For binary alternative-diesel fuel blends, methanol or butanol is the best additive based on synthetically considerations on spray, atomization and combustion. To meet the requirements of the fuel application of diesel engine, ternary fuel or even quaternary fuel have been proposed and explored. This review can help to form a systematic understanding on fuel recombining and obtain the guide of clean and efficient fuel formulation for diesel engine.

Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient’s own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients’ TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

Background. Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients. Methods. We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups. Results. Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05; p=0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617; p=0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein. Conclusions. Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.

Predicting adverse outcomes is essential for pregnant women with systemic lupus erythematosus (SLE) to minimize risks. Applying statistical analysis may be limited for the small sample size of childbearing patients, while the informative medical records could be provided. This study aimed to develop predictive models applying machine learning (ML) techniques to explore more information. We performed a retrospective analysis of 51 pregnant women exhibiting SLE, including 288 variables. After correlation analysis and feature selection, six ML models were applied to the filtered dataset. The efficiency of these overall models was evaluated by the Receiver Operating Characteristic Curve. Meanwhile, real-time models with different timespans based on gestation were also explored. Eighteen variables demonstrated statistical differences between the two groups; more than forty variables were screened out by ML variable selection strategies as contributing predictors, while the overlap of variables were the influential indicators testified by the two selection strategies. The Random Forest (RF) algorithm demonstrated the best discrimination ability under the current dataset for overall predictive models regardless of the data missing rate, while Multi-Layer Perceptron models ranked second. Meanwhile, RF achieved best performance when assessing the real-time predictive accuracy of models. ML models could compensate the limitation of statistical methods when the small sample size problem happens along with numerous variables acquired, while RF classifier performed relatively best when applied to such structured medical records.

Abstract Background Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.

Background and Purpose. Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and identify the risk factors for COVID-19 patients complicated with diabetes. Methods. In this multicenter retrospective study, patients with COVID-19 in China were included and classified into two groups according to whether they were complicated with diabetes or not. Demographic symptoms and laboratory data were extracted from medical records. Univariable and multivariable logistic regression methods were used to explore the risk factors. Results. 538 COVID-19 patients were finally included in this study, of whom 492 were nondiabetes and 46 were diabetes. The median age was 47 years (IQR 35.0-56.0). And the elderly patients with diabetes were more likely to have dry cough, and the alanine aminotransferase, lactate dehydrogenase, Ca, and mean hemoglobin recovery rate were higher than the other groups. Furthermore, we also found the liver and kidney function of male patients was worse than that of female patients, while female cases should be paid more attention to the occurrence of bleeding and electrolyte disorders. Moreover, advance age, blood glucose, gender, prothrombin time, and total cholesterol could be considered as risk factors for COVID-19 patients with diabetes through the multivariable logistic regression model in our study. Conclusion. The potential risk factors found in our study showed a major piece of the complex puzzle linking diabetes and COVID-19 infection. Meanwhile, focusing on gender and age factors in COVID-19 patients with or without diabetes, specific clinical characteristics, and risk factors should be paid more attention by clinicians to figure out a targeted intervention to improve clinical efficacy worldwide.

This study was performed to investigate the feasibility of using a three-dimensional (3D) finite element model for laryngomalacia severity assessment. We analyzed laryngeal computed tomography images of seven children with laryngomalacia using Mimics software. The gray threshold of different tissues was distinguishable, and a 3D visualization model and finite element model were constructed. The laryngeal structure parameters were defined. The peak von Mises stress (PVMS) value was obtained through laryngeal mechanical analysis. The PVMS values of the laryngeal soft tissue and cartilage scaffolds were independently correlated with disease severity. After stress loading the model, the relationship between laryngomalacia severity and the PVMS value was apparent. However, the PVMS value of laryngeal soft tissue was not correlated with laryngomalacia severity. This study established the efficacy of a finite element model to illustrate the morphological features of the laryngeal cavity in infants with laryngomalacia. However, further study is required before widespread application of 3D finite element modeling of laryngomalacia. PVMS values of the laryngeal cartilage scaffold might be useful for assessment of laryngomalacia severity. These findings support the notion that structural abnormalities of the laryngeal cartilage may manifest as quantifiable changes in stress variants of the supraglottic larynx.

This study aimed to investigate the influence of flash-boiling conditions on liquid propane sprays formed by a multi-hole injector at various injection pressures. The focus was on spray structures, which were analysed qualitatively and quantitatively by means of spray-tip penetration and global spray angle. The effect of flash boiling was evaluated in terms of trends observed for subcooled conditions. Propane was injected by a commercial gasoline direct injector into a constant volume vessel filled with nitrogen at pressures from 0.1 MPa up to 6 MPa. The temperature of the injected liquid was kept constant. The evolution of the spray penetration was observed by a high-speed camera with a Schlieren set-up. The obtained results provided information on the spray evolution in both regimes, above and below the saturation pressure of the propane. Based on the experimental results, an attempt to calibrate a simulation model has been made. The main advantage of the study is that the effects of injection pressure on the formation of propane sprays were investigated for both subcooled and flash-boiling conditions. Moreover, the impact of the changing viscosity and surface tension was limited, as the temperature of the injected liquid was kept at the same level. The results showed that despite very different spray behaviours in the subcooled and flash-boiling regimes, leading to different spray structures and a spray collapse for strong flash boiling, the influence of injection pressure on propane sprays in terms of spray-tip penetration and spray angle is very similar for both conditions, subcooled and flash boiling. As for the numerical model, there were no single model settings to simulate the flashing sprays properly. Moreover, the spray collapse was not represented very well, making the simulation set-up more suitable for less superheated sprays.

BackgroundA worldwide outbreak of coronavirus disease (COVID-19), since 2019, has brought a disaster to people all over the world. Many researchers carried out clinical epidemiological studies on patients with COVID-19 previously, but risk factors for patients with different levels of severity are still unclear.Methods562 patients with laboratory-confirmed COVID-19 from 12 hospitals in China were included in this retrospective study. Related clinical information, therapies, and imaging data were extracted from electronic medical records and compared between patients with severe and non-severe status. We explored the risk factors associated with different severity of COVID-19 patients by logistic regression methods.ResultsBased on the guideline we cited, 509 patients were classified as non-severe and 53 were severe. The age range of whom was 5-87 years, with a median age of 47 (IQR 35.0-57.0). And the elderly patients (older than 60 years old) in non-severe group were more likely to suffer from fever and asthma, accompanied by higher level of D-dimer, red blood cell distribution width and low-density lipoprotein. Furthermore, we found that the liver and kidney function of male patients was worse than that of female patients in both severe and non-severe groups with different age levels, while the severe females had faster ESR and lower inflammatory markers. Of major laboratory markers in non-severe cases, baseline albumin and the lymphocyte percentage were higher, while the white blood cell and the neutrophil count were lower. In addition, severe patients were more likely to be accompanied by an increase in cystatin C, mean hemoglobin level and a decrease in oxygen saturation. Besides that, advanced age and indicators such as count of white blood cell, glucose were proved to be the most common risk factors preventing COVID-19 patients from aggravating.ConclusionThe potential risk factors found in our study have shown great significance to prevent COVID-19 patients from aggravating and turning to critical cases during treatment. Meanwhile, focusing on gender and age factors in groups with different severity of COVID-19, and paying more attention to specific clinical symptoms and characteristics, could improve efficacy of personalized intervention to treat COVID-19 effectively.

The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

The analysis of in-cylinder combustion temperatures using flame image processing technology is reliable. This method can accurately, intuitively, and in real time obtain the temperature field distribution law of the combustion flame in the cylinder, so we can more deeply understand the characteristics of the combustion process of internal combustion engines. In this paper, a high-speed charge-coupled device (CCD) camera is used to record an in-cylinder combustion image, which is calculated and corrected according to the principle of three primary color temperature measurement, and the temperature field distribution of the combustion flame in the diesel engine cylinder is analyzed in detail. In addition, the temperature of the typical combustion flame images under the open-cycle and closed cycle conditions is compared by the CMS2002 measurement and MATLAB program, respectively. The results show that the accuracy of the MATLAB program is acceptable in general but not entirely acceptable in a few ways.

In order to benefit from a realistic hydrogen production device equipped on a vehicle, issues with the effects of the process parameters on H2 and CO yield need to be resolved. In this study, a reduced mechanism for n-heptane (as a surrogate diesel) reforming over a Pt/CeO2-Al2O3 catalyst is adopted to investigate the effects of the process parameters on H2 and CO yield, and the preferred process parameters are concluded. In addition, the comparison of reforming bench tests of diesel fuel and n-heptane under typical diesel engine operating conditions is conducted. The n-heptane reforming simulation results show that the maximum H2 and CO yield moves toward unity with the decreased GHSV and increased reaction temperature, and the GHSV of 10,000 1/h, O2/C ratio of 0.6 and reaction temperature of 500 °C is preferable. The contrast experiments reveal that the change trend of H2 and CO yield displays consistence, although the difference of the average H2 and CO yield results is obvious. The characteristics of n-heptane reforming can represent H2 and CO yield features of diesel fuel reforming at typical reaction temperatures in a way.