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Particulate Matter (PM) emissions from gasoline direct injection (GDI) engines, particularly Particle Number (PN) emissions, have been studied intensively in both academia and industry because of the adverse effects of ultrafine PM emissions on human health and other environmental concerns. GDI engines are known to emit a higher number of PN emissions (on an engine-out basis) than Port Fuel Injection (PFI) engines, due to the reduced mixture homogeneity in GDI engines. Euro 6 emission standards have been introduced in Europe (and similarly in China) to limit PN emissions from GDI engines. This article summarises the current state of research in GDI PN emissions (engine-out) including a discussion of PN formation, and the characteristics of PN emissions from GDI engines. The effect of key GDI engine operating parameters is analysed, including air-fuel ratio, ignition and injection timing, injection pressure, and EGR; in addition the effect of fuel composition on particulate emissions is explored, including the effect of oxygenate components such as ethanol.

According to in-depth research on the perception ability of dangerous omens of excellent drivers, references can be provided for the development of brain-like intelligence and its transplantation, as well as applications in the field of autonomous driving, which will improve the active safety and intelligence level of vehicles. Previous studies have shown that there is indeed a dangerous omen before an accident occurs. However, current studies are still unclear about the bio-psychophysiological characteristics exhibited by drivers with high levels of sensory agility when they anticipate potential warning signs, and there is no method for screening such drivers who can perceive dangerous omens proposed by any research. To address the above issues, this paper conducts in-depth research. Firstly, through designing dangerous scenarios and conducting hazard perception tests, we collect physiological, psychological, and physical data, such as drivers’ bioelectrical signals (electroencephalogram and electrocardiogram) and eye movements. Secondly, through playing back experimental videos, actively questioning drivers, and analyzing local changes in their electroencephalogram data, the driver’s ability to identify a dangerous omen and the moment of perception are determined. Thirdly, based on techniques such as the Kolmogorov–Smirnov test and the Mann–Whitney U test, the differences in bioelectrical and eye movement characteristics between drivers who can perceive a dangerous omen and others can be further revealed. Finally, the driver’s bioelectrical and eye movement characteristics are used as latent variables, and their corresponding data are utilized as observation indicators. We construct a structural equation model for screening drivers capable of perceiving a dangerous omen and conduct calibration and validation. This study provides inspirational ideas for empowering vehicles to identify potential hazards, advancing end-to-end and other higher-level autonomous driving technologies, and further enhancing road traffic safety.

There is growing evidence that mountains are experiencing some of the highest rates of climate warming, but assessment of the ecological impacts of climate change is often limited due to a lack of long-term monitoring data for comparative study in many ecosystems. In this study, we present an empirical work for assessing ecological responses with botanical legacy data in the Qilian Mountains of China. Plot-scale and transect-wide survey was conducted for alpine shrub communities along an elevational gradient 20 years ago. Recently, we resampled the permanent plots to investigate how the community changes may be linked to climatic variability. We found no significant temporal shifts in species richness; but the community structure underwent substantial changes, as indicated by visible shifts in the relative density of dominant shrub species and the frequency of occurrence of understory herbaceous species. This reshuffling of plant community composition reflected a series of complex responses to climate change. Specifically, wet-demanding species have become more frequent due to the recently enhanced precipitation, while the replacement of some low-statured plants with different requirements for light was indirectly regulated by climate warming via reshaping the altitudinal patterns of dominant shrubs. Climate-mediated shifts in shrub species distribution altered the expected evolutional trajectory of alpine community, which increased the complexity and nonlinearity of the responses of the communities at different altitudes to climatic variability. Our results suggested that in-depth knowledge of indirect effects can facilitate to lessen the uncertainty in predicting future community dynamics in a changing climate.

Porcine deltacoronavirus (PDCoV) is a newly discovered enteropathogenic coronavirus primarily responsible for diarrhea and mortality in piglets, with the potential to infect humans, thereby posing a significant threat to both human health and the global pig industry. Currently, there is no commercially available live-attenuated vaccine for PDCoV. In this study, an isolated virulent PDCoV strain, DHeB1, was continuously passaged in LLC-PK1 cells for up to 110 passages. The virus growth kinetics in cell culture and complete genome sequences of various passages (F11, F40, F70, F90, and F110) were determined. The results indicated significant increases in virus titers at passages F40 and F90. Sequence analysis revealed that only a few single-nucleotide mutations (some of which resulted in amino acid changes) and one nucleotide insertion were observed throughout successive passages. Notably, the eight and seven amino acid mutations that emerged in F40 and F70, respectively, remained stable in subsequent passages and were predominantly located in the S glycoprotein. The pathogenicity of F11, F40, F70, and F90 was assessed in 5-day-old piglets, revealing markedly reduced clinical symptoms, histopathological lesions, and intestinal PDCoV antigen distributions in piglets inoculated with F70 or F90. Importantly, F90 exhibited little to no virulence in piglets. The immunogenicity of F70, F90, and F110 was further evaluated in weaned piglets, with results indicating that the neutralizing antibody titers induced by F70 and F90 were comparable and significantly higher than those induced by F110. Collectively, these findings suggest that the PDCoV strain DHeB1 has been attenuated and can be used to develop a live-attenuated vaccine against PDCoV.

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety.

Changes in climate extremes pose far-reaching consequences to ecological processes and hydrologic cycles in alpine ecosystems of the arid mountain regions. Therefore, regional assessments in various climates and mountain regions are needed for understanding the uncertainties of the change trends for extreme climate events. The objective of this study was to assess the spatial distribution and temporal trends of extreme precipitation and temperature events responses to global warming on the arid mountain regions of China. Results found that temperature extremes exhibited a significant warming trend, consistent with global warming. Warming trend in autumn and winter were greater than in spring and summer. Besides, precipitation extremes also exhibited statistically increase trend, such as number of days with heavy precipitation and rain day precipitation, etc. The distribution of the number of rainy days was showed a significant increasing trend in many sites, indicating that the increase of rain day precipitation mainly contributed by the increase of single precipitation event duration and moderate-rain days. The greater increasing trend of extreme climate events mainly existed in higher altitudes. This results lend an evidence to earlier predictions that the climate in northwestern China is changing from cold-dry to warm-wet.

Antibiotics are the frontline therapy for bacterial infections, yet their efficacy is critically threatened by antimicrobial resistance (AMR), a crisis largely driven by mutations in protein targets. While this mechanism is prevalent, data on its structural impact remains highly dispersed, and existing resources such as CARD, ResFinder, MEGARes, and NDARO prioritize gene identification over mechanistic insight. To address this gap, we developed BactProNET, a bioinformatics platform focused specifically on target-mediated resistance caused by amino acid substitutions in antibiotic target proteins. It provides structural and evolutionary analysis distinct from broader AMR databases. Its core innovation is a multi-level data integration that establishes a wild-type reference system and enables comparative analysis with mutant proteins to facilitate mechanistic inference. The platform integrates curated resistance mutations with AlphaFold2-predicted three-dimensional (3D) structures, both wild-type and mutant molecular docking models defining an “optimal binding” baseline and its potential disruption by resistance mutations, and integrated phylogenetic and sequence alignment views. BactProNET currently houses data on 44 bacterial species, 107 protein targets, 196 mutation sites (representing 243 unique amino acid substitutions), 323 antibiotics, and 640 wild-type plus 1,697 mutant docking models. The platform is accessible via an interface with embedded basic local alignment search tool (BLAST) and multiple sequence alignment (MSA) tools. As a one-stop platform for target-mediated resistance, it facilitates the interpretation of AMR’s molecular mechanisms and provides a data-driven foundation for the rational design of next-generation antimicrobial drugs.

The establishment of large-scale forest plantations in the arid and semi-arid area of the Qilian Mountains in China has effectively protected water and soil resources and enhanced carbon sequestration capacity of forest ecosystems. However, the effects of different management practices in these plantations on soil water holding capacity (SWHC) and soil water availability (SWA) are uncertain in this fragile ecosystem. Here, we investigated the effects of no thinning (NT), light thinning (LT, 20% thinning intensity), and heavy thinning (HT, 40% thinning intensity) on SWHC and SWA in different soil depths of a forest plantation, and compared them to those in a natural Picea crassifolia forest (NF). Our results revealed that at low soil water suction stage, SWHC in the plantations (LT, HT, and NT) was greater in the topsoil layer (0-40 cm) than that in the NF site, while SWHC in the subsoil layer (40-80 cm) in NF was significantly greater than that in the thinning stands. At medium and high-water suction stage, SWHC in LT and NF stands was greater than that in HT and NT. Soil water characteristic curves fitted by VG model showed that the relative change in soil water content in LT topsoil layer was the smallest and SWHC was greatest. Changes in soil physicochemical properties included higher bulk density and lower total porosity, which reduced the number of macropores in the soil and affected SWHC. The bulk density, total porosity, silt content, and field capacity were the main factors jointly affecting SWA. High planting density was the main reason for the low SWA and SWHC in NT, but this can be alleviated by stand thinning. Overall, 20% thinning intensity (light intensity thinning) may be an effective forest management practice to optimize SWHC and SWA in P. crassifolia plantations to alleviate soil water deficits.

Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides. In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set). Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52–67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77–1·00) to 0·97 (0·93–1·00) and accuracy values ranged from 0·91 (0·85–0·98) to 0·97 (0·93–1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80–0·91) to 0·95 (0·86–1·00) and accuracy values ranged from 0·79 (0·74–0·85) to 0·99 (0·98–1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI 0·75–0·85) to 0·91 (0·88–1·00) and accuracy values ranged from 0·79 (0·76–0·82) to 0·95 (0·93–0·96); for aspiration biopsy samples, AUC values ranged from 0·76 (0·70–0·83) to 0·87 (0·80–0·94) and accuracy values ranged from 0·76 (0·74–0·79) to 0·97 (0·96–0·98). The model also showed strong performance on the TCGA dataset (473 patients; 535 slides; AUC values ranged from 0·82 [95% CI 0·71–0·93] to 0·96 [0·91–1·00], accuracy values ranged from 0·79 [0·70–0·88] to 0·95 [0·90–1·00]). The DeepGEM model, trained on primary region biopsy samples, could be generalised to biopsy samples from lymph node metastases, with AUC values of 0·91 (95% CI 0·88–0·94) for EGFR and 0·88 (0·82–0·93) for KRAS and accuracy values of 0·85 (0·80–0·88) for EGFR and 0·95 (0·92–0·96) for KRAS and showed potential for prognostic prediction of targeted therapy. The model generated spatial gene mutation maps, indicating gene mutation spatial distribution. We developed an AI-based method that can provide an accurate, timely, and economical prediction of gene mutation and mutation spatial distribution. The method showed substantial potential as an assistive tool for guiding the clinical treatment of patients with lung cancer. National Natural Science Foundation of China, the Science and Technology Planning Project of Guangzhou, and the National Key Research and Development Program of China. For the Chinese translation of the abstract see Supplementary Materials section.

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers’ life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.