Assessing the potential spray drift of a six-rotor unmanned aerial vehicle sprayer using a test bench and airborne drift collectors under low wind velocities: impact of atomization characteristics and application parameters.

BACKGROUND: The unmanned aerial spraying systems (UASS) have gained widespread use for plant protection in recent years. However, spray drift from UASS is a major concern when controlling weeds over large areas and warrants a thorough investigation. This study examined the atomization characteristics of the herbicide florpyrauxifen-benzyl under downwash airflow using a UASS spray test platform. Potential spray drift was assessed using a test bench (TB) and airborne drift collectors (ADCs) in the field under low wind speeds (<1 m s-1). RESULTS: Atomization characteristics were significantly affected by the spray liquid, adjuvant, nozzle type and spray pressure. The addition of an adjuvant reduced the liquid sheet length, improved physicochemical properties and increased droplet size under the downwash airflow field. Drift evaluation in the field using the TB revealed that sediment spray drift predominantly occurred from the middle to the entire length of the device when fine-to-medium droplets were produced after the sprayer passed. ADC assessment found that higher flight altitudes and finer droplets resulted in higher drift values, whereas the addition of an adjuvant and the use of an air-induction nozzle reduced drift <3 m aboveground. CONCLUSION: The combination of using TB in the target area and ADCs in the off-target area as an alternative method to determine residual droplets in the current airflow provided valuable insights into airborne drift assessment for UASS. © 2024 Society of Chemical Industry.

Cohort study of cardiovascular safety of different COVID-19 vaccination doses among 46 million adults in England.

The first dose of COVID-19 vaccines led to an overall reduction in cardiovascular events, and in rare cases, cardiovascular complications. There is less information about the effect of second and booster doses on cardiovascular diseases. Using longitudinal health records from 45.7 million adults in England between December 2020 and January 2022, our study compared the incidence of thrombotic and cardiovascular complications up to 26 weeks after first, second and booster doses of brands and combinations of COVID-19 vaccines used during the UK vaccination program with the incidence before or without the corresponding vaccination. The incidence of common arterial thrombotic events (mainly acute myocardial infarction and ischaemic stroke) was generally lower after each vaccine dose, brand and combination. Similarly, the incidence of common venous thrombotic events, (mainly pulmonary embolism and lower limb deep venous thrombosis) was lower after vaccination. There was a higher incidence of previously reported rare harms after vaccination: vaccine-induced thrombotic thrombocytopenia after first ChAdOx1 vaccination, and myocarditis and pericarditis after first, second and transiently after booster mRNA vaccination (BNT-162b2 and mRNA-1273). These findings support the wide uptake of future COVID-19 vaccination programs.

CFI-Net: A Choquet Fuzzy Integral based Ensemble Network with PSO-Optimized Fuzzy Measures for Diagnosing Multiple Skin Diseases Including Mpox.

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

A modified orthotopic left lung transplantation model in rats.

To enhance the operability of the rat orthotopic left lung transplantation model, we implemented several improvements and meticulously detailed the procedure. One hundred and thirty-one healthy male Sprague Dawley rats, weighing between 250 and 300 g, were utilized, with 64 serving as donors, 64 as recipients, and 3 as sham controls. We employed a modified three-cuff technique for the orthotopic left lung transplantation. Notably, our modified perfusion method could prevent donor lung edema, while waist-shaped cuffs minimized suture slippage during anastomosis. Additionally, positioning the recipient rat in a slightly left-elevated supine position during anastomosis reduced tension on the lung hilum, thus mitigating the risk of vascular laceration. The introduction of a unique two-person anastomosis technique significantly reduced operation time and substantially improved success rates. Furthermore, maximizing inflation of donor lungs both during preservation and surgery minimized the occurrence of postoperative atelectasis. Various other procedural refinements contributed to the enhanced operability of our model. Sixty-four rat orthotopic left lung transplantations were performed with only one surgical failure observed. The acquisition time for donor lungs averaged (19 ± 4) minutes, while (11 ± 1) minutes were allocated for donor lung hilum anatomy and cuff installation. Recipient thoracotomy and left lung hilar anatomy before anastomosis required (24 ± 8) minutes, with anastomosis itself taking (31 ± 6) minutes. Remarkably, the survival rate at the 4-h postoperative mark stood at 96.7 %. Even six months post-operation, transplanted left rat lungs continued to exhibit proper inflation and contraction rhythms, displaying signs of chronic pathological changes. In summary, our modified rat model of orthotopic left lung transplantation demonstrates robust operability, significantly reducing surgical duration, improving operation success rates, and enhancing postoperative survival rates. Furthermore, its long-term survival capacity enables the simulation of acute and chronic disease processes following lung transplantation.

The Tandem Nitrate and CO2 Reduction for Urea Electrosynthesis: Role of Surface N-Intermediates in CO2 Capture and Activation.

Electrochemical reduction of CO2 and nitrate offers a promising avenue to produce valuable chemicals through the using of greenhouse gas and nitrogen-containing wastewater. However, the generally proposed reaction pathway of concurrent CO2 and nitrate reduction for urea synthesis requires the catalysts to be both efficient in both CO2 and nitrate reduction, thus narrowing the selection range of suitable catalysts. Herein, we demonstrate a distinct mechanism in urea synthesis, a tandem NO3 - and CO2 reduction, in which the surface amino species generated by nitrate reduction play the role to capture free CO2 and subsequent initiate its activation. When using the TiO2 electrocatalyst derived from MIL-125-NH2, it intrinsically exhibits low activity in aqueous CO2 reduction, however, in the presence of both nitrate and CO2, this catalyst achieves an excellent urea yield rate of 43.37 mmol ⋅ g-1 ⋅ h-1 and a Faradaic efficiency of 48.88 % at -0.9 V vs. RHE in a flow cell. Even at a low CO2 level of 15 %, the Faradaic efficiency of urea synthesis remains robust at 42.33 %. The tandem reduction procedure was further confirmed by in situ spectroscopies and theoretical calculations. This research provides new insights into the selection and design of electrocatalysts for urea synthesis.

Quercetin attenuates inflammation in rosacea by directly targeting p65 and ICAM-1.

AIMS: Rosacea is an inflammatory skin disease with immune and vascular dysfunction. Although there are multiple treatment strategies for rosacea, the clinical outcomes are unsatisfactory. MAIN METHODS: Combining transcriptome data and the Connectivity Map database quercetin was identified as a novel candidate for rosacea. Next, the therapeutic efficacy of quercetin was substantiated through proteomic analyses, in vivo experiments, and in vitro assays. Additionally, the utilization of DARTS, molecular docking and experimental verification revealed the therapeutic mechanisms of quercetin. KEY FINDINGS: Treatment with quercetin resulted in the following effects: (i) it effectively ameliorated rosacea-like features by reducing immune infiltration and angiogenesis; (ii) it suppressed the expression of inflammatory mediators in HaCaT cells and HDMECs; (iii) it interacted with p65 and ICAM-1 directly, and this interaction resulted in the repression of NF-κB signal and ICAM-1 expression in rosacea. SIGNIFICANCE: We show for the first time that quercetin interacted with p65 and ICAM-1 directly to alleviated inflammatory and vascular dysfunction, suggesting quercetin is a novel, promising therapeutic candidate for rosacea.

Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

BACKGROUND: The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. RESULTS: The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. CONCLUSION: The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort.

BACKGROUND: Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort. METHODS: The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions. RESULTS: For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis. CONCLUSIONS: In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection.

The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.

Pathways for achieving conservation targets under metacoupled anthropogenic disturbances.

Enhancing connectivity between protected areas stands as a paramount objective in advancing global conservation goals, particularly in coastal regions grappling with escalating human disruptions. However, little attention has been given to quantitative assessment of human-nature interactions within and among protected areas. Here, we endeavored to model the connectivity between protected areas in rapidly urbanizing regions in China, drawing on insights from the framework of metacoupling based on connected corridors at short and long distances. In alignment with the overarching global conservation aim of increasing the overall coverage of protected areas, we found that adding new site to the protected area system yields superior connectivity gains compared to merely expanding the boundaries of the existing sites. Within the connectivity network between protected areas, we discerned specific sites acting as stepping stones, pivotal in enhancing connectivity among the chosen protected areas. Our study propounds a pragmatic methodology for prioritizing local protection initiatives and underscores the criticality of incorporating connectivity conservation strategies. This approach is vital for attaining regional biodiversity targets, given the dual perspective encompassing both human activities and the natural environment, particularly in the face of mounting anthropogenic disturbances.

Stronger increases but greater variability in global mangrove productivity compared to that of adjacent terrestrial forests.

Mangrove forests are a highly productive ecosystem with important potential to offset anthropogenic greenhouse gas emissions. Mangroves are expected to respond differently to climate change compared to terrestrial forests owing to their location in the tidal environment and unique ecophysiological characteristics, but the magnitude of difference remains uncertain at the global scale. Here we use satellite observations to examine mean trends and interannual variability in the productivity of global mangrove forests and nearby terrestrial evergreen broadleaf forests from 2001 to 2020. Although both types of ecosystem experienced significant recent increases in productivity, mangroves exhibited a stronger increasing trend and greater interannual variability in productivity than evergreen broadleaf forests on three-quarters of their co-occurring coasts. The difference in productivity trends is attributed to the stronger CO2 fertilization effect on mangrove photosynthesis, while the discrepancy in interannual variability is attributed to the higher sensitivities to variations in precipitation and sea level. Our results indicate that mangroves will have a faster increase in productivity than terrestrial forests in a CO2-rich future but may suffer more from deficits in water availability, highlighting a key difference between terrestrial and tidal ecosystems in their responses to climate change.