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PurposeThis study aims to investigate the glymphatic system activity changes in patients with mild traumatic brain injury (mTBI), particularly in MRI-negative patients, using analysis along the perivascular space (ALPS) technology.MethodsA total of 161 mTBI patients (age: 15–92 years old) and 28 healthy controls (age: 15–84 years old) were included in this retrospective study. The mTBI patients were divided into MRI-negative and MRI-positive groups. ALPS index was calculated automatically using whole-brain T1-MPRAGE imaging and diffusion tensor imaging. The Student's t and chi-squared tests were performed to compare the ALPS index, age, gender, course of disease, and Glasgow Coma Scale (GCS) score between groups. Correlations among ALPS index, age, course of disease and GCS score were computed using Spearman's correlation analysis.ResultsIncreased activity of the glymphatic system was suggested in mTBI patients based on ALPS index analysis, including the MRI-negative patients. There was a significant negative correlation between the ALPS index and age. In addition, a weak positive correlation between the ALPS index and course of disease was also observed. On the contrary, there was no significant correlation between the ALPS index and sex nor between the ALPS index and GCS score.ConclusionOur study demonstrated that the activity level of the glymphatic system was enhanced in mTBI patients, even when their brain MRI scans were negative. These findings may provide novel insights for understanding the pathophysiology of mild TBI.

O3 pollution in China has been increasing in recent years, but the process of O3 impact on net primary productivity of terrestrial ecosystems remains unclear. We attempts to explore a remote sensing-based method to assess the impact of O3 on NPP of China’s terrestrial ecosystems by combining MODIS NPP and the latest ground observation data of O3 concentration. By comparing the NPP data of MODIS image pixels with the 6-year average AOT40 data of corresponding pixels, we extracted the signal data that highlighted the effects of O3 on NPP and established the response relationships between AOT40 and NPP. It was found that NPP was significantly negatively correlated with AOT40 in farmland and grassland ecosystems in China (farmland: r = 0.8674, p < 0.003, grassland: r = 0.7181, p < 0.03). Then the response relationships were adopted to evaluate the effect in China in 2014. The results showed that the estimated percentage of O3-induced NPP decline was in the range of 5%–35%. Among them, the most significant declines were found in farmland ecosystems, with the vast majority of declines in 10%–35%. The decrease rate of evergreen coniferous forests ranked second, mostly in the range of 15%–20%. The grassland ecosystems declined at a lower rate, almost between 5% and 15%. And the evergreen broad-leaved forests has the lowest decline rate, most distributed in 0%–5%. The highest percentage decreases were mainly found in the Beijing-Tianjin-Hebei region and Shandong Province. And the decline rate of farmland ecosystems was significantly higher than other ecosystem types.

There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval.

The effects of cold rolling reduction and annealing temperatures on the formation of cube texture and microstructural evolution have been investigated in Cu-44%Ni alloy. The results show that the recrystallized cube texture is strengthened by either increasing the rolling reduction or increasing the annealing temperature. And the strong cube texture can be observed for the severely cold-rolled (>90%) alloy after annealing at high temperature (>900 °C). Furthermore, high-angle grain boundaries (HAGBs) and annealing twin boundaries (Σ3 boundaries) decrease with the increase of rolling reduction. However, during isochronal annealing, the trend of Σ3 boundaries is consistent with that of HAGBs, which firstly increases during recrystallization process and then decreases with further increasing temperature. After annealing at 1100 °C for 1 h, the 99% cold-rolled Cu-44%Ni alloy obtains the fraction of the cube texture 99.8%, and the fractions of HAGBs and Σ3 boundaries are 2.5% and 1.3%, respectively.