Overall, tea and rice plantations appear to be the major nutrient contributors to reservoir Daxi. And the main nutrient sources for reservoir Shahe are tea plantations, orchards, farmland, forestland, and point sources. Regarding the CSAs identified only by nutrient load, agronomic measures such as reducing fertilizer amount, biochar application, straw incorporation, and plastic mulch coverage can be employed to improve soil water retention and curb soil erosion. Regarding the CSAs identified by nutrient load intensity (NLI), the CSAs with narrow areas should be turned directly into forestland. For the CSAs with large areas, engineering measures such as constructing ecological riparian zone, filtration, and sedimentation tank can be employed to prevent pollutants from entering downstream reaches. Overall, the present results can provide the decision-making support for the safe and efficient management of watershed land use in southern China.Yunnan Province in southwest China is characterized by a vast area, diverse climate types, rich ecosystem types, and unique biodiversity resources. With consideration of global climate change, there is an urgent need to evaluate the response of vegetation to drought in Yunnan. This study utilized the MOD13A3, MOD17A2, and Tropical Rainfall Measuring Mission (TRMM) 3B43 remote sensing products. The TRMM 3B43 downscaled monthly precipitation data were used to calculate the tropical rainfall condition index (TRCI) for Yunnan. The TRCI was used as a drought index, and the temporal and spatial changes in TRCI, gross primary productivity (GPP), and vegetation condition index (VCI) from 2009 to 2018 were explored. The response of vegetation to drought was evaluated under different time scales and varying land-use types. The results showed that during 2009-2018, (1) at an annual scale, the drought in Yunnan showed a weakening trend, and at a spatial scale, the drought showed a weakening trend from northwest to southeast. This weakening trend was more noticeable for cultivated land than for forest, grassland, and other land-use types. (2) GPP and VCI showed overall increasing trends at an annual scale, indicating gradual improvements in the GPP of vegetation and vegetation status, whereas the summer vegetation index showed a decreasing trend. (3) Although both the GPP and the growth state of vegetation were affected by drought, the responses of GPP and VCI to drought differed under different temporal scales and different land-use types. The responses of GPP and VCI to drought during spring were greater than those over other seasons, and the response of VCI to drought was more sensitive than that of GPP. Drought had a high impact on the GPP and vegetation growth of cultivated land and grassland with shallow root systems, whereas the impact of drought on forest was relatively stable.The efficient removal of uranium (VI) (UO22+) is of great significance to the ecological environment. However, there is still a lack of efficient adsorption materials to remove UO22+ in wastewater economically. Because natural basswood has high porosity, natural hydrophilicity, and abundant surface functional groups, wood as a support material has a good application prospect in water treatment. In the present work, the amidoxime functional group (AO) is grafted to the hydroxyl group of the wood fiber (AO-wood). A carbon layer is formed on the surface of the basswood by heating, and some Ag nanoparticles with good optothermal effect are added to the wood tunnel (Ag-C-AO-wood). Ag-C-AO-wood is used for efficient wastewater treatment under light conditions. The adsorption kinetic of Ag-C-AO-wood is 4.6 h under one irradiation, which is 7 times faster than AO-wood. It has approached or even surpassed some traditional carbon materials with stirring. This method is expected to break the traditional stirring method. Ag-C-AO-wood can not only remove uranium up to 82% but also have a good removal efficiency (27%) on iodide ions. More importantly, due to basswood characteristics, it is possible to large-scale preparation and explore its potential application value in wastewater.Air pollution and particulate matter (PM) are significant factors for adverse health effects most prominently cardiovascular disease (CVD).  https://www.selleckchem.com/products/gsk2643943a.html  PM is produced from various sources, which include both natural and anthropogenic. It is composed of biological components, organic compounds, minerals, and metals, which are responsible for inducing inflammation and adverse health effects. However, the adverse effects are related to PM size distribution. Finer particles are a significant cause of cardiovascular events. This review discusses the direct and indirect mechanisms of PM-induced CVD like myocardial infarction, the elevation of blood pressure, cardiac arrhythmias, atherosclerosis, and thrombosis. The two potential mechanisms are oxidative stress and systemic inflammation. Prenatal exposure has also been linked with cardiovascular outcomes later in life. Moreover, we also mentioned the epidemiological studies that strongly associate PM with CVD.
  The automatic extraction of knowledge about intervention execution from surgical manuals would be of the utmost importance to develop expert surgical systems and assistants. In this work we assess the feasibility of automatically identifying the sentences of a surgical intervention text containing procedural information, a subtask of the broader goal of extracting intervention workflows from surgical manuals.
 
  We frame the problem as a binary classification task. We first introduce a new public dataset of 1958 sentences from robotic surgery texts, manually annotated as procedural or non-procedural. We then apply different classification methods, from classical machine learning algorithms, to more recent neural-network approaches and classification methods exploiting transformers (e.g., BERT, ClinicalBERT). We also analyze the benefits of applying balancing techniques to the dataset.
 
  The architectures based on neural-networks fed with FastText's embeddings and the one based on ClinicalBERT outperform all the tested methods, empirically confirming the feasibility of the task. Adopting balancing techniques does not lead to substantial improvements in classification.
 
  This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task.
 This is the first work experimenting with machine / deep learning algorithms for automatically identifying procedural sentences in surgical texts. It also introduces the first public dataset that can be used for benchmarking different classification methods for the task.