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The China Meteorological Forcing Dataset (CMFD) is the first high spatial-temporal resolution gridded near-surface meteorological dataset developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis datasets and in-situ station data. Its record begins in January 1979 and is ongoing (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in the CMFD, including 2-meter air temperature, surface pressure, and specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate. Validations against observations measured at independent stations show that the CMFD is of superior quality than the GLDAS (Global Land Data Assimilation System); this is because a larger number of stations are used to generate the CMFD than are utilised in the GLDAS. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely-used climate datasets for China. Measurement(s) temperature • pressure • humidity • atmospheric wind speed • radiation • precipitation process Technology Type(s) digital curation Factor Type(s) geographic location • time Sample Characteristic - Environment climate system Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11558439

Natural killer cells (NK cells) play an important role in innate immunity. NK cells recognize self and nonself depending on the balance of activating receptors and inhibitory receptors. After binding to their ligands, NK cell receptors trigger subsequent signaling conduction and then determine whether NK is activated or inhibited. Furthermore, NK cell response includes cytotoxicity and cytokine release, which is tightly related to the activation of NK cell-activating receptors and the inhibition of inhibitory receptors on the surfaces of NK cells. The expression and function of NK cell surface receptors also alter in virus infection, tumor, and autoimmune diseases and influence the occurrence and development of diseases. So, it is important to understand the mechanism of recognition between NK receptors and their ligands in pathological conditions and the signaling pathways of NK cell receptors. This review mainly summarizes the research progress on NK cell surface receptors and their signal pathways.

The challenges of increasing environmental contamination and scarcity of natural resources require innovative solutions to ensure a sustainable future. Recent breakthroughs in synthetic biology and protein engineering provide promising platform technologies to develop novel engineered biological materials for beneficial applications towards environmental sustainability. In particular, biocatalysis and biosorption are receiving increasing attention as sustainable approaches for environmental remediation and resource recovery from wastes. This review focuses on up-to-date advances in engineering biocatalytic and biosorptive materials that can degrade persistent organic contaminants of emerging concern, remove hazardous metal pollutants, and recover value-added metals. Opportunities and challenges for future research are also discussed. Advances in synthetic biology and protein engineering provide promising platform technologies for engineering renewable biomaterials with biocatalytic or biosorptive capabilities to address the crucial challenges of environmental pollution and natural resource scarcity. Biocatalytic materials that harness the function of various biological enzymes to degrade recalcitrant contaminants to environmentally benign compounds can be developed using molecular biology techniques and physicochemical processes; these materials demonstrate great potential for environmental remediation applications. Biosorptive materials engineered by decorating microbial cells with specific metal-binding proteins/peptides using genetic engineering can be used for hazardous metal removal and valuable metal recovery with high selectivity and binding efficiency.

The serum uric acid-to-creatinine ratio (UCR) may be a simple method for assessing xanthine oxidase overactivation, which may contribute to an increase in serum uric acid production and oxidative stress. In this study, we investigated the nonlinear association between the UCR and long-term mortality in patients with hypertension. Data were acquired from the National Health and Nutrition Examination Survey database, and a total of 11,346 patients with hypertension were included. We explored the nonlinear link between the UCR and all-cause mortality via spline smoothing, threshold saturation, and log-likelihood ratio tests. The results were validated through a competing risk model. A nonlinear pattern emerged between the UCR and all-cause mortality in hypertensive patients, with an inflection point at 4.3. Below this point, an increased UCR was associated with a decreased mortality risk ( OR  = 0.80, 95% CI : 0.68–0.94, P  = 0.008), whereas above this point, the risk increased ( OR  = 1.21, 95% CI : 1.07–1.36, P  = 0.004). The competing risk model yielded similar findings for cardiovascular and chronic kidney disease-related deaths. In patients with hypertension, the UCR nonlinearly predicted all-cause mortality, with a notable inflection at 4.3. These findings suggest that the UCR is a valuable prognostic indicator for assessing long-term outcomes in patients with hypertension.

Current climate models often have significant wet biases in the Tibetan Plateau and encounter particular difficulties in representing the climatic effect of the Central Himalaya Mountain (CHM), where the gradient of elevation is extremely steep and the terrain is complex. Yet, there were few studies dealing with the issue in the high altitudes of this region. In order to improve climate modeling in this region, a network consisting of 14 rain gauges was set up at elevations > 2800 m above sea level along a CHM valley. Numerical experiments with Weather Research and Forecasting model were conducted to investigate the effects of meso- and micro-scale terrain on water vapor transport and precipitation. The control case uses a high horizontal resolution (0.03°) and a Turbulent Orographic Form Drag (TOFD) scheme to resolve the mesoscale terrain and to represent sub-grid microscale terrain effect. The effects of the horizontal resolution and the TOFD scheme were then analyzed through comparisons with sensitivity cases that either use a low horizontal resolution (0.09°) or switch off the TOFD scheme. The results show that the simulations with high horizontal resolution, even without the TOFD scheme, can not only increase the spatial consistency (correlation coefficient 0.84–0.92) between the observed and simulated precipitation, but also considerably reduce the wet bias by more than 250%. Adding the TOFD scheme further reduces the precipitation bias by 50% or so at almost all stations in the CHM. The TOFD scheme reduces precipitation intensity, especially heavy precipitation (> 10 mm h −1 ) over high altitudes of the CHM. Both high horizontal resolution and TOFD enhance the orographic drag to slow down wind; as a result, less water vapor is transported from lowland to the high altitudes of CHM, causing more precipitation at lowland area of the CHM and less at high altitudes of CHM. Therefore, in this highly terrain-complex region, it is crucial to use a high horizontal resolution to depict mesoscale complex terrain and a TOFD scheme to parameterize the drag caused by microscale complex terrain.

The parameterization of thermal roughness length z0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry‐land areas. The first improvement is the replacement of the model's z0h scheme with a new one. A previous study has validated the revised Noah model at several dry‐land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer‐ (MODIS‐) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM. Key Points Improved modeling of land surface temperature in dry land of China Use of newly developed forcing data Improved modeling of land surface energy budget

The calcium-binding protein S100A9 has emerged as a pivotal biomolecular actor in oncology, implicated in numerous malignancies. This comprehensive bioinformatics study transcends traditional boundaries, investigating the prognostic and therapeutic potential of S100A9 across diverse neoplastic entities. Leveraging a wide array of bioinformatics tools and publicly available cancer genomics databases, such as TCGA, we systematically examined the S100A9 gene. Our approach included differential expression analysis, mutational burden assessment, protein interaction networks, and survival analysis. This robust computational framework provided a high-resolution view of S100A9’s role in cancer biology. The study meticulously explored S100A9’s oncogenic facets, incorporating comprehensive analyses of its relationship with prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), DNA methylation, and immune cell infiltration across various tumor types. This study presents a panoramic view of S100A9 expression across a spectrum of human cancers, revealing a heterogeneous expression landscape. Elevated S100A9 expression was detected in malignancies such as BLCA (Bladder Urothelial Carcinoma), CESC (Cervical squamous cell carcinoma and endocervical adenocarcinoma), COAD (Colon adenocarcinoma), ESCA (Esophageal carcinoma), and GBM (Glioblastoma multiforme), while reduced expression was noted in BRCA (Breast invasive carcinoma), HNSC (Head and Neck squamous cell carcinoma), and KICH (Kidney Chromophobe). This disparate expression pattern suggests that S100A9’s role in cancer biology is multifaceted and context-dependent. Prognostically, S100A9 expression correlates variably with patient outcomes across different cancer types. Furthermore, its expression is intricately associated with TMB and MSI in nine cancer types. Detailed examination of six selected tumors—BRCA, CESC, KIRC (Kidney renal clear cell carcinoma), LUSC (Lung squamous cell carcinoma), SKCM (Skin Cutaneous Melanoma); STAD (Stomach adenocarcinoma)—revealed a negative correlation of S100A9 expression with the infiltration of most immune cells, but a positive correlation with neutrophils, M1 macrophages, and activated NK cells, highlighting the complex interplay between S100A9 and the tumor immune environment. This bioinformatics synthesis posits S100A9 as a significant player in cancer progression, offering valuable prognostic insights. The data underscore the utility of S100A9 as a prognostic biomarker and its potential as a therapeutic target. The therapeutic implications are profound, suggesting that modulation of S100A9 activity could significantly impact cancer management strategies.

Financial stability is of great importance especially in the context of achieving sustainable environment. The objective of this study is to fill the research gap in this area by introducing financial stability, international trade, renewable energy, and income as novel determinants of consumption-based carbon emissions. The present study is based on G-7 economies, and the time period is from 1990 to 2018. The present study employed advanced econometric techniques that can deal with problems of slope homogeneity and cross-section dependence. The cointegration analysis results show a stable long-run association between financial stability, renewable energy, international trade, national income, and consumption-based carbon emissions with structural breaks (1994 Italy’s fiscal crises, 2001 mild recession, 2008 global financial crises, and 2010 European debt crises). The results show that both in long- and short-run financial stability, exports and renewable energy significantly reduce carbon emissions. In contrast, national income and imports are found to have a significant positive effect on consumption-based carbon emissions. Policymakers in G-7 countries should focus more on financial sector stability and encourage firms to use renewable energy. Any policy that targets financial stability, exports, and renewable energy will significantly reduce carbon emissions. This study is a novel contribution to the area of consumption-based carbon emissions as it incorporates the role of financial stability for G-7 economies.

Objective Several inflammatory indices have been used to assess the prognosis of ovarian cancer, with variable results. This review assessed whether the systemic immune inflammation index (SII) can predict outcomes in patients with ovarian cancer. Methods Embase, PubMed, CENTRAL, Web of Science, and Scopus databases were searched by the two reviewers from inception to 15th October 2024 for studies assessing the relationship between SII and overall survival (OS) or disease-free survival (DFS). Results Ten studies with eleven cohorts were included. Pooled analysis showed that higher SII was a significant predictor of poor OS (HR: 2.35 95% CI: 1.56, 3.55 I 2  = 88%) and worse DFS (HR: 2.51 95% CI: 1.71, 3.67 I 2  = 80%) after ovarian cancer. Sensitivity analysis failed to change the significance of the results. No publication bias was noted. Most results remained significant on subgroup analyses based on location, sample size, FIGO stage, treatment, adjusted outcomes, cut-off of SII, method of determining cut-off, and quality score. Conclusions SII can be a potential predictor of OS and DFS after ovarian cancer. Further studies are required to improve the evidence.

Porphyrin (TMPyP) functionalized carbon quantum dots (CQDs-TMPyP), a novel and efficient carbon nanocomposite material, were developed as a novel luminescent material, which could be very useful for the sensitive detection of copper ions in the Cu2+ quenching luminescence of functionalized carbon quantum dots. Therefore, we constructed a sensitive “signal off” ECL biosensor for the detection of Cu2+. This sensor can sensitively respond to copper ions in the range of 10 nM to 10 μM, and the detection limit is 2.78 nM. At the same time, it has good selectivity and stability and a benign response in complex systems. With excellent properties, this proposed ECL biosensor provides an efficient and ultrasensitive method for Cu2+ detection.