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To fully understand the market price volatility mechanism of natural resource commodities (NRCs), and control the price risks of NRCs, it is important to deeply analyze how NRC price is affected by uncertain risk factors, such as supply risk, demand risk, macroscopic price risk, political price risk, policy price risk, seasonal price risk, and sudden price risks. The theory of behavioral finance can establish a transaction decision-making model more in line with the actual market situation. Therefore, this paper explores the price risk control of NRCs from the perspective of financial theories. Firstly, the authors empirically examined the specific impacts of uncertain risk factors on NRC price, and the directions of the impacts. Next, the price volatility and transaction risks of NRCs brought by information transmission were analyzed, following by the construction of an information transmission model for NRC trading market. After that, a dynamic price model was built for NRCs based on the theory of behavioral finance. Finally, the effectiveness of the proposed model was verified through experiments.

Excess reactive nitrogen (Nr), including nitrogen oxides (NO x ) and ammonia (NH 3 ), contributes strongly to fine particulate matter (PM 2.5 ) air pollution in Europe, posing challenges to public health. Designing cost-effective Nr control roadmaps for PM 2.5 mitigation requires considering both mitigation efficiencies and implementation costs. Here we identify optimal Nr control pathways for Europe by integrating emission estimations, air quality modeling, exposure-mortality modeling, Nr control experiments and cost data. We find that phasing out Nr emissions would reduce PM 2.5 by 2.3 ± 1.2 μg·m −3 in Europe, helping many locations achieve the World Health Organization (WHO) guidelines and reducing PM 2.5 -related premature deaths by almost 100 thousand in 2015. Low-ambition NH 3 controls have similar PM 2.5 mitigation efficiencies as NO x in Eastern Europe, but are less effective in Western Europe until reductions exceed 40%. The efficiency for NH 3 controls increases at high-ambition reductions while NO x slightly decreases. When costs are considered, strategies for both regions uniformly shift in favor of NH 3 controls, as NH 3 controls up to 50% remain 5-11 times more cost-effective than NO x per unit PM 2.5 reduction, emphasizing the priority of NH 3 control policies for Europe. Reactive nitrogen (Nr) contributes strongly to PM 2.5 air pollution in Europe. Here, authors identify diverse Nr control pathways for Europe depending on emission and pollution formation and a priority of NH 3 control when costs are considered.

It is imperative to enhance the scientific management of logistics through the creation and advancement of an Intelligent Logistics Management in Smart Hospitals. This paper presents a preliminary introduction to an intelligent logistics management system. The subsequent section offers a comprehensive overview of the diverse platforms that constitute the intelligent logistics management system. These include the energy management platform, intelligent lighting control platform, one-stop service platform, power operation and maintenance monitoring platform, and the BIM O&M platform, the latter of which is visualised. Furthermore, it provides a comprehensive account of the construction, architectural design, and the functions and responsibilities of the constituent sub-platforms. Furthermore, a thorough examination is conducted to ascertain the substantial efficacy and energy-saving impact of implementing an intelligent logistics management system within the context of a hospital project in Shenzhen, China. The findings indicate a substantial reduction in the energy consumption of the entire building structure, with the maximum total energy consumption reduced by 402 MWh, signifying an 18.5% decline. The system has been demonstrated to reduce operational costs and facilitate environmentally conscious operations, which represents a core objective. The construction of the logistics operation and maintenance platform serves to enhance the efficiency of integrated logistics management, as well as the degree of management refinement. The management system, which employs information technology, is an effective tool for the oversight and enhancement of logistics management. Furthermore, the system provides logistical support for the construction of an environmentally sustainable hospital.

PRRSV is a major swine pathogen, suppresses innate immunity, and causes persistent infection and coinfection with other pathogens. As a central immune mediator, MALT1 plays essential roles in regulating immunity and inflammation. To fulfill virus replication and persistent infection in hosts, viruses have to find ways to compromise innate immunity, including timely impedance on antiviral RNases and inflammatory responses. Porcine reproductive and respiratory syndrome virus (PRRSV) is a major swine pathogen causing immune suppression. MALT1 is a central immune regulator in both innate and adaptive immunity. In this study, MALT1 was confirmed to be induced rapidly upon PRRSV infection and mediate the degradation of two anti-PRRSV RNases, MCPIP1 and N4BP1, relying on its proteolytic activity, consequently facilitating PRRSV replication. Multiple PRRSV nsps, including nsp11, nsp7β, and nsp4, contributed to MALT1 elicitation. Interestingly, the elevated expression of MALT1 began to decrease once intracellular viral expression reached a high enough level. Higher infection dose brought earlier MALT1 inflection. Further, PRRSV nsp6 mediated significant MALT1 degradation via ubiquitination-proteasome pathway. Downregulation of MALT1 suppressed NF-κB signals, leading to the decrease in proinflammatory cytokine expression. In conclusion, MALT1 expression was manipulated by PRRSV in an elaborate manner to antagonize precisely the antiviral effects of host RNases without excessive and continuous activation of inflammatory responses. These findings throw light on the machinery of PRRSV to build homeostasis in infected immune system for viral settlement. IMPORTANCE PRRSV is a major swine pathogen, suppresses innate immunity, and causes persistent infection and coinfection with other pathogens. As a central immune mediator, MALT1 plays essential roles in regulating immunity and inflammation. Here, PRRSV was confirmed to manipulate MALT1 expression in an accurate way to moderate the antiviral immunity. Briefly, multiple PRRSV nsps induced MALT1 protease to antagonize anti-PRRSV RNases N4BP1 and MCPIP1 upon infection, thereby facilitating viral replication. In contrast, PRRSV nsp6 downregulated MALT1 expression via ubiquitination-proteasome pathway to suppress the inflammatory responses upon infection aggravation, contributing to immune defense alleviation and virus survival. These findings revealed the precise expression control on MALT1 by PRRSV for antagonizing antiviral RNases, along with recovering immune homeostasis. For the first time, this study enlightens a new mechanism of PRRSV adapting antiviral innate immunity by modulating MALT1 expression.

Recent Chinese air pollution actions have significantly lowered the levels of fine particulate matter (PM2.5) in North China via controlling emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) together with primary aerosols, while the emissions of another precursor, ammonia (NH3), have not yet been regulated. This raises a question that how effective the NH3 emission controls can be on the mitigation of PM2.5 pollution along with the reduction of SO2 and NOx emissions. Here we use a regional air quality model to investigate this issue focusing on the PM2.5 pollution in North China for January and July 2015. We find that the efficiency of the PM2.5 reduction is highly sensitive to the NH3 emission and its reduction intensity. Reductions in the population-weighted PM2.5 concentration (PWC) in the Beijing-Tianjin-Hebei region are only 1.4-3.8 μg m−3 (1.1%-2.9% of PM2.5) with 20%-40% NH3 emission reductions, but could reach 8.1-26.7 μg m−3 (6.2%-21%) with 60%-100% NH3 emission reductions in January 2015. Besides, the 2015-2017 emission changes (mainly reduction in SO2 emissions) could lower the PM2.5 control efficiency driven by the NH3 reduction by up to 30% for high NH3 emission conditions, while lead to no change or increase in the efficiency when NH3 emissions become low. NOx emission reductions may enhance the wintertime PM2.5 pollution due to the weakened titration effect and can be offset by simultaneously controlling NH3 emissions. Our results emphasize the need to jointly consider NH3 with SO2 and NOx emission controls when designing PM2.5 pollution mitigation strategies.

Substantial forestation-induced greening has occurred over South China, affecting the terrestrial carbon storage and atmospheric chemistry. However, these effects have not been systematically quantified due to complex biosphere-atmosphere interactions. Here we integrate satellite observations, forestry statistics, and an improved atmospheric chemistry model to investigate the impacts of forestation on both carbon storage and ozone air quality. We find that forestation alleviates surface ozone via enhanced dry deposition and suppressed turbulence mixing, outweighing the effect of enhanced biogenic emissions. The 2005-2019 greening mitigated the growing season mean surface ozone by 1.4 ± 2.3 ppbv, alleviated vegetation exposure by 15%-41% (depending on ozone metrics) in forests over South China, and increased Chinese forest carbon storage by 1.8 (1.6-2.1) Pg C. Future forestation may enhance carbon storage by 4.3 (3.8-4.8) Pg C and mitigate surface ozone over South China by 1.4 ± 1.2 ppbv in 2050. Air quality management should consider such co-benefits as forestation becomes necessary for carbon neutrality. Forestation can aid both carbon neutrality and air quality. Here, the authors show that forestation in South China increases biomass carbon storage and improves surface ozone air quality by increasing dry deposition and reducing turbulence.

Liver cancer, classified as a malignant hepatic tumor, can be divided into two categories: primary, originating within the liver, and secondary, resulting from metastasis to the liver from other organs. Hepatocellular carcinoma (HCC) is the main form of primary liver cancer and the third leading cause of cancer-related deaths. The diagnosis and prognosis of HCC using current methods still face numerous challenges. This study aims to develop novel diagnostic and prognostic models while identifying new biomarkers for improved HCC treatment. Diagnostic and prognostic models for HCC were constructed using traditional binary classification methods and machine learning algorithms based on the TCGA database (Downloaded in August 2023). The mechanisms by which APLN (Apelin) affects HCC were investigated using single-cell sequencing data sourced from the GEO database (GSE149614). The diagnostic models yielded by various algorithms could effectively distinguished HCC samples from normal ones. The prognostic model, composed of four genes, was constructed using LASSO and Cox regression algorithms, demonstrating good performance in predicting the three-year survival rate of HCC patients. The HCC biomarker Apelin (APLN) was identified in this study. APLN in liver cancer tissues mainly comes from endothelial cells and is associated with the carcinogenesis of these cells. APLN expression is significantly upregulated in liver cancer tissues, marking it as a viable indicator of endothelial cell malignancy in HCC. Furthermore, APLN expression was determined to be an independent predictor of tumor endothelial cell carcinogenesis, unaffected by its modifications such as single nucleotide variation, copy number variation, and methylation. Additionally, liver cancers characterized by high APLN expression are likely to progress rapidly after T2 stage. Our study presents diagnostic and prognostic models for HCC with appreciably improved accuracy and reliability compared to previous reports. APLN is a reliable HCC biomarker and contributes to the establishment of our models.

The epidermal growth factor receptor (EGFR) is frequently overexpressed in a variety of human epithelial tumors, and its aberrant activation plays a pivotal role in promoting tumor growth, invasion, and metastasis. The clinically approved passive EGFR-related therapies have numerous limitations. Seven EGFR-ECD epitope peptides (EG1-7) were selected through bioinformatics epitope prediction tools including NetMHCpan-4.1, NetMHCIIpan-3.2, and IEDB Consensus (v2.18 and v2.22) and fused to the translocation domain of diphtheria toxin (DTT). The A549 tumor model was successfully established in a murine mouse model. The vaccine was formulated by combining the adjuvants Alum and CpG and subsequently assessed for its immunogenicity and anti-tumor efficacy. DTT-EG (3;5;6;7) vaccines elicited specific humoral and cellular immune responses and effectively suppressed tumor growth in both prophylactic and therapeutic mouse tumor models. The selected epitopes EG3 (HGAVRFSNNPALCNV145-159), EG5 (KDSLSINATNIKHFK346-360), EG6 (VKEITGFLLIQAWPE398-412), and EG7 (LCYANTINWKKLFGT469-483) were incorporated into vaccines for active immunization, representing a promising strategy for the treatment of tumors with overexpressed epidermal growth factor receptor (EGFR). The vaccine design and fusion method employed in this study demonstrate a viable approach toward the development of cancer vaccines.

With the increasing prevalence of electric vehicles (EVs), optimizing their scheduling for grid peak-shaving has become a focal point of research. This study develops a multi-time-scale optimization model for EV clusters to participate in peak shaving, integrating a comprehensive evaluation and feedback mechanism. The innovation of this paper lies in the addition of an evaluation and feedback loop to the multi-time-scale scheduling optimization method for EVs participating in peak shaving, which fully utilizes the scheduling potential of EV clusters and mitigates the impact of uncertainties associated with EV clusters. The multi-time-scale approach mitigates response errors stemming from EV uncertainties. A feedback loop enables the grid to adaptively adjust scheduling commands to match real-time conditions. Simulations on the IEEE 33-node system demonstrate that the proposed model effectively optimizes EV load profiles, reducing the peak-to-valley difference rate from 41.74% to 35.19%. It also enhances response accuracy to peak-shaving instructions and upgrades the peak-shaving evaluation from a C rating to a B rating, ultimately increasing the revenue for aggregators participating in peak shaving.

Determining the optimal vegetation cover threshold in a region for facilitating both high levels of ecosystem services (ESs) supply and synergistic sustainable development among different ESs is crucial. This study delineated the nonlinear relationship between the fractional vegetation cover (FVC) and the integrated synergy–supply capacity of ESs in Ji County, on China’s Loess Plateau (2000–2023). The FVC was quantified using Landsat remote sensing data. Assessments of carbon storage, soil conservation, water conservation, and habitat quality were conducted based on multi-source remote sensing datasets and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, which subsequently informed the evaluation of the integrated synergy–supply capacity of ESs. Spatial–temporal distribution characteristics were assessed via trend analysis methods and the spatial correlation relationship was assessed via bivariate local spatial autocorrelation analysis. The constraint line analysis and the restricted cubic spline method were combined to analyze the nonlinear relationship between the two and to quantify the FVC threshold. The results revealed that the spatial distribution of both the FVC and the integrated synergy–supply capacity of ESs was higher in the north, with a growth trend observed respectively. A highly significant positive spatial correlation existed between the two (Moran’s I > 0.6520, p < 0.01), dominated by the High–High agglomeration type (55.71%). The relationship between the regional FVC and the ISSC of ESs, the forest land FVC and the ISSC of ESs, and the grassland FVC and the ISSC of ESs all exhibited a positive convex function constraint line. The regional FVC threshold was 0.5, the forest land FVC threshold was 0.28, and the grassland FVC threshold was 0.77. When the FVC value was above the threshold, its facilitating effect on the ISSC of ESs diminished. This study advances vegetation threshold research by integrating the supply levels and synergy degrees of multiple ESs, providing a scientific foundation for formulating strategies for regional ecological restoration and adaptive management, and offering a reference for high-quality vegetation restoration in global arid, semi-arid, and erosion-prone regions.