Acceptability of Long-Acting Injectable Antiretroviral Therapy Among People with HIV Receiving Care at Three Ryan White Funded Clinics in the United States.

Understanding the acceptability of long-acting injectable antiretroviral therapy (LAI-ART) among people with HIV (PWH), especially priority populations, is essential for effective implementation. We conducted semi-structured interviews with patients in three Ryan White-funded HIV clinics in San Francisco, Chicago, and Atlanta. We employed maximal variation sampling across age, gender, race, ethnicity, and time living with HIV and oversampled for individuals with suboptimal clinical engagement. An 8-step hybrid deductive and inductive thematic analysis approach guided data analysis. Between August 2020 and July 2021, we conducted 72 interviews. Median age was 46 years; 28% were ciswomen, 7% transwomen, 44% Black/African-American and 35% Latinx, 43% endorsed a psychiatric diagnosis, 35% were experiencing homelessness/unstable housing, and 10% had recent substance use. Approximately 24% were sub-optimally engaged in care. We observed a spectrum of LAI-ART acceptability, ranging from enthusiasm to hesitancy to rejection. We also characterized four emergent orientations towards LAI-ART: innovator, pragmatist, deliberator, and skeptic. Overall, the majority of participants expressed favorable initial reactions towards LAI-ART. Most approached LAI-ART pragmatically, but acceptability was not static, often increasing over the course of the interview. Participants considered their HIV providers as essential for affirming personal relevance. HIV stigma, privacy concerns, and medical mistrust had varied impacts, sometimes facilitating and other times hindering personal relevance. These findings held across priority populations, specifically young adults, cis/trans women, racial/ethnic minorities, and individuals with suboptimal clinical engagement. Further research is needed to explore the transition from hypothetical acceptance to uptake and to confirm the actual benefits and drawbacks of this treatment.

RESUMEN: La aceptabilidad de la terapia antirretroviral inyectable de acción prolongada (LAI-ART, por su sigla en inglés) entre personas con VIH es esencial para una implementación efectiva. Durante el periodo de agosto de 2020 a julio de 2021, realizamos 72 entrevistas semiestructuradas con personas con VIH en clínicas públicas ubicadas en San Francisco, Chicago y Atlanta. Un análisis temático, tanto deductivo como inductivo, guio nuestra investigación. Observamos un espectro de aceptabilidad de LAI-ART que va desde el entusiasmo hasta la indecisión y el rechazo. También caracterizamos cuatro orientaciones actitudinales emergentes hacia LAI-ART: innovadora, pragmática, deliberativa y escéptica. Los participantes también señalaron la importancia de sus proveedores de VIH para validar su relevancia personal. El estigma asociado al VIH, preocupaciones sobre la privacidad y desconfianza en el sistema médico tuvieron diversos impactos, a veces facilitando y otras veces obstaculizando la relevancia personal. Entre las poblaciones prioritarias del estudio, los resultados fueron consistentes.

Neural basis of writing in prodromal to mild dementia with lewy bodies.

OBJECTIVES: We have previously demonstrated difficulties in written production in dementia with Lewy bodies (DLB) patients. We now aim to determine the neural correlates of writing production in DLB, combining clinical data and structural MRI measures. METHOD: Sixteen prodromal to mild DLB patients were selected to participate in the study. The GREMOTS test was used to assess writing production. Using three-dimensional T1 brain MRI images, correlations between the GREMOTS test and grey matter (GM) volume were performed using voxel-based morphometry (VBM; SPM12, XjView and Matlab R2021b softwares). RESULTS: VBM analysis (p < 0.001, uncorrected) revealed a positive and significant correlation between both left anterior insula and left supramarginal gyrus GM volumes and DLB patients' ability to write logatoms using the phonological route. The handwriting deficit was negatively and significantly correlated to the supplementary motor area. The parkinsonism-like characteristics of agraphia were negatively and significantly correlated with both right anterior and right posterior cerebellum GM volumes. Our study also revealed a negative and significant correlation between grammatical spelling impairments and an area of the orbitofrontal gyrus, and a negative and significant correlation between supramarginal gyrus and general slowness in dictation tasks. CONCLUSION: Writing disorders in early DLB patients appears to be GM decreases in several brain regions, such as the left anterior insula, the left supramaginal gyrus, as well as two areas of the right cerebellum.

Association between the irrigation-agitation techniques and Periapical Healing of large periapical lesions: a Randomized Controlled Trial.

OBJECTIVES: The aim of this study was to evaluate the effects of manual dynamic activation (MDA), passive ultrasonic irrigation (PUI), and laser-activated irrigation (shock wave-enhanced emission photoacoustic streaming (SWEEPS)) on the periapical healing of large periapical lesions following nonsurgical root canal treatment. MATERIALS AND METHODS: A total of fifty-six systemically healthy patients with a mandibular single-rooted tooth with periapical lesions of endodontic origin and a periapical index score of 3 or higher were included in the study. Before the treatment procedures, lesion volumes were determined volumetrically using cone-beam computed tomography (CBCT). Patients were randomized into treatment (MDA, PUI, SWEEPS) and control groups (n = 14). Root canal treatment and irrigation procedures were performed by a calibrated postgraduate operator and completed at one visit. For routine follow-up, clinical and radiographic evaluations were performed by a blinded evaluator using periapical (PA) radiographs according to Molven's criteria at 3, 6, and 9 months. At 12 months, lesion volumes were quantified volumetrically using CBCT (ITK-SNAP). The data were statistically analyzed with the Wilcoxon test. The significance level was set at p < 0.05. RESULTS: In all groups, the mean lesion volume after treatment was significantly smaller than the mean volume before treatment (p = 0.001). Among the 56 teeth, 11 teeth were 'totally healed', and 39 teeth were 'reduced' on PA radiographs. No 'enlargement' was detected in any group. On CBCT, the lesion volume decreased in the following order: LAI-SWEEPS (86.9%) > PUI (85.4%) > MDA (80.4%) > control (74.5%), with no statistically significant difference (p > 0.05). CONCLUSIONS: Despite the limitations of the present study, although a greater percentage of healing was observed in the LAI-SWEEPS and PUI groups, irrigation procedures had no statistically significant effect on the healing of periapical lesions with a single root canal at the 12-month follow-up. On the other hand, the outcome may change in multirooted teeth with curved and complex root canal systems. CLINICAL RELEVANCE: In the short term and in single-canal teeth, advanced irrigation agitation methods such as laser and ultrasonic did not make a difference in healing other than manual irrigation agitation.

Recommendations from Black and Latinx sexual minority males to include pharmacists to increase greater accessibility and OnlyFans stars to promote uptake of injectable PrEP.

BACKGROUND: Pre-exposure Prophylaxis (PrEP) medication is the keystone of preventative measures to curtail the spread of HIV. However, oral PrEP, the pill intended to prevent HIV, has been slow to proliferate among men who have sex with men (MSM). This is of major concern as MSM account for the largest number of new HIV diagnoses in the U.S. More recently, the newest generation of PrEP in the form of a long-acting injectable (LAI) is to be administered every two months as an intramuscular injection and many MSM indicate preferring LAI-PrEP to the oral form of PrEP. However, uptake of PrEP, in all forms, remains low. Research is sparse that focuses on LAI-PrEP uptake among Black/African American and Latinx men who have sex with men (BLMSM). OBJECTIVE: To address this concern, this study explored the willingness to uptake LAI-PrEP, and recommendations for increasing awareness and encouraging uptake of LAI-PrEP among BLMSM. METHODS: Qualitative data were collected between February 2022 to December 2022 through focus groups via Zoom with BLMSM (N=30; Black=14, Latinx=16) aged 18 to 29 (Mean = 23, SD = 3) in Los Angeles County. RESULTS: Findings revealed that while 90% of BLMSM were aware of PrEP in oral form, only 10% were aware of LAI-PrEP. Findings from the qualitative analysis suggested to consider self-administration of LAI-PrEP, allow local community pharmacists to assess and administer it, and promote uptake of LAI-PrEP using high-profile male content creators and stars on OnlyFans social media platform. CONCLUSION: Increasing PrEP uptake, in all forms available, such as promoting awareness through popular social media stars, and engaging community pharmacists in feasible ways, is critical for addressing the disproportionate impact of HIV among the BLMSM community.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.

PhenoBot: an automated system for leaf area analysis using deep learning.

MAIN CONCLUSION: A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiation of the leaf canopy, making the leaf area index (LAI) a critical parameter for estimating yields. The growth rate of leaves at different growth stages determines the overall LAI, which is used by crop growth models (CGM) for simulating yield. Consequently, precise phenotyping of the leaves can help elucidate phenological processes relating to resource capturing. A stable system for acquiring images and a strong data processing backend play a vital role in reducing throughput time and increasing accuracy of calculations, compared to manual analysis. However, most available solutions are not dynamic, as they use color-based segmentation, which fails to capture leaves with varying shades and shapes. We have developed a system that uses a low-cost setup to acquire images and an automated pipeline to manage the data storage on the device and in the cloud. The system is powered by virtual machines that run multiple custom-trained deep learning models to segment out leaves, calculate leaf area (LA) for the whole set and at the individual leaf level, overlays important information on the images, and appends them on a compatible file used for CGMs with very high accuracy. The pipeline is dynamic and can be used for multiple crops. The use of open-source hardware, platforms, and algorithms makes this system affordable and reproducible.

Optimizing Uptake of Long-Acting Injectable Pre-exposure Prophylaxis for HIV Prevention for Men Who Have Sex with Men.

Pre-exposure prophylaxis (PrEP) is a highly effective HIV prevention tool. Long-acting injectable PrEP (LAI-PrEP) offers another opportunity to reduce HIV. However, how at-risk individuals will consider LAI-PrEP over other modes of administration is unclear. We conducted a discrete choice experiment on preferences for PrEP among a sample of N = 688 gay, bisexual, and other men who have sex with men (GBMSM). We analyzed preferences for mode of administration, side-effects, monetary cost, and time cost using a conditional logit model and predicted preference for PrEP options. LAI-PrEP was preferred, despite mode of administration being the least important PrEP attribute. Side-effects were the most important attribute influencing preferences for PrEP (44% of decision); costs were second-most-important (35% of decision). PrEP with no side-effects was the most important preference, followed by monthly out-of-pocket costs of $0. Practitioners and policymakers looking to increase PrEP uptake should keep costs low, communicate clearly about PrEP side-effects, and allow the use of patient-preferred modes of PrEP administration, including LAI-PrEP.

RESUMEN: La profilaxis prexposición (PrEP) es una herramienta de prevención del VIH muy eficaz. La PrEP inyectable de acción prolongada (LAI-PrEP) ofrece otra oportunidad para reducir el VIH. Sin embargo, no está claro cómo las personas en riesgo considerarán LAI-PrEP sobre otros modos de administración. Realizamos un experimento de elección discreta sobre las preferencias por la PrEP entre una muestra de N = 688 hombres homosexuales, bisexuales y otros hombres que tienen sexo con hombres (GBMSM). Analizamos las preferencias por el modo de administración, los efectos secundarios, el costo monetario y el costo del tiempo mediante un modelo logit condicional y la preferencia prevista por las opciones de PrEP. Se prefirió LAI-PrEP, a pesar de que el modo de administración es el atributo de PrEP menos importante. Los efectos secundarios fueron el atributo más importante que influyó en las preferencias por la PrEP (44% de la decisión); los costos fueron los segundos más importantes (35% de la decisión). La PrEP sin efectos secundarios fue la preferencia más importante, seguida de costos de bolsillo mensuales de $0. Los médicos y legisladores que buscan aumentar la aceptación de la PrEP deben mantener los costos bajos, comunicar claramente los efectos secundarios de la PrEP y permitir el uso de los modos de administración de la PrEP preferidos por los pacientes, incluido LAI-PrEP.

High Interest in Long-acting Injectable Pre-exposure Prophylaxis (LAI-PrEP) for HIV Prevention Among Men Who Have Sex With Men (MSM): Result From A Nationwide Survey in Malaysia.

The recent approval of long-acting injectable cabotegravir (CAB-LA) as PrEP for HIV prevention could be an attractive alternative for MSM, particularly among those who face barriers to adherence using the oral pill. This study reports on the awareness of long-acting injectable PrEP (LAI-PrEP) and factors associated with interest in LAI-PrEP use among a nationwide sample of MSM in Malaysia. An online cross-sectional survey was conducted between August and September 2021 to explore perspectives on PrEP modalities among Malaysian MSM (N = 870). Convenience sampling was used to recruit participants using ads on two platforms hornet and facebook. While only 9.1% of the study participants were aware of LAI-PrEP, the majority had heard of oral PrEP (80.9%). After giving a description of it, a large majority (86.6%) expressed interest in using it if made accessible. Those who had a prior history of HIV testing (aOR = 1.9; 95% CI = 1.2-3.2) were more likely to use LAI-PrEP. Interestingly, despite the concerns related to potential high cost (aOR = 3.4; 95% CI = 2.1-5.5) and long-term side effects (aOR = 1.9; 95% CI = 1.2-3.1), the majority of the participants were interested in using LAI-PrEP. Those who were afraid of (or disliked) syringes were less interested in using it (aOR = 0.2; 95% CI; 0.1-0.4). In the recent context that LAI-PrEP was shown to be safe and effective at preventing HIV, our results indicate its potential relevance as an additional PrEP option that could accelerate the uptake and scale-up of PrEP. However, it is crucial to conduct future research urgently to improve the understanding of strategies that could enhance the accessibility, acceptability, and affordability of LAI-PrEP for MSM in low- and middle-income countries, including Malaysia.

Convolution-based approach for modeling the paliperidone extended release and Long-Acting Injectable (LAI) PK of once-, and three-monthly products administration and for optimizing the development of new LAI products.

The aim of this paper was to develop a convolution-based modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. In particular, the convolution-based modeling can (a) facilitate the development of in vitro/in vivo correlation, (b) be used to identify formulations with optimal in vivo release properties, and (c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in vitro and in vivo drug release, in vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in vivo drug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.

Enhancing Leaf Area Index Estimation for Maize with Tower-Based Multi-Angular Spectral Observations.

The leaf area index (LAI) played a crucial role in ecological, hydrological, and climate models. The normalized difference vegetation index (NDVI) has been a widely used tool for LAI estimation. However, the NDVI quickly saturates in dense vegetation and is susceptible to soil background interference in sparse vegetation. We proposed a multi-angular NDVI (MAVI) to enhance LAI estimation using tower-based multi-angular observations, aiming to minimize the interference of soil background and saturation effects. Our methodology involved collecting continuous tower-based multi-angular reflectance and the LAI over a three-year period in maize cropland. Then we proposed the MAVI based on an analysis of how canopy reflectance varies with solar zenith angle (SZA). Finally, we quantitatively evaluated the MAVI's performance in LAI retrieval by comparing it to eight other vegetation indices (VIs). Statistical tests revealed that the MAVI exhibited an improved curvilinear relationship with the LAI when the NDVI is corrected using multi-angular observations (R2 = 0.945, RMSE = 0.345, rRMSE = 0.147). Furthermore, the MAVI-based model effectively mitigated soil background effects in sparse vegetation (R2 = 0.934, RMSE = 0.155, rRMSE = 0.157). Our findings demonstrated the utility of tower-based multi-angular spectral observations in LAI retrieval, having the potential to provide continuous data for validating space-borne LAI products. This research significantly expanded the potential applications of multi-angular observations.

Assessment of Forest Ecosystem Variations in the Lancang-Mekong Region by Remote Sensing from 2010 to 2020.

Five countries in the Lancang-Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang-Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang-Mekong region, which is crucial for implementing effective conservation strategies.

Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data.

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy-top point clouds were selected using a method based on the highest percentile, and a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R2 = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R2 = 0.24, RMSE = 0.1) and ground return intensity (R2 = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R2 = 0.77, RMSE = 0.03) was more accurate.

Growth Monitoring and Yield Estimation of Maize Plant Using Unmanned Aerial Vehicle (UAV) in a Hilly Region.

More than 66% of the Nepalese population has been actively dependent on agriculture for their day-to-day living. Maize is the largest cereal crop in Nepal, both in terms of production and cultivated area in the hilly and mountainous regions of Nepal. The traditional ground-based method for growth monitoring and yield estimation of maize plant is time consuming, especially when measuring large areas, and may not provide a comprehensive view of the entire crop. Estimation of yield can be performed using remote sensing technology such as Unmanned Aerial Vehicles (UAVs), which is a rapid method for large area examination, providing detailed data on plant growth and yield estimation. This research paper aims to explore the capability of UAVs for plant growth monitoring and yield estimation in mountainous terrain. A multi-rotor UAV with a multi-spectral camera was used to obtain canopy spectral information of maize in five different stages of the maize plant life cycle. The images taken from the UAV were processed to obtain the result of the orthomosaic and the Digital Surface Model (DSM). The crop yield was estimated using different parameters such as Plant Height, Vegetation Indices, and biomass. A relationship was established in each sub-plot which was further used to calculate the yield of an individual plot. The estimated yield obtained from the model was validated against the ground-measured yield through statistical tests. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Green-Red Vegetation Index (GRVI) indicators of a Sentinel image was performed. GRVI was found to be the most important parameter and NDVI was found to be the least important parameter for yield determination besides their spatial resolution in a hilly region.

Integrated effects of co-evolutions among climate, land use and vegetation growing dynamics to changes of runoff quantity and quality.

Climates, Land use/land cover (LULC) and vegetation growing dynamics have been regarded as the main factors affecting terrestrial hydrological process. However, the mechanisms underlying their integrated effects on terrestrial runoff and nutrient dynamics are not understood well. Here, we constructed a framework to disentangle and quantify the independent and coupled contributions of climate, LULC and vegetation leaf area index (LAI) changes to watershed runoff and nutrient yields changes. Long series of changing meteorological, LULC and LAI data between 1990 and 2020 were integrated into a factor-controlled simulation protocol in a distributed hydrological model, to quantify their comprehensive contributions (individual contribution of single factor change and coupling contribution of multiple factor synchronous changes) to runoff and nutrient changes. The results showed that changes of runoff and nutrient yields are more induced by climate change, rather than LULC and LAI transformations. Increase in annual precipitation significantly elevated runoff and nutrient yields. TP yield was more sensitive to climate change than runoff and TN yields. LULC transformation and climate change have synergistic effects on runoff and nutrient yields. Shift of vegetation areas to construction lands will amplify the effect of climate change on runoff and nutrient yields. Single LAI change has weak effect on runoff and nutrient yields, but it can significantly alter the hydrological effects derived from climate change and the synergistic effects between climate change and LULC transformation. This study considered the coupling and potential synergistic effects among climate change, LULC conversion and LAI variation, which elucidated the comprehensive effects of changing environment on runoff and nutrients evolutions in a more systematic and integrated perspective.

Crop monitoring and biomass estimation based on downscaled remote sensing data in AquaCrop model (case study: Qazvin Plain, Iran).

Ensuring food security requires monitoring crop growth conditions prior to harvesting. This research encompass three stages: remote sensing, crop modeling, and establishing a connection between the two. Besides, Fusion satellite images have been implemented to monitor and investigate fodder corn in three study areas. In the remote sensing stage, after implementing the downscaling algorithm and producing the leaf area index (LAI) time series, the results were compared with the estimated values from Landsat 8 and MODIS images, which were associated with overestimation in all cases. Furthermore, the results exhibited statistically significance with R2 > 95% and P-value < 0.05. The AquaCrop model was first calibrated and implemented in the crop modeling section at each growth stage based on the observational data measured in each field. The accuracy of the simulated model was checked according to the results of statistical indicators with high accuracy (NRMSE = 10% and RMSE = 0.03 (Ton/ha)) at significant level (95%) and was associated with underestimation. Using the SVM decision support algorithm, the relationship between downscaled LAI and calibrated CC (crop canopy) was estimated. This relationship was generated with 70% of the data and subsequently validated using the remaining 30% (R2 = 0.99, NRMSE = 0.01). On this basis, CC values were predicted. Finally, biomass values were compared with observed biomass values. According to the results of statistical indicators (RMSE = 0.19 (Ton/ha), NRMSE = 0.01, R2 = 0.96, P-value < 0.05), biomass estimation was highly accurate. These results demonstrate the reliability and accuracy of the model and the proposed method for simulating and estimating biomass before harvesting.

Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data.

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery.

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020-2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0-40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient k(ϑ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing k(ϑ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The k(ϑ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-023-09993-9.

Biocomputational Identification of sRNAs in Leptospira interrogans Serovar Lai.

Regulatory small RNAs (sRNA) are RNA transcripts that are not translated into proteins but act as functional RNAs. Pathogenic Leptospira cause an epidemic spirochaetal zoonosis, Leptospirosis. It is speculated that Leptospiral sRNAs are involved in orchestrating their pathogenicity. In this study, biocomputational approach was adopted to identify Leptospiral sRNAs. In this study, two sRNA prediction programs, i.e., RNAz and nocoRNAc, were employed to screen the reference genome of Leptospira interrogans serovar Lai. Out of 126 predicted sRNAs, there are 96 cis-antisense sRNAs, 28 trans-encoded sRNAs and 2 sRNAs that partially overlap with protein-coding genes in a sense orientation. To determine whether these candidates are expressed in the pathogen, they were compared with the coverage files generated from our RNA-seq datasets. It was found out that 7 predicted sRNAs are expressed in mid-log phase, stationary phase, serum stress, temperature stress and iron stress while 2 sRNAs are expressed in mid-log phase, stationary phase, serum stress, and temperature stress. Besides, their expressions were also confirmed experimentally via RT-PCR. These experimentally validated candidates were also subjected to mRNA target prediction using TargetRNA2. Taken together, our study demonstrated that biocomputational strategy can serve as an alternative or as a complementary strategy to the laborious and expensive deep sequencing methods not only to uncover putative sRNAs but also to predict their targets in bacteria. In fact, this is the first study that integrates computational approach to predict putative sRNAs in L. interrogans serovar Lai. Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-022-01050-9.

Cross-sectional study of asymptomatic malaria and seroepidemiological surveillance of seven districts in Gia Lai province, Vietnam.

BACKGROUND: Malaria elimination by 2030 is an aim of many countries in the Greater Mekong Sub-region, including Vietnam. However, to achieve this goal and accelerate towards malaria elimination, countries need to determine the extent and prevalence of asymptomatic malaria as a potential reservoir for malaria transmission and the intensity of malaria transmission. The purpose of this study was to determine the prevalence of asymptomatic malaria and seropositivity rate in several districts of Gia Lai province in the Central Highlands of Vietnam. METHODS: A cross-sectional survey of asymptomatic malaria and serological testing was conducted in 3283 people living at 14 communes across seven districts in Gia Lai province in December 2016 to January 2017. Finger prick capillary blood samples were tested for malaria using rapid diagnostic testing and polymerase chain reaction (PCR), as well as detecting antibodies against 3 Plasmodium falciparum and 4 Plasmodium vivax antigens by indirect enzyme-linked immunosorbent assay (ELISA). Age-seroprevalence curves were fitted using reverse catalytic models with maximum likelihood. RESULTS: The study population was predominantly male (65.9%, 2165/3283), adults (88.7%, 2911/3283) and of a minority ethnicity (72.2%, 2371/3283), with most participants being farmers and outdoor government workers (90.2%, 2960/3283). Using a small volume of blood (≈ 10 µL) the PCR assay revealed that 1.74% (57/3283) of the participants had asymptomatic malaria (P. falciparum 1.07%, P. vivax 0.40%, Plasmodium malariae 0.15% and mixed infections 0.12%). In contrast, the annual malaria prevalence rates for clinical malaria in the communities where the participants lived were 0.12% (108/90,395) in 2016 and 0.22% (201/93,184) in 2017. Seropositivity for at least one P. falciparum or one P. vivax antigen was 38.5% (1257/3262) and 31.1% (1022/3282), respectively. Age-dependent trends in the proportion of seropositive individuals in five of the districts discriminated the three districts with sustained low malaria prevalence from the two districts with higher transmission. CONCLUSIONS: Asymptomatic Plasmodium carriers were found to be substantially more prevalent than clinical cases in seven districts of Gia Lai province, and a third of the population had serological evidence of previous malaria exposure. The findings add knowledge on the extent of asymptomatic malaria and transmission for developing malaria elimination strategies for Vietnam.