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A retrospective analysis was conducted on 58 patients with laryngeal and hypopharyngeal tumors and 27 patients with benign vocal cord lesions to explore the role of serum cytokines in these diseases’ characteristics and immunotherapy. The differences in the levels of 12 cytokines were measured. Additionally, the study examined the correlation between T helper cells (Th)1/Th2 cytokine levels and the clinical characteristics and immunotherapy efficacy of laryngeal and hypopharyngeal cancers. The results show that the balance of Th1/Th2 is biased towards Th2 in patients with laryngeal and hypopharyngeal tumors. Among these, interleukin (IL)-6 (P = 0.021) was highly expressed in laryngeal tumors, and the expression levels of IL-1β (P = 0.008), IL-6 (P = 0.005), and IL-8 (P = 0.05) were higher in patients with poorly differentiated laryngeal tumors. The level of IL-4 (P = 0.0048) was significantly correlated with tumor location. The expression levels of IL-2 (P = 0.010), IL-4 (P = 0.028), IL-10 (P = 0.011), IL-12p70 (P = 0.034), IL-17 (P = 0.024), tumor necrosis factor (TNF)-α (P = 0.003), and interferon (IFN)-γ (P = 0.007) were related to lymph node metastasis. The level of IFN-γ (P = 0.016) was correlated with the efficacy of neoadjuvant therapy, while the level of IFN-α (P = 0.013) was significantly correlated with programmed death ligand 1 (PD-L1) expression. The Principal Component Analysis (PCA) results showed that patients with tumors, poor differentiation, and lymph node metastasis had higher levels of Th1 and Th2 cytokine separation. In conclusion, the shift in the balance of Th1 and Th2 cytokine expression indicates higher tumor invasiveness, and IFN has potential as a circulating molecular marker for immunotherapy of head and neck squamous cell carcinoma.

Beef production is the primary contributor to greenhouse gas emissions in animal husbandry. But the carbon emission distribution, potential for carbon reduction, and pathways in the main links of the beef cattle industry chain are not yet clear. Balancing efficiency and fairness while taking tailored emission reduction paths according to local conditions is crucial in helping to achieve the low-carbon animal husbandry and "dual carbon" goals. This research adopts an industry chain perspective to assess four leading beef production regions' carbon emissions, potential, and pathways in China from 2007 to 2021. We use life cycle assessment, the potential index which considers efficiency and fairness; the results are as follows: (1) The potential for carbon emission reduction in China's beef industry showed an inverted U-shaped change trend from 2007 to 2021, and the average potential for carbon emission reduction in most provinces fluctuated around 0.500. (2) The main producing areas of China's beef industry were divided into four areas of high efficiency and low fairness (Area A); high efficiency and high fairness (Area B); low efficiency and low fairness (Area C); and low efficiency and high fairness (Area D). (3) Differentiated emission reduction pathways were designed to reduce emissions, highlighting the need to implement region-specific carbon reduction policies. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, extensive validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications in the regions of optical remote sensing. Reprocessing MODIS LAI predominantly relies on temporal information to achieve smoother LAI profiles with little use of spatial information and may easily ignore genuine LAI anomalies. To address these problems, we designed the spatiotemporal information compositing algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatiotemporal correlation, and the original retrieval, thereby enabling both "reprocessing" and "value-added data" with respect to the existing MODIS LAI products, leading to the development of the high-quality LAI (HiQ-LAI) dataset. Compared with ground measurements, HiQ-LAI shows better performance than the original MODIS product with a root-mean-square error (RMSE) or bias decrease from 0.87 or -0.17 to 0.78 or -0.06 , respectively. This is due to the improvement of HiQ-LAI with respect to capturing the seasonality in vegetation phenology and reducing abnormal time-series fluctuations. The time-series stability (TSS) index, which represents temporal stability, indicated that the area with smooth LAI time series expanded from 31.8 % (MODIS) to 78.8 % (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing cannot usually achieve good performance. We found that HiQ-LAI demonstrates superior continuity and consistency compared with raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time series, generated using the STICA procedure on the Google Earth Engine (GEE) platform, will substantially enhance support for diverse global LAI time-series applications. The 5 km 8 d HiQ-LAI datasets from 2000 to 2022 are available at 10.5281/zenodo.8296768 (Yan et al., 2023). [ABSTRACT FROM AUTHOR]