Development and optimization of the new ultrasonic-infrared-vacuum dryer in drying Kelussia odoratissima and its comparison with conventional methods

Among the post-harvest processing of medicinal plants, drying is an important and influential process. Given the numerous applications of medicinal plants, especially Kelussia odoratissima, in the food and pharmaceutical industries, the aim of this study was to compare the effects of the ultrasound-infrared radiation-vacuum method with conventional drying methods on the drying time, the total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity of K. odoratissima. ANOVA result showed that the effects of drying methods, drying temperature and their interaction effect on phenolic, flavonoid and antioxidant content were significant at 1% probability level. In the ultrasound-IR-vacuum method, by increasing temperature from 40 °C to 80 °C, the TFC increased by 35%. The highest antioxidant capacity was obtained for dry shade treatment, followed by dry sun treatment and three temperatures, i.e., 40 °C, 60 °C and 80 °C, in the combined method. The proposed optimal temperatures for the hot air, IR, and ultrasonic drying, were 63 °C, 66 °C and 71 °C, respectivel

Estimation and prediction of avoidable health care costs of cardiovascular diseases and type 2 diabetes through adequate dairy food consumption: a systematic review and micro simulation modeling study

Background: Recent evidence from prospective cohort studies show a relationship between consumption of dairy foods and cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM). This association highlights the importance of dairy foods consumption in prevention of these diseases and also reduction of associated healthcare costs. The aim of this study was to estimate avoidable healthcare costs of CVD and T2D through adequate dairy foods consumption in Iran. Methods: This was a multistage modelling study. We conducted a systematic literature review in PubMed and EMBASE to identify any association between incidence of CVD and T2DM and dairy foods intake, and also associated relative risks. We obtained age- and sex-specific dairy foods consumption level and healthcare expenditures from national surveys and studies. Patient level simulation Markov models were constructed to predict the disease incidence, patient population size and associated healthcare costs for current and optimal dairy foods consumption at different time horizons (1, 5, 10 and 20 years). All parameters including costs and transition probabilities were defined as statistical distributions in the models, and all analyses were conducted by accounting for first and second order uncertainty. Results: The systematic review results indicated that dairy foods consumption was inversely associated with incidence of T2DM, coronary heart disease (CHD) and stroke. We estimated that the introduction of a diet containing 3 servings of dairy foods per day may produce a $0.43 saving in annual per capita healthcare costs in Iran in the first year due to saving in cost of CVD and T2DM treatment. The estimated savings in per capita healthcare costs were$8.42, $39.97 and$190.25 in 5, 10 and 20-years’ time, respectively. Corresponding total aggregated avoidable costs for the entire Iranian population within the study time horizons were $33.83,$661.31, $3,138.21 and$14,934.63 million, respectively. Conclusion: Our analysis demonstrated that increasing dairy foods consumption to recommended levels would be associated with reductions in healthcare costs. Further randomized trial studies are required to investigate the effect of dairy foods intake on cost of CVD and T2DM in the population

Application of A Simple Landsat-MODIS Fusion Model to Estimate Evapotranspiration over A Heterogeneous Sparse Vegetation Region

A simple Landsat-MODIS (Moderate Resolution Imaging Spectroradiometer) fusion model was used to generate 30-m resolution evapotranspiration (ET) maps for the 2010 growing season over a heterogeneous sparse vegetation, agricultural region using the METRIC (mapping evapotranspiration with internalized calibration) algorithm. The fusion model performance was evaluated, and experiments were undertaken to investigate the frequency for updating Landsat-MODIS data into the fusion model during the growing season, to maintain model accuracy and reduce computation. Initial evaluation of the fusion model resulted in high bias stemming from the landscape heterogeneity and small landholdings. To reduce the bias, the fusion model was modified to be applicable pixel-wise (i.e., implementing specific pixels for generating outputs), and an NDVI-based (Normalized Difference Vegetation Index) coefficient was added to capture crop phenology. A good agreement that resulted from the comparison of the fused and non-fused maps with root mean square error (RMSE) of 0.15 mm day−1 with coefficient of determination (R2) of 0.83 indicated successful implementation of the modifications. Additionally, the fusion model performance was evaluated against in-situ observation at the pixel level as well as the watershed level to estimate seasonal ET for the growing season. The default METRIC model (Landsat only) yielded relative error (RE) of 31% and RMSE of 2.44 mm day−1, while using the modified fusion model improved the accuracy resulting in RE of 3.5% with RMSE of 0.37 mm day−1. Considering different data frequency update, the optimal fusion experiment (RMSE of 0.61 mm day−1, and RE of 6.5%) required the consideration of the crop phenology and weekly updates in the early growing stage and harvest time, and bi-weekly for the rest of the season. The resulting fusion model for ET output is planned to be a part of ET mapping and irrigation scheduling systems

Positive effects of public breeding on U.S. rice yields under future climate scenarios

This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "Positive effects of public breeding on U.S. rice yields under future climate scenarios". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm. This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205

Positive effects of public breeding on U.S. rice yields under future climate scenarios

<p>This data repository offers comprehensive resources, including datasets, Python scripts, and models associated with the study entitled, "<strong>Positive effects of public breeding on U.S. rice yields under future climate scenarios</strong>". The repository contains three models: a PCA model for data transformation, along with two meta-machine learning models for predictive analysis. Additionally, three Python scripts are available to facilitate the creation of training datasets and machine-learning models. The repository also provides tabulated weather, genetic, and county-level rice yield information specific to the southern U.S. region, which serves as the primary data inputs for our research. The focus of our study lies in modeling and predicting rice yields, incorporating factors such as molecular marker variation, varietal productivity, and climate, particularly within the Southern U.S. rice growing region. This region encompasses Arkansas, Louisiana, Texas, Mississippi, and Missouri, which collectively account for 85% of total U.S. rice production. By digitizing and merging county-level variety acreage data from 1970 to 2015 with genotyping-by-sequencing data, we estimate annual county-level allele frequencies. These frequencies, in conjunction with county-level weather and yield data, are employed to develop ten machine-learning models for yield prediction. An ensemble model, consisting of a two-layer meta-learner, combines the predictions of all ten models and undergoes external evaluation using historical Uniform Regional Rice Nursery trials (1980-2018) conducted within the same states. Lastly, the ensemble model, coupled with forecasted weather data from the Coupled Model Intercomparison Project, is employed to predict future production across the 110 rice-growing counties, considering various groups of germplasm.</p> <p>This study was supported by USDA NIFA 2014-67003-21858 and USDA NIFA 2022-67013-36205. </p&gt

Ten years of GLEAM : a review of scientific advances and applications

During the past decades, consistent efforts have been undertaken to model the Earth's hydrological cycle. Multiple mathematical models have been designed to understand, predict, and manage water resources, particularly under the context of climate change. A variable that has traditionally received limited attention by the hydrological community—but that is crucial to understand the links to climate—is terrestrial evaporation. The Global Land Evaporation Amsterdam Model (GLEAM) was developed ten years ago with the goal to derive terrestrial evaporation from satellite imagery. Since then, GLEAM has been used in a variety of applications, including trend analysis, drought and heatwave studies, hydrological model calibration and validation, water budget assessment, and studies of changes in vegetation. To streamline the development of the model and improve its ability and accuracy in capturing the spatiotemporal patterns of evaporation, while tailoring the development to the needs of stakeholders, it is important to review previous studies and highlight the potential strengths and weaknesses of the model. Therefore, in this study, we provide a literature review of the GLEAM model applications and its accuracy. The results of this metanalysis indicate that GLEAM is preferentially used in climate studies, potentially due to its coarse (25 km) spatial resolution being a limiting factor for its use in water management and, particularly, agricultural applications. Validations to date suggest that, while GLEAM provides a relatively accurate evaporation dataset, its performance over short canopies requires further improvement. Two major sources of uncertainty in the GLEAM algorithm have been identified: (1) the modelling of evaporative stress in response to water limitation, (2) the need to consider below canopy evaporation estimates for a more realistic attribution of evaporation to its different sources. These potential drawbacks of the model could be alleviated by combining the current algorithm with a machine learning-based approach for a next generation of the model. Likewise, ongoing activities of running the model at high (100 m–1 km) resolutions open possibilities to utilise the data for water and agricultural management applications

Modeling large-scale heatwave by incorporating enhanced urban representation

This study evaluates the impact of land surface models (LSMs) and urban heterogeneity [using local climate zones (LCZs)] on air temperature simulated by the Weather Research and Forecasting model (WRF) during a regional extreme event. We simulated the 2017 heatwave over Europe considering four scenarios, using WRF coupled with two LSMs (i.e., Noah and Noah-MP) with default land use/land cover (LULC) and with LCZs from the World Urban Database and Access Portal Tools (WUDAPT). The results showed that implementing the LCZs significantly improves the WRF simulations of the daily temperature regardless of the LSMs. Implementing the LCZs altered the surface energy balance partitioning in the simulations (i.e., the sensible heat flux was reduced and latent heat flux was increased) primarily due to a higher vegetation feedback in the LCZs. The changes in the surface flux translated into an increase in the simulated 2-m relative humidity and 10-m wind speed as well as changed air temperature within cities section and generated a temperature gradient that affected the temperatures beyond the urban regions. Despite these changes, the factor separation analysis indicated that the impact of LSM selection was more significant than the inclusion of LCZs. Interestingly, the lowest bias in temperature simulations was achieved when WRF was coupled with the Noah as the LSM and used WUDAPT as the LULC/urban representation

Estimation and prediction of avoidable health care costs of cardiovascular diseases and type 2 diabetes through adequate dairy food consumption: a systematic review and micro simulation modeling study

Background: Recent evidence from prospective cohort studies show a relationship between consumption of dairy foods and cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM). This association highlights the importance of dairy foods consumption in prevention of these diseases and also reduction of associated healthcare costs. The aim of this study was to estimate avoidable healthcare costs of CVD and T2D through adequate dairy foods consumption in Iran. Methods: This was a multistage modelling study. We conducted a systematic literature review in PubMed and EMBASE to identify any association between incidence of CVD and T2DM and dairy foods intake, and also associated relative risks. We obtained age- and sex-specific dairy foods consumption level and healthcare expenditures from national surveys and studies. Patient level simulation Markov models were constructed to predict the disease incidence, patient population size and associated healthcare costs for current and optimal dairy foods consumption at different time horizons (1, 5, 10 and 20 years). All parameters including costs and transition probabilities were defined as statistical distributions in the models, and all analyses were conducted by accounting for first and second order uncertainty. Results: The systematic review results indicated that dairy foods consumption was inversely associated with incidence of T2DM, coronary heart disease (CHD) and stroke. We estimated that the introduction of a diet containing 3 servings of dairy foods per day may produce a $0.43 saving in annual per capita healthcare costs in Iran in the first year due to saving in cost of CVD and T2DM treatment. The estimated savings in per capita healthcare costs were$8.42, $39.97 and$190.25 in 5, 10 and 20-years’ time, respectively. Corresponding total aggregated avoidable costs for the entire Iranian population within the study time horizons were $33.83,$661.31, $3,138.21 and$14,934.63 million, respectively. Conclusion: Our analysis demonstrated that increasing dairy foods consumption to recommended levels would be associated with reductions in healthcare costs. Further randomized trial studies are required to investigate the effect of dairy foods intake on cost of CVD and T2DM in the population