Objective: Long noncoding RNAs (lncRNAs) have already been proposed to function in Parkinson's disease (PD). However, the role of lncRNA BACE1-AS in PD has never been discussed. This study aims to examine the mechanism of BACE1-AS on oxidative stress injury of dopaminergic neurons in PD rats. Methods: Rat models of PD were established through the injection of 6-hydroxydopamine. The rotation of rats was induced by intraperitoneal injection of apomorphine, and number of rotations per minute was detected. The levels of malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px), glutamic acid (Glu), dopamine (DA), tyrosine hydroxylase (TH), αsynuclein and inducible nitric oxide synthase (iNOS) in the substantia nigra of rats in each group were detected. Apoptosis and pathological changes in the substantia nigra were also observed. BACE1-AS, miR-34b-5p, BACE1, Bax and Bcl-2 expression in the substantia nigra were detected. The binding of BACE1-AS and miR-34b-5p and the targeting relationship of miR-34b-5p and BACE1 were further determined. Results: Downregulated BACE1-AS reduced iNOS, α-synuclein and Glu levels and elevated DA and TH levels in the substantia nigra of PD rats. Downregulated BACE1-AS repressed apoptosis and oxidative stress injury in the substantia nigra neurons of PD rats. BACE1-AS specifically bound to miR-34b-5p. BACE1 was a direct target gene of miR-34b-5p. Conclusion: Collectively, our study reveals that downregulation of lncRNA BACE1-AS inhibits iNOS activation in the substantial nigra and improve oxidative stress injury in PD rats by upregulating miR-34b-5p and downregulating BACE1.

Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha–1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R2 = 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.

Nitrogen (N) is the most limiting nutrient for turfgrass growth. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the lone turfgrass growth prediction model only takes into account temperature, limiting its accuracy. This study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass (Agrostis stolonifera) putting green management by estimating short-term clipping yield. This method was compared against three alternative N application strategies including (1) PACE Turf growth potential (GP) model, (2) an experience-based method for applying N fertilizer (experience-based method), and (3) the experience-based method guided by a vegetative index, normalized difference red edge (NDRE)-based method. The ML-RF model was built based on a set of variables including 7-day weather, evapotranspiration (ET), traffic intensity, soil moisture content, N fertilization rate, NDRE, and root zone type. The field experiment was conducted on two sand-based research greens in 2020 and 2021. The cumulative applied N fertilizer was 281 kg ha−1 for the PACE Turf GP model, 190 kg ha−1 for the experience-based method, 140 kg ha−1 for the ML-RF model, and around 75 kg ha−1 NDRE-based method. ML-RF model and NDRE-based method were able to provide customized N fertilization recommendations on different root zones. The methods resulted in different mean turfgrass qualities and NDRE. From highest to lowest, they were PACE Turf GP model, experience-based, ML-RF model, and NDRE-based method, and the first three methods produced turfgrass quality over 7 (on a scale from 1 to 9) and NDRE value over 0.30. N fertilization guided by the ML-RF model resulted in a moderate amount of fertilizer applied and acceptable turfgrass performance characteristics. This application strategy is based on the N cycle and has the potential to assist turfgrass managers in making N fertilization decisions for creeping bentgrass putting greens.

目前, 3DVAG的制备方法主要有定向冷冻法(图2 (a))、PECVD法(图2(b))以及KOH辅助水热法(图2(c)).

The application of flexible indium tin oxide (ITO-free) electrochromic devices has steadily attracted widespread attention in wearable devices. Recently, silver nanowire/poly(dimethylsiloxane) (AgNW/PDMS)-based stretchable conductive films have raised great interest as ITO-free substrate for flexible electrochromic devices. However, it is still difficult to achieve high transparency with low resistance due to the weak binding force between AgNW and PDMS with low surface energy because of the possibility of detaching and sliding occurring at the interface. Herein, we propose a method to pattern the pre-cured PDMS (PT-PDMS) by stainless steel film as a template through constructed micron grooves and embedded structure, to prepare a stretchable AgNW/PT-PDMS electrode with high transparency and high conductivity. The stretchable AgNW/PT-PDMS electrode can be stretched (5000 cycles), twisted, and surface friction (3M tape for 500 cycles) without significant loss of conductivity (ΔR/R ≈ 16% and 27%). In addition, with the increase of stretch (stretching to 10–80%), the AgNW/PT-PDMS electrode transmittance increased, and the conductivity increased at first and then decreased. It is possible that the AgNWs in the micron grooves are spread during PDMS stretching, resulting in a larger spreading area and higher transmittance of the AgNWs film; at the same time, the nanowires between the grooves come into contact, thus increasing conductivity. An electrochromic electrode constructed with the stretchable AgNW/PT-PDMS exhibited excellent electrochromic behavior (transmittance contrast from ~61% to ~57%) even after 10,000 bending cycles or 500 stretching cycles, indicating high stability and mechanical robustness. Notably, this method of preparing transparent stretch electrodes based on patterned PDMS provides a promising solution for developing electronic devices with unique structures and high performance.