Clinical Significance of Elevated S100A8 Expression in Breast Cancer Patients

Breast cancer is the leading cause of female cancer-related death; however, novel biomarkers for predicting cancer recurrence still need to be explored. Aberrant expression of S100A8 has been reported to be related to tumor progression in various cancer types. This study aims to evaluate the clinical significance of S100A8 expression in breast cancer patients. In this study, data from 140 breast cancer patients were retrospectively collected to examine the association between S100A8 expression and clinical prognosis. Increased S100A8 expression was detected in breast cancer patients with relapse. The patients with increased S100A8 levels had significantly shorter disease-free survival (DFS) and overall survival (OS). In a multivariate survival analysis, a high histological grade and an elevated S100A8 level were independent factors associated with poor DFS and OS. Moreover, S100A8 expression was correlated with clinical subtype in breast cancer patients. The results showed that ER-negative and triple-negative breast cancer (TNBC) patients had significantly higher expression of S100A8 than patients with other subtypes. In conclusion, this study identified S100A8 as a potential biomarker for relapse in breast cancer patients

Research Progress on Flexibility Modification of Coal-Fired Generating Units

[Introduction] The strategic goal of "carbon peaking and carbon neutrality" has increased the demand for new energy power grid integration, so it is necessary to improve the flexible operation capacity of coal-fired generating units. [Method] This paper introduced the flexible transformation technology and common evaluation indicators of existing coal-fired units in detail. Flexibility transformation mainly included condensate throttling technology, coupled biomass co-combustion transformation technology of coal-fired units, flexibility technology of pulverizing system of coal-fired units, etc.; Common evaluation indicators for the flexibility of coal-fired generating units included: the power consumption rate of the generator set plant, the thermal efficiency of the boiler, the standard coal consumption rate of the generator set, etc. On this basis, this paper summarized and analyzed the flexibility transformation technology and evaluation indexes. [Result] Finally, the paper puts forward seven kinds of the technology development direction and relevant suggestions of coal-fired generating units. [Conclusion] The structural improvement of original generating units, the introduction of multi-form new energies and the coordinated regulation design of multi-heat storage will be the main direction of subsequent development. It is expected that paper can provide reference for subsequent coal-fired generating units to adapt to "dual carbon" energy planning

Polar-facing slopes showed stronger greening trend than equatorial-facing slopes in Tibetan plateau grasslands

The orientation of slopes in alpine zones creates microclimates, e.g. equatorial-facing slopes (EFSs) are generally drier and warmer than are polar-facing slopes (PFSs). The vegetation growing in these microhabitats responds divergently to climatic warming depending on the slope orientation. We proposed a spatial metric, the greenness asymmetric index (GAI), defined as the ratio between the average normalized difference vegetation index (NDVI) on PFSs and EFSs within a given spatial window, to quantify the asymmetry of greenness across aspects. We calculated GAI for each non-overlapping 3 × 3 km2 (100 × 100 Landsat pixels) grid, and seamlessly mapped it on Tibetan Plateau (TP) grassland using NDVI time series from the Landsat-5, -7 and -8 satellites. PFSs were greener than EFSs (GAI > 1) in warm and dry areas, and EFSs were greener than PFSs (GAI < 1) in cold and wet areas. We also detected a stronger greening trend (0.0040 vs 0.0034 y−1) and a higher sensitivity of NDVI to temperature (0.031 vs 0.026 °C−1) on PFSs than EFSs, leading to a significant positive trend in GAI (0.00065 y−1, P < 0.01) in the TP from 1991 to 2020. Our results suggest that global warming exacerbated the greenness asymmetry associated with the slope orientation: PFSs are more sensitive to warming and have been greening at a faster rate than EFSs. The gradient of EFSs and PFSs provided a “natural laboratory” to study interaction of water and temperature limitations on vegetation growth. Our study is the first to detect the effect of aspect on the greening trend in the TP. Future research needs to clarify the full biotic and abiotic determinants for this spatial and temporal asymmetry of greenness across aspects with the support of extensive field measurements and refined high-resolution NDVI products.This study was funded by the National Natural Science Foundation of China 42271323 and 41971282, the Sichuan Science and Technology Program 2021JDJQ0007, the Spanish Government project TED2021-132627B-I00 funded by the Spanish MCIN, AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR, the Fundación Ramón Areces project CIVP20A6621 and the Catalan government project SGR2021-1333.N

Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R2 = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R2 = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions

Comparison of Remote Sensing Time-Series Smoothing Methods for Grassland Spring Phenology Extraction on the Qinghai–Tibetan Plateau

Accurate evaluation of start of season (SOS) changes is essential to assess the ecosystem&rsquo;s response to climate change. Smoothing method is an understudied factor that can lead to great uncertainties in SOS extraction, and the applicable situation for different smoothing methods and the impact of smoothing parameters on SOS extraction accuracy are of critical importance to be clarified. In this paper, we use MOD13Q1 normalized difference vegetation index (NDVI) data and SOS observations from eight agrometeorological stations on the Qinghai&ndash;Tibetan Plateau (QTP) during 2001&ndash;2011 to compare the SOS extraction accuracies of six popular smoothing methods (Changing Weight (CW), Savitzky-Golay (SG), Asymmetric Gaussian (AG), Double-logistic (DL), Whittaker Smoother (WS) and Harmonic Analysis of NDVI Time-Series (HANTS)) for two types of different SOS extraction methods (dynamic threshold (DT) with 9 different thresholds and double logistic (Zhang)). Furthermore, a parameter sensitivity analysis for each smoothing method is performed to quantify the impacts of smoothing parameters on SOS extraction. Finally, the suggested smoothing methods and reference ranges for the parameters of different smoothing methods were given for grassland phenology extraction on the QTP. The main conclusions are as follows: (1) the smoothing methods and SOS extraction methods jointly determine the SOS extraction accuracy, and a bad denoising performance of smoothing method does not necessarily lead to a low SOS extraction accuracy; (2) the default parameters for most smoothing methods can result in acceptable SOS extraction accuracies, but for some smoothing methods (e.g., WS) a parameter optimization is necessary, and the optimal parameters of the smoothing method can increase the R2 and reduce the RMSE of SOS extraction by up to 25% and 331%; (3) The main influencing factor of the SOS extraction using the DT method is the stability of the minimum value in the NDVI curve, and for the Zhang method the curve shape before the peak of the NDVI curve impacts the most; (4) HANTS is the most stable method no matter with (fitness = 35.05) or without parameter optimization (fitness = 33.52), which is recommended for QTP grassland SOS extraction. The findings of this study imply that remote sensing-based vegetation phenology extraction can be highly uncertain, and a careful selection and parameterization of the time-series smoothing method should be taken to achieve an accurate result