Porcine epidemic diarrhea virus E protein induces formation of stress granules and attenuates protein translation through activation of the PERK/eIF2α signaling pathway.

Porcine epidemic diarrhea virus (PEDV) envelope protein (E) has been characterized as an important structural protein that plays critical roles in the interplay with its host to affect the virus life cycle. Stress granules (SGs) are host translationally silent ribonucleoproteins, which are mainly induced by the phosphorylation of eIF2α in the PERK/eIF2α signaling pathway. Our previous study found that PEDV E protein caused endoplasmic reticulum stress response (ERS)-mediated suppression of antiviral proteins' translation. However, the link and the underlying mechanism by which PEDV induces SGs formation and suppresses host translation remain elusive. In this study, our results showed that PEDV E protein significantly elevated the expression of GRP78, CANX, and phosphorylation of PERK and eIF2α, indicating that the PERK/eIF2α branch of ERS was activated. PEDV E protein localized to the ER and aggregated into puncta to reconstruct ER structure, and further induced SGs formation, which has been caused through upregulating the G3BP1 expression level. In addition, a significant global translational stall and endogenous protein translation attenuation were detected in the presence of E protein overexpression, but the global mRNA transcriptional level remained unchanged, suggesting that the shutoff of protein translation was associated with the translation, not with the transcription process. Collectively, this study demonstrates that PERK/eIF2α activation is required for SGs formation and protein translation stall. This study is beneficial for us to better understand the mechanism by which PEDV E suppresses host protein synthesis, and provides us a new insight into the host translation regulation during virus infection.

Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer.

Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM). Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability as an independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A's implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.

Kidney toxicity and transcriptome analyses of male ICR mice acutely exposed to the mushroom toxin α-amanitin.

Amatoxins are responsible for most fatal mushroom poisoning cases, as it causes both hepatotoxicity and nephrotoxicity. However, studies on amatoxin nephrotoxicity are limited. Here, we investigated nephrotoxicity over 4 days and nephrotoxicity/hepatotoxicity over 14 days in mice. The organ weight ratio, serological indices, and tissue histology results indicated that a nephrotoxicity mouse model was established with two stages: (1) no apparent effects within 24 h; and (2) the appearance of adverse effects, with gradual worsening within 2-14 days. For each stage, the kidney transcriptome revealed patterns of differential mRNA expression and significant pathway changes, and Western blot analysis verified the expression of key proteins. Amanitin-induced nephrotoxicity was directly related to RNA polymerase II because mRNA levels decreased, RNA polymerase II-related pathways were significantly enriched at the transcription level, and RNA polymerase II protein was degraded in the early poisoning stage. In the late stage, nephrotoxicity was more severe than hepatotoxicity. This is likely associated with inflammation because inflammation-related pathways were significantly enriched and NF-κB activation was increased in the kidney.

Improvement of the anthropogenic emission rate estimate in Ulaanbaatar, Mongolia, for 2020-21 winter.

Ulaanbaatar (UB), the fast-growing capital of Mongolia, is known for its world's worst level of particulate matter (PM) concentrations in winter. However, current anthropogenic emission inventories over the UB are based on data from more than fifteen years ago, and satellite observations are scarce because UB is in high latitudes. During the winter of 2020-21, the first period of the Fine Particle Research Initiative in East Asia considering the National Differences (FRIEND), several times higher concentrations of PM in UB compared to other urban sites in East Asia were observed but not reproduced with a chemical transport model mainly due to the underestimated anthropogenic emissions. Therefore, we devised a method for sequentially adjusting emissions based on the reactivity of PM precursors using ground observations. We scaled emission rates for the inert species (CO, elemental carbon (EC), and organic carbon (OC)) to reproduce their observed ambient concentrations, followed by SO2 to reproduce the concentration of SO42-, which was examined to have the least uncertainty based on the abundance of observed NH3, and finally NO and NH3 for NO3-, and NH4+. This improved estimation is compared to regional inventories for Asia and suggests more than an order of magnitude increase in anthropogenic emissions in UB. Using the improved emission inventory, we were able to successfully reproduce independent observation data on PM2.5 concentrations in UB in December 2021 from the U.S. Embassy. During the campaign period, we found more than 50% of the SO42-, NO3-, and NH4+ increased in UB due to the improvement could travel to Beijing, China (BJ), and about 20% of the SO42- could travel to Noto, Japan (NT), more than 3000 km away. Also, the anthropogenic emissions in UB can effectively increase OC, NO3-, and NH4+ concentrations in BJ when Gobi dust storms occur.

Anlotinib plus docetaxel vs. docetaxel alone for advanced non-small-cell lung cancer patients who failed first-line treatment: A multicenter, randomized phase II trial.

OBJECTIVES: Given the modest efficacy of docetaxel in advanced non-small cell lung cancer (NSCLC), this study assesses the therapeutic potential and safety profile of anlotinib in combination with docetaxel compared to docetaxel monotherapy as a second-line therapy for patients with advanced NSCLC. MATERIALS AND METHODS: In this phase II study, patients with advanced NSCLC experiencing failure with first-line platinum-based regimens were randomized in a 1:1 ratio to receive either anlotinib plus docetaxel or docetaxel alone. Primary endpoint was progression-free survival (PFS), with overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety as secondary endpoints. RESULTS: A total of 83 patients were randomized. The combination of anlotinib and docetaxel significantly extended median PFS to 4.4 months compared to 1.6 months for docetaxel alone (hazard ratio [HR] = 0.38, 95 % confidence interval [CI]: 0.23-0.63, P = 0.0002), and also demonstrated superior ORR (32.5 % vs. 9.3 %, P = 0.0089) and DCR (87.5 % vs. 53.5 %, P = 0.0007). Median OS was observed at 12.0 months in the combination group vs. 10.9 months in the monotherapy group (HR = 0.82, 95 % CI: 0.47-1.43, P = 0.4803). For patients previously treated with immunotherapy, the median PFS was notably longer at 7.8 vs. 1.7 months (HR = 0.22, 95 % CI: 0.09-0.51, P = 0.0290). The incidence of grade ≥ 3 treatment-related adverse events, predominantly leukopenia (15.0 % vs. 7.0 %) and neutropenia (10.0 % vs. 5.0 %), was manageable across both groups. CONCLUSION: Anlotinib plus docetaxel offers a viable therapeutic alternative for patients with advanced NSCLC who failed first-line platinum-based treatments.

Polymeric Electronic Shielding Layer Enabling Superior Dendrite Suppression for All-Solid-State Lithium Batteries.

LiBH4 is one of the most promising candidates for use in all-solid-state lithium batteries. However, the main challenges of LiBH4 are the poor Li-ion conductivity at room temperature, excessive dendrite formation, and the narrow voltage window, which hamper practical application. Herein, we fabricate a flexible polymeric electronic shielding layer on the particle surfaces of LiBH4. The electronic conductivity of the primary LiBH4 is reduced by 2 orders of magnitude, to 1.15 × 10-9 S cm-1 at 25 °C, due to the high electron affinity of the electronic shielding layer; this localizes the electrons around the BH4- anions, which eliminates electronic leakage from the anionic framework and leads to a 68-fold higher critical electrical bias for dendrite growth on the particle surfaces. Contrary to the previously reported work, the shielding layer also ensures fast Li-ion conduction due to the fast-rotational dynamics of the BH4- species and the high Li-ion (carrier) concentration on the particle surfaces. In addition, the flexibility of the layer guarantees its structural integrity during Li plating and stripping. Therefore, our LiBH4-based solid-state electrolyte exhibits a high critical current density (11.43 mA cm-2) and long cycling stability of 5000 h (5.70 mA cm-2) at 25 °C. More importantly, the electrolyte had a wide operational temperature window (-30-150 °C). We believe that our findings provide a perspective with which to avoid dendrite formation in hydride solid-state electrolytes and provide high-performance all-solid-state lithium batteries.

Unique gap-related SERS behaviors ofp-aminothiophenol molecules absorbed on TiO2surface in periodic TiO2/Ni nanopillar arrays.

We observed a unique interpillar gap-related surface-enhanced Raman scattering (SERS) behavior ofp-aminothiophenol (PATP) molecules from periodic TiO2nanopillar arrays with three gap sizes of 191, 297 and 401 nm, which is completely different from that on Ag and Ni nanopillar arrays. Especially, the gap-size-dependent charge-transfer (CT) resonance enhancement from TiO2/Ni has been indicated through comparisons of variation trend of SERS intensities with inter-pillar gap size between TiO2/Ni and Ag/TiO2/Ni as well as Ni nanoarrays, and been confirmed by spectra of ultraviolet-visible absorption and photoluminescence. Results demonstrate that the CT resonance enhancement is more susceptible to the change of the gap size compared with the surface plasmon resonance (SPR) enhancement in TiO2/Ni nanoarrays. Hence, SPR and CT enhancement showing different variation trend and rate with the gap size that leads to a different relative contribution of CT resonance to the overall SERS enhancement as gap size changes, and consequently results in a unique gap-related SERS behavior for TiO2/Ni nanoarrays. The present study is not only helpful for investigating SERS mechanism for semiconductors but also providing a method to design and optimize periodic metal/semiconductor SERS substrates in a controllable way.

Survival benefit of surgery for second primary esophageal cancer following gastrointestinal cancer: a population-based study.

BACKGROUND: The incidence of second primary malignancy is increasing. However, although there is some information on second primary esophageal cancer (SPEC) itself, there is no study or guideline on the use of surgery for SPEC after gastrointestinal cancer (SPEC-GC). Thus, this study aimed to gather evidence for the benefits of surgery by analyzing a national cohort and determining the prognostic factors and clinical treatment decisions for SPEC-GC. METHODS: Data for patients with SPEC-GC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019. The prognostic factors of SPEC-GC were investigated by stepwise Cox proportional hazards regression and Kaplan-Meier analyses for overall survival and cancer-specific survival. RESULTS: A total of 8308 patients with SPEC were selected, including 582 patients with SPEC-GC. Multivariate analysis revealed that surgery, year of diagnosis, scope of regional lymph node surgery, tumor differentiation grade, SEER historic stage, and triple therapy were significant predictors of survival outcomes (P < .05). Surgery seemed to improve the prognosis of patients with SPEC-GC significantly compared with no surgery and chemoradiotherapy (P < .001). CONCLUSIONS: Surgery should be considered as the main treatment for SPEC-GC. Surgery, year of diagnosis, scope of regional lymph node surgery, tumor differentiation grade, SEER historic stage, and triple therapy were found to be independent prognostic factors for these patients. These factors should be considered in the clinical diagnosis and treatment of SPEC-GC.

Recent Advances in Understanding of the Singlet Oxygen in Energy Storage and Conversion.

Singlet oxygen (term symbol 1 Δg , hereafter 1 O2 ), a reactive oxygen species, has recently attracted increasing interest in the field of rechargeable batteries and electrocatalysis and photocatalysis. These sustainable energy conversion and storage technologies are of vital significance to replace fossil fuels and promote carbon neutrality and finally tackle the energy crisis and climate change. Herein, the recent progresses of 1 O2 for energy storage and conversion is summarized, including physical and chemical properties, formation mechanisms, detection technologies, side reactions in rechargeable batteries and corresponding inhibition strategies, and applications in electrocatalysis and photocatalysis. The formation mechanisms and inhibition strategies of 1 O2 in particular aprotic lithium-oxygen (Li-O2 ) batteries are highlighted, and the applications of 1 O2 in photocatalysis and electrocatalysis is also emphasized. Moreover, the confronting challenges and promising directions of 1 O2 in energy conversion and storage systems are discussed.

Bone metastasis is a late-onset and unfavorable event in survivors of gastric cancer after radical gastrectomy: Results from a clinical observational cohort.

Background: The timing and incidence of recurrent bone metastasis (BM) after radical gastrectomy in patients with gastric cancer (GC) as well as the survival of these patients were not fully understood. The aim of this study was to analyze the data of an observational GC cohort and identify patients who underwent curative gastrectomy and had recurrent BM to describe and clarify the pattern and profile of BM evolution after surgery. Methods: Data were retrieved from a hospital-based GC cohort, and patients who underwent upfront radical gastrectomy were selected. The time points of specific organ metastatic events were recorded, and the person-year incidence rate of metastatic events was calculated. The latency period of BM events after gastrectomy was measured and compared with that of the other two most common metastatic events, liver metastasis (LM) and distant lymph node metastasis (LNM), using analysis of variance. Propensity score matching and subgroup analysis were used for sensitivity analysis. Results: A total of 1324 GC cases underwent radical gastrectomy between January 2011 and December 2021. Of these, 67 BM, 218 LM, and 248 LNM occurred before the last follow-up. The incidence of BM events was 1.7/100 person-years, which was approximately 3-fold lower than that of LM and distant LNM events (5.5 and 6.3 per 100 person-years, respectively). BM events had a significantly longer latency (median time, 16.5 months) than LM and LNM events (11.1 and 12.0 months, respectively). Recurrent BM led to a worse prognosis (median survival, 4.5 months) than those of LM and LNM events (median survival, 7.7 and 7.1 months, respectively). However, no difference in overall survival after gastrectomy was observed among the groups. Conclusions: Compared with other common metastatic events, BM in GC after gastrectomy is a late-onset event indicating poor survival. Trial registration: No. ChiCTR1800019978; http://www.chictr.org.cn/.

Life's essential 8 and risk of all-cause mortality in individuals with cardiovascular diseases: A prospective community-based study.

BACKGROUND: Although risk factors for mortality in individuals with cardiovascular diseases (CVD) have been reported, little is known regarding the association between the comprehensive cardiovascular health (CVH) index assessed by life's essential 8 (LE8) and the risk of mortality. HYPOTHESIS: The aim of this study was to evaluate the CVH assessed by LE8 and risk of mortality in individuals with CVD. METHODS: A total of 1391 participants with CVD diagnosed before 2014 from the Kailuan cohort were included in the analysis. The CVH score ranged from 0 to 100 was assessed using the LE8 metrics (diet quality, physical activity, sleep health, cigarette smoking, body mass index, lipids, blood glucose, and blood pressure). Cox regression model was used to estimate the association between the CVH score and risk of all-cause mortality. RESULTS: During a mean follow-up of 6.1 ± 1.5 years, 229 deaths occurred. The hazard ratio for all-cause mortality was 0.57 (95% confidence interval [CI]: 0.38, 0.84) in the highest quartiles compared with the lowest quartiles of CVH scores and 0.85 (95% CI: 0.75, 0.95) for each 10 points increment in CVH scores (ptrend = .009), after adjustment for age, sex, CVD duration, social-economic status, alcohol consumption, inflammation, medicine use, and kidney function. We did not observe significant interactions between the CVH scores and age, sex, and duration of CVD diagnosis (pinteraction > .05 for all). CONCLUSIONS: The CVH assessed by the LE8 metrics was associated with a lower risk of all-cause mortality in individuals with CVD.

Design and implementation of an automatic nursing assessment system based on CDSS technology.

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation. RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.

A pan-cancer analysis of anti-proliferative protein family genes for therapeutic targets in cancer.

The recently discovered APRO (anti-proliferative protein) family encodes a group of trans-membrane glycoproteins and includes 6 members: TOB1, TOB2, BTG1, BTG2, BTG3 and BTG4. The APRO family is reportedly associated with the initiation and progression of cancers. This study aims to undertake a comprehensive investigation of the APRO family of proteins as a prognostic biomarker in various human tumors. We performed a pan-cancer analysis of the APRO family based on The Cancer Genome Atlas (TCGA). With the bioinformatics methods, we explored the prognostic value of the APRO family and the correlation between APRO family expression and tumor mutation burden (TMB), microsatellite instability (MSI), drug sensitivity, and immunotherapy in numerous cancers. Our results show that the APRO family was primarily down-regulated in cancer samples. The expression of APRO family members was linked with patient prognosis. In addition, APRO family genes showed significant association with immune infiltrate subtypes, tumor microenvironment, and tumor cell stemness. Finally, our study also demonstrated the relationship between APRO family genes and drug sensitivity. This study provides comprehensive information to understand the APRO family's role as an oncogene and predictor of survival in some tumor types.

A network system for the prevention and treatment of mushroom poisoning in Chuxiong Autonomous Prefecture, Yunnan Province, China: implementation and assessment.

BACKGROUND: Mushroom poisoning is a major public health issue in China. The integration of medical resources from different institutes of different levels is crucial in reducing the harm of mushroom poisoning. However, few studies have provided comprehensive implementation procedures and postimplementation effectiveness evaluations. To reduce the harm caused by mushroom poisoning, a network system for the prevention and treatment of mushroom poisoning (NSPTMP) was established in Chuxiong, Yunnan Province, a high-risk area for mushroom poisoning. METHODS: The NSPTMP consists of three types of institutions, namely, centers for disease prevention, hospitals, and health administration departments, with each kind of institution comprising prefecture, county/city, town, and village levels. After three years of implementation, the network was evaluated by comparing the indices before and after network implementation using data from the "Foodborne Disease Outbreak Surveillance System" and 17 hospitals in Chuxiong. The indices included the fatalities caused by mushroom poisoning, the composition ratios of different types of mushrooms for both outpatients and inpatients and the hospitalization rates. RESULTS: Compared to the average fatality rate of mushroom poisoning from 2015 to 2017, the average fatality rate from 2018 to 2020 significantly decreased from 0.57 to 0.06% (P < 0.001). Regarding the poisonous genus containing lethal mushrooms, the outpatient and inpatient composition ratios significantly decreased for Amanita (9.36-2.91% and 57.23-17.68%, respectively) and Russula (15.27-8.41%) (P < 0.05). Regarding poisonous mushrooms that caused mild symptoms, the outpatient and inpatient composition ratios significantly increased for Scleroderma (5.13-13.90% and 2.89-18.90%, respectively) and Boletaceae (19.08-31.71%) (P < 0.05), and the hospitalization rates significantly increased for Scleroderma (6.33-18.02%) and Boletaceae (5.65-12.71%) (P < 0.05). CONCLUSIONS: These findings suggest that the NSPTMP effectively reduced the harm caused by mushroom poisoning. In addition to the integration of medical resources, the development of poisonous mushroom identification, hierarchical treatment systems in hospitals, public education, and professional training also played important roles in improving the system's effectiveness. The establishment and evaluation of the NSPTMP in Chuxiong Prefecture can provide valuable insights and serve as a model for other regions facing similar challenges in managing mushroom poisoning.

Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions.

Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp < 400 nm, but 34.8% of them contained multi-soot cores (nsoot ≥ 2) and nsoot increased 3-9 times with increasing Dp. We established the soot mixing models to quantify transformation from condensation- to coagulation-dominant regime at Dp ≈ 400 nm. Studies provide essential references for adopting mixing rules and quantifying the optical absorption of soot in atmospheric models.

Developing an Indirect ELISA for the Detection of African Swine Fever Virus Antibodies Using a Tag-Free p15 Protein Antigen.

African swine fever (ASF) is one of the most severe diseases caused by the ASF virus (ASFV), causing massive economic losses to the global pig industry. Serological tests are important in ASF epidemiological surveillance, and more antigen targets are needed to meet market demand for ASFV antibody detection. In the present study, ASFV p15 protein was fusion-expressed in Escherichia coli (E. coli) with elastin-like polypeptide (ELP), and the ELP-p15 protein was purified using a simple inverse transition cycling (ITC) process. The ELP tag was cleaved off using tobacco etch virus protease (TEVp), resulting in a tag-free p15 protein. Western blot analysis demonstrated that the p15 protein reacted strongly with ASFV-positive serum. The p15 protein was used as a coating antigen in an indirect ELISA (iELISA) for detecting ASFV antibodies. The p15-iELISA method demonstrated high specificity to ASFV-positive sera, with a maximum detection dilution of 1:1600. Moreover, the method exhibited good reproducibility, with less intra-assay and inter-assay CV values than 10%. Therefore, p15-iELISA offers a novel approach for accurately detecting ASFV antibodies with significant clinical application potential.

Predicting Atmospheric Particle Phase State Using an Explainable Machine Learning Approach Based on Particle Rebound Measurements.

The particle phase state plays a vital role in the gas-particle partitioning, multiphase reactions, ice nucleation activity, and particle growth in the atmosphere. However, the characterization of the atmospheric phase state remains challenging. Herein, based on measured aerosol chemical composition and ambient relative humidity (RH), a machine learning (ML) model with high accuracy (R2 = 0.952) and robustness (RMSE = 0.078) was developed to predict the particle rebound fraction, f, which is an indicator of the particle phase state. Using this ML model, the f of particles in the urban atmosphere was predicted based on seasonal average aerosol chemical composition and RH. Regardless of seasons, aerosols remain in the liquid state of mid-high latitude cities in the northern hemisphere and in the semisolid state over semiarid regions. In the East Asian megacities, the particles remain in the liquid state in spring and summer and in the semisolid state in other seasons. The effects of nitrate, which is becoming dominant in fine particles in several urban areas, on the particle phase state were evaluated. More nitrate led the particles to remain in the liquid state at an even lower RH. This study proposed a new approach to predict the particle phase state in the atmosphere based on RH and aerosol chemical composition.

Comparative analysis of organic chemical compositions in airborne particulate matter from Ulaanbaatar, Beijing, and Seoul using UPLC-FT-ICR-MS and artificial neural network.

This paper presents comparative study on the composition and sources of PM2.5 in Ulaanbaatar, Beijing, and Seoul. Ultrahigh performance liquid chromatography (UPLC) combined with ultrahigh resolution mass spectrometry (UHR-MS) were employed to analyze 85 samples collected in winter. The obtained 340 spectra were interpreted with artificial neural network (ANN). PM2.5 mass concentrations in Ulaanbaatar were significantly higher than those in Beijing and Seoul. ANN based interpretation of UPLC UHR-MS data showed that aliphatic/lipid derived organo­sulfur compounds, polycyclic aromatic and organo­oxygen compounds were characteristic to Ulaanbaatar. Whereas, aliphatic/lipid-derived organo­oxygen compounds were major components in Beijing and Seoul. Aromatic organo­nitrogen compounds were the main contributors to differentiating the spectra obtained from Beijing from the other cities. Based on two-dimensional gas chromatography/high resolution mass spectrometric (GCxGC/HRMS) data, it was determined that the concentrations of the polycyclic aromatic hydrocarbon (PAH) and polycyclic aromatic sulfur heterocycle (PASH) containing sulfur were highest in Ulaanbaatar, followed by Beijing and Seoul. Coal/biomass combustion was identified as the primary source of contamination in Ulaanbaatar, while petroleum combustion was the main contributor to PM2.5 in Beijing and Seoul. The conclusion that diesel-powered heavy-duty trucks and buses are the main contributors to NOx emissions in Beijing is consistent with previous reports. This study provides a more comprehensive understanding of the composition and sources of PM2.5 in the three cities, with a focus on the differences in their atmospheric pollution profiles based on the UPLC UHR-MS and ANN analysis. It is notable that this study is the first to utilize this method on a large-scale sample set, providing a more detailed and molecular-level understanding of the compositional differences among PM2.5. Overall, the study contributes to a better understanding of the sources and composition of PM2.5 in Northeast Asia, which is essential for developing effective strategies to reduce air pollution and improve public health.

Surface Enhancement Effects of Tiny SnO2 Nanoparticle Modification on α-Fe2O3 for Room-Temperature NH3 Sensing.

The development of a gas sensor capable of detecting ammonia with high selectivity and rapid response at room temperature has consistently posed a formidable challenge. To address this issue, the present study utilized a one-step solvothermal method to co-assemble α-Fe2O3 and SnO2 by evenly covering SnO2 nanoparticles on the surface of α-Fe2O3. By controlling the morphology and Fe/Sn mole ratio of the composite, the as-prepared sample exhibits high-performance detection of NH3. At room temperature conditions, a gas sensor composed of α-Fe2O3@3%SnO2 demonstrates a rapid response time of 14 s and a notable sensitivity of 83.9% when detecting 100 ppm ammonia. Experiments and density functional theory (DFT) calculations suggest that the adsorption capacity of α-Fe2O3 to ammonia is enhanced by the surface effect provided by SnO2. Meanwhile, the existence of SnO2 tailors the pore structure and effective surface area of α-Fe2O3, creating multiple channels for the diffusion and adsorption of ammonia molecules. Additionally, an N-N heterostructure is formed between α-Fe2O3 and SnO2, which enhances the potential energy barrier and improves the ammonia sensing performance. Demonstration experiments have proved that the sensor shows significant advantages over commercial sensors in the process of ammonia detection in agricultural facilities. This work provides new insights into the perspectives on ammonia detection at room temperature.

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family.

Introduction: Insect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy. Methods: To address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters. Results: Experimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%. Discussion: Our model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment.