Toward a remote sensing method based on commercial LiDAR sensors for the measurement of spray drift and potential drift reduction.

Spray drift is inevitable in chemical applications, drawing global attention because of its potential environmental pollution and the risk of exposing bystanders to pesticides. This issue has become more pronounced with a growing consensus on the need for enhanced environmental safeguards in agricultural practices. Traditionally, spray drift measurements, crucial for refining spray techniques, relied on intricate, time-consuming, and labor-intensive sampling methods utilizing passive collectors. In this study, we investigated the feasibility of using close-range remote sensing technology based on Light Detection and Ranging (LiDAR) point clouds to implement drift measurements and drift reduction classification. The results show that LiDAR-based point clouds vividly depict the spatial dispersion and movement of droplets within the vertical plane. The capability of LiDAR to accurately determine drift deposition was demonstrated, evident from the high R2 values of 0.847, 0.748 and 0.860 achieved for indoor, wind tunnel and field environments, respectively. Droplets smaller than 100 µm and with a density below 50 deposits·cm-2·s-1 posed challenges for LiDAR detection. To address these challenges, the use of multichannel LiDAR with higher wavelengths presents a potential solution, warranting further exploration. Furthermore, we found a satisfactory consistency when comparing the drift reduction classification calculated from LiDAR measurements with those obtained though passive collectors, both in indoor tests and the unmanned air-assisted sprayer (UAAS) field test. However, in environments with less dense clouds of larger droplets, a contradiction emerged between higher drift deposition and lower scanned droplet counts, potentially leading to deviations in the calculated drift potential reduction percentage (DPRP). This was exemplified in a field test using an unmanned aerial vehicle sprayer (UAVS). Our findings provide valuable insights into the monitoring and quantification of pesticide drift at close range using LiDAR technology, paving the way for more precise and efficient drift assessment methodologies.

ABHD5 protects cardiac function in alcoholic cardiomyopathy via HDAC4-NT to inhibit CAMKII/MEF2 axis.

Excessive intake of Alcohol is associated with a high incidence of alcoholic cardiomyopathy (ACM), which may impair cardiac function. In our study, we explored the Abhydrolase Domain Containing 5 (ABHD5) mechanism in ACM about histone deacetylase 4 (HDAC4) and CaM-CaMKII/MEF2 signaling pathway. Rat models of ACM were established in Wistar rats, and in vitro cell models were constructed in rat cardiomyocytes H9C2 utilizing 12-h of treatment of Alcohol (200 mM) to study the regulatory role of ABHD5 in ACM with the involvement of HDAC4 and CaM-CaMKII/MEF2 signaling pathway, as evidenced by determination of cardiac function, myocardial fibrosis, apoptosis of cardiomyocytes and oxidative stress condition. We found that both ABHD5 mRNA and protein expression was significantly lower in the ACM rats and rat cardiomyocytes H9C2. ACM rats with oe-ABHD5 injection showed repressed myocardial hypertrophy and myocardial fibrosis. Also, overexpression of ABHD5 reduced apoptosis and oxidative stress in H9C2 cells. Mechanistic studies demonstrated that ABHD5 via HDAC4-NT inhibits CAMKII/MEF2 axis. This study highlighted that ABHD5 decreased cardiac hypertrophy and myocardial fibrosis and limited cardiomyocyte apoptosis and oxidative stress injury in ACM.

Spray drift evaluation with point clouds data of 3D LiDAR as a potential alternative to the sampling method.

Spray drift is an inescapable consequence of agricultural plant protection operation, which has always been one of the major concerns in the spray application industry. Spray drift evaluation is essential to provide a basis for the rational selection of spray technique and working surroundings. Nowadays, conventional sampling methods with passive collectors used in drift evaluation are complex, time-consuming, and labor-intensive. The aim of this paper is to present a method to evaluate spray drift based on 3D LiDAR sensor and to test the feasibility of alternatives to passive collectors. Firstly, a drift measurement algorithm was established based on point clouds data of 3D LiDAR. Wind tunnel tests included three types of agricultural nozzles, three pressure settings, and five wind speed settings were conducted. LiDAR sensor and passive collectors (polyethylene lines) were placed downwind from the nozzle to measure drift droplets in a vertical plane. Drift deposition volume on each line and the number of LiDAR droplet points in the corresponding height of the collecting line were calculated, and the influencing factors of this new method were analyzed. The results show that 3D LiDAR measurements provide a rich spatial information, such as the height and width of the drift droplet distribution, etc. High coefficients of determination (R 2 > 0.75) were observed for drift points measured by 3D LiDAR compared to the deposition volume captured by passive collectors, and the anti-drift IDK12002 nozzle at 0.2 MPa spray pressure has the largest R 2 value, which is 0.9583. Drift assessment with 3D LiDAR is sensitive to droplet density or drift mass in space and nozzle initial droplet spectrum; in general, larger droplet density or drift mass and smaller droplet size are not conducive to LiDAR detection, while the appropriate threshold range still needs further study. This study demonstrates that 3D LiDAR has the potential to be used as an alternative tool for rapid assessment of spray drift.