Search

Abstract Ensuring the security of China’s rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil properties and agricultural statistics from 2000 to 2017. The research evaluates six artificial intelligence (AI) models including machine learning (ML), deep learning (DL) models and their hybridization to predict rice production across China, particularly focusing on the main rice cultivation areas. These models were random forest (RF), extreme gradient boosting (XGB), conventional neural network (CNN) and long short-term memory (LSTM), and the hybridization of RF with XGB and CNN with LSTM based on eleven combinations (scenarios) of input variables. The main results identify that hybrid models have performed better than single models. As well, the best scenario was recorded in scenarios 8 (soil variables and sown area) and 11 (all variables) based on the RF-XGB by decreasing the root mean square error (RMSE) by 38% and 31% respectively. Further, in both scenarios, RF-XGB generated a high correlation coefficient (R2) of 0.97 in comparison with other developed models. Moreover, the soil properties contribute as the predominant factors influencing rice production, exerting an 87% and 53% impact in east and southeast China, respectively. Additionally, it observes a yearly increase of 0.16 °C and 0.19 °C in maximum and minimum temperatures (Tmax and Tmin), coupled with a 20 mm/year decrease in precipitation decline a 2.23% reduction in rice production as average during the study period in southeast China region. This research provides valuable insights into the dynamic interplay of environmental factors affecting China’s rice production, informing strategic measures to enhance food security in the face of evolving climatic conditions.

Comprehensive climate time series data is indispensable for monitoring the impacts of climate change. However, observational datasets often suffer from data gaps within their time series, necessitating imputation to ensure dataset integrity for further analysis. This study evaluated six univariate and multivariate imputation methods to infill missing values. These methods were applied to complete the subsets of time series data for precipitation, temperature, and relative humidity from Mosul station spanning 1980–2013. Artificial gaps of 5%, 10%, 20%, and 30% missing observations were introduced under scenarios of missing completely at random (MCAR) missing at random (MAR), and missing not at random (MNAR). Evaluation metrics including RMSE and Kling-Gupta Efficiency were utilized for performance evaluation. Results revealed that seasonal decomposition was the most effective univariate imputation method across all variables. For the multivariate imputation, kNN demonstrated superior performance in infilling the precipitation missing data under MCAR, while norm.predict exhibited optimal performance in the temperature missing data under all missing scenarios. Moreover, missForest was identified as the most suitable method for infilling missing relative humidity data. This study's methodology offers insights into selecting appropriate imputation methods for other climate stations, thereby enhancing the accuracy of the climate change effects analysis.

Study Region: Egypt is a country located in northeastern Africa. Study Focus: The research evaluated the random forest (RF) and extreme gradient boosting (XGB) as single models and the models' hybrid to predict the ETo for the baseline and future (2015–2099) period from Shared Socioeconomic Pathways (SSP1–26, SSP2–45 and SSP5–85) based on 18 GCMs models. New Hydrological Insights for the Region: The hybrid model has performed better than single models; compared RF and XGB to RF-XGB, the RMSE values were decreased in all zones esepically in zone 3 by 16.2 %, these results indicate that the highest performances of all models are observed in the middle and south Egypt, which exhibit the strongest correlation between temperature and ETo. For the SSP5–8.5 scenario, the ETo increased over the years for all zones; the ETo will increase by 4.38 %,3.71 %, 4.27 %, 2.16 %, 3.26 %, 1.35 %, 5.22 % at the year 2099 compared to the year 2015 for zone 1, 2, 3, 4, 5, 6 and 7 respectively. The Tmin and Tmax are the most critical factors that affect the ETo in all zones in the baseline and future scenarios. This study provides important insights into applying machine learning models to estimate ETo and its implications for future water management strategies. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.

Unemployment may cause problems for those who experience it for example depression and cause other social problems including criminal acts such as thuggery. This problem can only be eliminated if the root cause of unemployment is eliminated. Eliminating unemployment must be done comprehensively although every region always has unemployment due to various factors, and it would be wiser if in overcoming the problem of unemployment and its side excesses such as thuggery, the local potential of the region and the communal approach are put forward. Handling of these social problems will be applied in Dermo Hamlet, Dau Subdistrict, where many welding workshops are located, yet there are still many people of productive age who do not have permanent jobs or are unemployed. So that the proposed Science and Technology-Based Community Service program in collaboration with Youth Organization partners in Dermo Hamlet aims to overcome these social problems through organizing design training, welding training and OHS / Occupational Health and Safety socialization. The initial capital constraints in making this business even though it is simple are related to the funding budget that is still not available to buy welding equipment and other supporting tools, besides that it is also constrained related to skills. So that in this PKM, 1 set of welding machines is provided. And the indicator of program achievement is marked by understanding of the material by giving 10 Basic Welding certificates and 10 OHS certificates from the Mechanical Engineering Study Program.

Abstract The coefficients of uniformity Christiansen's uniformity coefficient (CU) and distribution uniformity (DU) are an important parameter for designing irrigation systems, and are an accurate measure for water lose. In this study, three machine learning algorithms Random forest (RF), extreme gradient boosting (XGB) and random forest-extreme gradient boosting (XGB-RF) were developed to predict the water distribution uniformity based on operating pressure, heights of sprinkler, discharge, nozzle diameter, wind speed, humidity, highest and lowest temperature for three different impact sprinklers (KA-4, FOX and 2520) for square and triangular system layout based on four scenarios (input combinations). The main findings were; the highest CU value was 86.7% in the square system of 2520 sprinkler under 200 kPa, 0.5 m height and 0.855 m3/h (Nozzle 2.5 mm). Meanwhile, in the triangular system, it was 87.3% under the same pressure and discharge and 1 m height. For applied machine learning, the highest values of R2 were 0.796, 0.825 and 0.929 in RF, XGB and XGB-RF respectively in the first scenario for CU. Moreover, for the DU, the highest values of R2 were 0.701, 0.479 and 0.826 in RF, XGB and XGB-RF respectively in the first scenario. The obtained results revealed that the sprinkler height had the lowest impact on modeling of the water distribution uniformity.

Abstract Water scarcity is a major challenge for irrigated agriculture, particularly in developing countries where access to meteorological data for calculating reference evapotranspiration (ET o ) is limited. Thus, this study explores the potential of two machine learning models (random forest (RF) and long short-term memory (LSTM)) and autoregressive integrated moving average (ARIMA) to forecast ET o . The investigation was conducted for four weather stations in Egypt, from 1982 to 2020. The machine learning models were evaluated using four combinations of inputs: maximum and minimum temperature, relative humidity, and wind speed. The best results for both RF and LSTM models were achieved with the first set of inputs that included all four variables at both regional and local scales. For the regional scale, RF and LSTM models achieved R 2 values of 0.85 and 0.86, respectively, with RMSE values of 0.69 and 0.68 mm/day. At the local scale, RF and LSTM models exhibited R 2 values ranging from 0.92 to 0.95 and 0.93 to 0.95, respectively, while RMSE ranged between 0.38 and 0.46 mm/day and 0.37–0.43 mm/day, respectively. Additionally, ARIMA models were employed for tim series analysis of the same ET o data. ARIMA (2,1,4) and ARIMA (2,1,3) were found to be the most suitable models for the local-scale analysis while ARIMA (2,1,4) was identified as the optimal model for the regional-scale analysis. For the local-scale analysis, R 2 values ranged from 0.86 to 0.91 and RMSE values ranged from 0.26 to 0.38. The regional scale analysis yielded an R 2 value of 0.89 and an RMSE value of 0.58 mm/day. The developed models can be used in places where meteorological data for forecasting ET o are limited.

Land use changes profoundly affect the equilibrium of soil organic carbon (SOC) sequestration and greenhouse gas emissions. With the current global climatic changes, it is vital to understand the influence of ecological restoration and conservation management on the dynamics of SOC under different land uses, especially in erosion-endangered Loess soils. Therefore, we investigated changes in SOC through a suit of labile fractions, namely: light fraction organic C (LFOC), heavy fraction organic C (HFOC), coarse particulate organic C (CPOC), fine particulate organic C (FPOC), and dissolved organic C (DOC), from two forests i.e., Robinia pseudoacacia (RP) and Platycladus orientalis (PO), with different ages, in comparison with farmland (FL). The SOC and STN contents significantly increased over 42 years in the RP forest where the contents of CPOC and FPOC were significantly higher than in the FL. Moreover, total SOC and its labile fractions, in the studied land use types, significantly correlated with soil CaCO3, pH, and STN contents, indicating their key roles in SOC sequestration. The results reported here from different vegetation with different ages provide a better understanding of SOC and STN alterations at different stages of vegetation restoration. Our findings suggest that long-term natural vegetation restoration could be an effective approach for SOC sequestration and soil conservation on the Loess soil.

Global warming has resulted in increases in the intensity, frequency, and duration of drought in most land areas at the regional and global scales. Nevertheless, comprehensive understanding of how water use efficiency (WUE), gross primary production (GPP), and actual evapotranspiration (AET)-induced water losses respond to exceptional drought and whether the responses are influenced by drought severity (DS) is still limited. Herein, we assess the fluctuation in the standardized precipitation evapotranspiration index (SPEI) over the Middle East from 1982 to 2017 to detect the drought events and further examine standardized anomalies of GPP, WUE, and AET responses to multiyear exceptional droughts, which are separated into five groups designed to characterize the severity of extreme drought. The intensification of the five drought events (based on its DS) increased the WUE, decreased the GPP and AET from D5 to D1, where both the positive and negative variance among the DS group was statistically significant. The results showed that the positive values of standardized WUE with the corresponding values of the negative GPP and AET were dominant (44.3% of the study area), where the AET values decreased more than the GPP, and the WUE fluctuation in this region is mostly controlled by physical processes, i.e., evaporation. Drought’s consequences on ecosystem carbon-water interactions ranged significantly among eco-system types due to the unique hydrothermal conditions of each biome. Our study indicates that forthcoming droughts, along with heightened climate variability, pose increased risks to semi-arid and sub-humid ecosystems, potentially leading to biome restructuring, starting with low-productivity, water-sensitive grasslands. Our assessment of WUE enhances understanding of water-carbon cycle linkages and aids in projecting ecosystem responses to climate change.

Drought is a costly natural disaster. The accuracy and applicability of different drought indexes in drought monitoring at different research areas are also different. Based on remote sensing (RS) and geographic information system technology, we propose a new RS-based drought index, Vegetation Drought Status Index (VDSI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and data from 81 verification points regarding the in situ soil drought status. The model was applied to drought monitoring in Xinjiang Uygur Autonomous Region of China. Based on the comprehensive influence of water absorption on the reflectance spectrum of vegetation and soil, the reflectance of soil in MODIS bands B6 and B7 is generally higher than that of vegetation, so the model can directly obtain the surface soil moisture index. The correlation analysis (R2 > 0.79) was valid; the change trends were the same; i.e., VDSI is a reliable referential index for agricultural drought monitoring. HIGHLIGHTS A new drought index based on remote sensing – Vegetation Drought State Index (VDSI) was proposed.; The model can directly obtain the surface soil moisture index.; The results are applicable to agricultural drought monitoring in arid and humid regions.;

Objective: Management of pregnancy complicated by severe early-onset fetal growth restriction (FGR) is one of the most challenging obstetrical issues. So far, there has not been a proven option for the treatment or improvement of this condition. Improper immune response during placentation leads to inadequate trophoblast invasion and impaired utero-placental perfusion. Pentoxifylline improves the endothelial function and induces vasodilation by reducing the inflammatory-mediated cytokines. We have evaluated the effect of Pentoxifylline on fetal-placental perfusion, neonatal outcome, and the level of oxidative stress markers before and after the intervention in the setting of severe early-onset FGR. Materials and methods: This study is a pilot randomized clinical trial on 40 pregnant women who had developed early-onset growth restricted fetus. Pentoxifylline and placebo were given with a dose of 400 mg per os two times daily until delivery. Serial ultrasound examination regarding fetal weight, amniotic fluid and also utero-placenta-fetal Doppler's were done. For the assessment of serum Antioxidant level, blood sampling was done once at the beginning of the study and again, at least, three weeks after the investigation. After delivery, umbilical-cord blood gas analysis, APGAR score at 1 and 5 min, NICU admission, and neonatal death were recorded and compared between the two groups. Results: Utero-placenta-fetal Doppler's in the Pentoxifylline group did not significantly change compared to the control group. Fetal weight gain was significantly higher in the Pentoxifylline group before (996.33 ± 317.41) and after (1616.89 ± 527.90) treatment (P = 0.002). Total serum antioxidant capacity significantly increased in the Pentoxifylline group (p

In spite of the huge advancements in both diagnosis and interventions, hormone refractory prostate cancer (HRPC) remains a major hurdle in prostate cancer (PCa). Metabolic reprogramming plays a key role in PCa oncogenesis and resistance. However, the dynamics between metabolism and oncogenesis are not fully understood. Here, we demonstrate that two multi-target natural products, cannabidiol (CBD) and cannabigerol (CBG), suppress HRPC development in the TRansgenic Adenocarcinoma of the Mouse Prostate (TRAMP) model by reprogramming metabolic and oncogenic signaling. Mechanistically, CBD increases glycolytic capacity and inhibits oxidative phosphorylation in enzalutamide-resistant HRPC cells. This action of CBD originates from its effect on metabolic plasticity via modulation of VDAC1 and hexokinase II (HKII) coupling on the outer mitochondrial membrane, which leads to strong shifts of mitochondrial functions and oncogenic signaling pathways. The effect of CBG on enzalutamide-resistant HRPC cells was less pronounced than CBD and only partially attributable to its action on mitochondria. However, when optimally combined, these two cannabinoids exhibited strong anti-tumor effects in TRAMP mice, even when these had become refractory to enzalutamide, thus pointing to their therapeutical potential against PCa.

Abstract Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.

Mesoscale combustor is one of the components that serves to generate heat on a micro power generator. As one of the components of a micro power generator, meso scale combustor serves to supply heat through the combustion process. The stability of the flame in the combustion chamber meso scale combustor is influenced by the temperature of the combustion chamber. One way to maintain a high temperature in the combustion chamber is to insert a flue gas stainless steel mesh resistor.. This research aims to prove the role of flue gas mesh resistors in stabilizing the flame on the meso scale combustor... The heptane liquid fuel flame was successfully stabilized at an equivalence ratio of ɸ 0.81 – 1.29 and a reactant flow velocity of 26.12 – 36.83 cm/s. The higher the rate of reactant flow, the higher the flame temperature until it reaches 502ºC. Combustor with flue gas mesh resistor is 10 mm away has a flammability limit that is not wider than a combustor without flue gas mesh resistor.

The Gully Consolidation and Highland Protection (GCHP) project is crucial in preventing gully erosion in the Loess Plateau. However, there are new problems after the completion of the GCHP project, such as secondary disasters caused by sudden changes in water flow paths. To study the impact of different GCHP measures on runoff and sediment production, we conducted a series of scouring experiments using the similarity principle, taking Fengbao Gully, Xifeng District, Qingyang City, Gansu Province, as a prototype. Moreover, three scenarios in the GCHP project (landfill (LT), nondrained terraced (NDT), and terrace with a drainage system (DT)) are established. Seasonal rainfall simulation experiments are conducted with a constant slope. The results showed that during summer rainfall, the 10 min runoff depth of LT is 67.83~276.03% higher than that of NDT. However, in spring and autumn, the runoff depth of NDT is 4.12~39.84% higher than LT’s. The sediment yield of LT is 0.06–5.58 times higher than that of NDT and 1.91–25.58 times higher than that of DT. The sediment yield of NDT is 0.46–4.02 times higher than that of LT and 2.27–23.93 times higher than that of DT, indicating that, under the same conditions, the effect of slope replacement with terraces for GCHP is better than that of gully head landfill in reducing soil erosion and secondary geological disasters. Furthermore, imperfect terrace construction can result in increased sediment yield. This study provides a scientific basis for the maintenance and later management of GCHP and helps implement soil and water conservation measures in similar regions worldwide.

Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.

The increasing number of motorized vehicles has a direct impact on exhaust gas air pollution. The air pollution in urban areas is dominated by motorized vehicle emissions, along with pollution problems. This study aims to reduce motor vehicle emissions by using a catalytic converter design made from a brass plate catalyst in the shape of a honeycomb. Honeycomb-shaped brass is suitable for catalysts in the catalytic converter. Besides being easy to obtain and cheap in price, the catalyst can reduce and oxidize exhaust gases well, making it suitable as a catalyst material. The method used in this research is the experimental method. It is started from the design of the catalytic converter house and determining the type of catalyst to the process of making the catalytic converter with a honeycomb-shaped brass plate. Then, testing to determine the emission of exhaust gases produced is required. The last step is to compare it without using a catalytic converter or standard conditions. From the results of the emission test, it was found that the use of a catalytic converter made from a brass plate catalyst in the shape of a honeycomb can reduce HC and CO emissions, while CO2 emissions have increased. A decrease in HC gas emissions by 19.1% for a single catalytic converter and 33.7% for a dual catalytic converter is better compared to without using a catalytic converter or standard conditions. Reduced CO gas emissions by 23.8% for a single catalytic converter and 43.1% for a dual catalytic converter are compared to without using a catalytic converter. Meanwhile, CO2 gas emissions increased by 60.7% for a single catalytic converter, and 81.6% for multiple catalytic converters are compared without using a catalytic converter. This is a result of the addition of oxygen to the oxidation process that running smoothly.

Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980–2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield.

Abstract Background Palmitoylethanolamide (PEA) is a pleiotropic endogenous lipid mediator currently used as a “dietary food for special medical purposes” against neuropathic pain and neuro-inflammatory conditions. Several mechanisms underlie PEA actions, among which the “entourage” effect, consisting of PEA potentiation of endocannabinoid signaling at either cannabinoid receptors or transient receptor potential vanilloid type-1 (TRPV1) channels. Here, we report novel molecular mechanisms through which PEA controls mast cell degranulation and substance P (SP)-induced histamine release in rat basophilic leukemia (RBL-2H3) cells, a mast cell model. Methods RBL-2H3 cells stimulated with SP were treated with PEA in the presence and absence of a cannabinoid type-2 (CB2) receptor antagonist (AM630), or a diacylglycerol lipase (DAGL) enzyme inhibitor (OMDM188) to inhibit the biosynthesis of the endocannabinoid 2-arachidonoylglycerol (2-AG). The release of histamine was measured by ELISA and β-hexosaminidase release and toluidine blue staining were used as indices of degranulation. 2-AG levels were measured by LC-MS. The mRNA expression of proposed PEA targets (Cnr1, Cnr2, Trpv1, Ppara and Gpr55), and of PEA and endocannabinoid biosynthetic (Napepld, Dagla and Daglb) and catabolic (Faah, Naaa and Mgl) enzymes were also measured. The effects of PEA on the activity of DAGL-α or -β enzymes were assessed in COS-7 cells overexpressing the human recombinant enzyme or in RBL-2H3 cells, respectively. Results SP increased the number of degranulated RBL-2H3 cells and triggered the release of histamine. PEA counteracted these effects in a manner antagonized by AM630. PEA concomitantly increased the levels of 2-AG in SP-stimulated RBL-2H3 cells, and this effect was reversed by OMDM188. PEA significantly stimulated DAGL-α and -β activity and, consequently, 2-AG biosynthesis in cell-free systems. Co-treatment with PEA and 2-AG at per se ineffective concentrations downmodulated SP-induced release of histamine and degranulation, and this effect was reversed by OMDM188. Conclusions Activation of CB2 underlies the inhibitory effects on SP-induced RBL-2H3 cell degranulation by PEA alone. We demonstrate for the first time that the effects in RBL-2H3 cells of PEA are due to the stimulation of 2-AG biosynthesis by DAGLs.

Cannabinoid type 1 (hCB1) and type 2 (hCB2) receptors are pleiotropic and crucial targets whose signaling contributes to physiological homeostasis and its restoration after injury. Being predominantly expressed in peripheral tissues, hCB2R represents a safer therapeutic target than hCB1R, which is highly expressed in the brain, where it regulates processes related to cognition, memory, and motor control. The development of hCB2R ligands represents a therapeutic opportunity for treating diseases such as pain, inflammation and cancer. Identifying new selective scaffolds for cannabinoids and determining the structural determinants responsible for agonism and antagonism are priorities in drug design. In this work, a series of N-[1,3-dialkyl(aryl)-2-oxoimidazolidin-4-ylidene]-aryl(alkyl)sulfonamides is designed and synthesized and their affinity for human hCB1R and hCB2R is determined. Starting with a scaffold selected from the NIH Psychoactive Drug Screening Program Repository, through a combination of molecular modeling and structure–activity relationship studies, we were able to identify the chemical features leading to finely tuned hCB2R selectivity. In addition, an in silico model capable of predicting the functional activity of hCB2R ligands was proposed and validated. The proposed receptor activation/deactivation model enabled the identification of four pure hCB2R-selective agonists that can be used as a starting point for the development of more potent ligands.

The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.

Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria.

The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO _2 ) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO _2 emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.

In continuing our investigation on the chemical diversity of Algerian plants, we examined Centaurea omphalotricha, whose chemical composition has been poorly studied. The present work was aimed at characterizing the secondary metabolite pattern of the CHCl3 extract of the aerial parts of this plant that displayed antiproliferative properties in a preliminary screening on HeLa cell line. The chemical analysis led us to characterize the bioactive oxygenated terpenoid fraction which includes, within major known metabolites, two new minor sesquiterpene lactones, centaurolide-A (1) and centaurolide-B (2). The structures of two compounds exhibiting the 12,8-guaianolide skeleton were determined by spectroscopic methods as well as by chemical correlation with inuviscolide (3), a well-known bioactive guaianolide isolated from Dittrichia (=Inula) viscosa. Centaurolides A and B represent the first report of 8,12-guaianolide skeleton in Centaurea genus. The effect of new compounds 1 and 2 and inuviscolide (3) on HeLa cell has also been evaluated.

The rapid increase of the number of motor vehicles, especially motorcycles, makes serious problem caused by the pollution from gas emission of fossil fuel combustion. The problems related to human health and the erosion on ozone layer is credited to its gas emission. Several dangerous and toxic gasses such as Hydrocarbon (HC), Carbon Monoxide (CO), and Nitrogen Oxide (NOx), Sulphur Dioxide (SO2), and Lead (Pb) are emitted during IC engine operation. To manage those dangerous gasses, catalytic converter which converts the gasses into more eco-friendly CO2H2O and N2 may be employed. In this research, a specially designed catalytic converter made from brass (CuZn) wire in the form of spider web pattern was fitted into exhaust system of a standard motorcycle. The performance of the converter for several engine rotation speeds was examined by measuring CO and HC of exhaust by using Gas Analyzer. By comparing the exhaust of a standard exhaust system and modified exhaust system (fitted with converter), it can be concluded that the converter was able to decrease the exhaust emission gas. For HC content, the percentage of decrease was as much as 36,88 % for converter fitted exhaust system compared to 61.12% for standard one. For CO content, the decrease percentage was 19.90% compared to 80.10% for standard one.

Abstract Background Maternal hypotension is a common complication after spinal anesthesia for cesarean section (CS). In this study we investigated the role of leg elevation (LE) as a method for prevention of post-spinal hypotension (PSH) for cesarean section. Methods One hundred and fifty full term parturients scheduled for CS were included in the study. Patients were randomized into two groups: Group LE (leg elevation group, n = 75) and group C (Control group, n = 75). Spinal block was performed in sitting position after administration of 10 mL/Kg Ringer’s lactate as fluid preload. After successful intrathecal injection of local anesthetic, Patients were positioned in the supine position. Leg elevation was performed for LE group directly after spinal anesthesia and maintained till skin incision. Intraoperative hemodynamic parameters (Arterial blood pressure and heart rate), intra-operative ephedrine consumption, incidence of PSH, and incidence of nausea and vomiting were reported. Results LE group showed lower incidence of PSH (34.7% Vs 58.7%, P = 0.005) compared to the control group. Arterial blood pressure was higher in the LE group compared to the control group in the first two readings after spinal block. Other readings showed comparable arterial blood pressure and heart rate values between both study groups; however, LE showed less ephedrine consumption (4.9 ± 7.8 mg Vs 10 ± 11 mg, P = 0.001). Conclusion LE performed immediately after spinal block reduced the incidence of PSH in parturients undergoing CS. Trial registration The study was registered at Pan African Clinical Trials Registry system on 5/10/2015 with trial number PACTR201510001295348 .

Objective:The present study aimed to compare dentoskeletal changes in mandibular-deficient patients treated with Bionator and Farmand appliances.Methods:This study included 54 subjects treated for class II division I malocclusion. All subjects fulfilled the following criteria: ANB>5°, SNB5 mm. The Bionator group consisted of 27 patients (15 girls, 12 boys) with the mean age of 11 (SD 1) years and the Farmand group consisted of 27 patients (17 girls, 10 boys) with the mean age of 11.1 (SD 1.4) years. Statistical analyses were performed using t-test, paired t-test, Wilcoxon, and Mann–Whitney test.Results:In the Farmand group, SNB significantly increased from 74.3° (SD 1.7) to 77.6° (SD 2.3) and ANB decreased by 3.2° (SD 1.7) (p

Bone is a highly complex and metabolically active tissue undergoing a continuous remodeling process, which endures throughout life. A complex cell-signaling system that plays role in regulating different physiological processes, including bone remodeling, is the endocannabinoid system (ECS). Bone mass expresses CB1 and CB2 cannabinoid receptors and enzymatic machinery responsible for the metabolism of their endogenous ligands, endocannabinoids (AEA and 2-AG). Exogenous AEA is reported to increase the early phase of human osteoblast differentiation in vitro. However, regarding this cell context little is known about how endocannabinoids and endocannabinoid-related N-acylethanolamines like PEA and OEA are modulated, in vitro, during cell differentiation and, in vivo, over time up to adulthood. Here we characterized the endocannabinoid tone during the different phases of the osteoblast differentiation process in MC3T3-E1 cells, and we measured endocannabinoid levels in mouse femurs at life cycle stages characterized by highly active bone growth (i.e., of juvenile, young adult, and mature adult bone). Endocannabinoid tone was significantly altered during osteoblast differentiation, with substantial OEA increment, decline in 2-AG and AEA, and consistent modulation of their metabolic enzymes in maturing and mineralized MC3T3-E1 cells. Similarly, in femurs, we found substantial, age-related, decline in 2-AG, OEA, and PEA. These findings can expand existing knowledge underlying physiological bone cell function and contribute to therapeutic strategies for preventing bone-related metabolic changes accruing through lifespan.

Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.

Background and Aims: Central odontogenic fibroma is a rare odontogenic neoplasm that originates from odontogenic ectomesenchyme. Most cases occur in the mandible and between the ages of 11 and 39 years. The neoplasm shows a definite female preponderance, with a ratio of 2.2:1 and has a very low recurrence rate. The aim of this article was to report a case of this rare lesion which was accidental found. Case Report: A 11-year-old female, during her orthodontic treatment and without any complain and any sign or symptoms, performed a panoramic image for assessing the permanent teeth buds. In the panoramic view a unilocular radiolucent lesion with well-defined and corticated borders in the right mandibular body region was evident. The lesion was treated with simple excision. Simple type of central odontogenic fibroma histopathologic features was observed in the microscopic view. Conclusion: Although the central odontogenic fibroma is a rare benign neoplasm, however, a careful evaluation of radiographic images is important for early detection of lesions because the patient may have no clinical symptoms such as swelling, pain and paresthesia.

Soils in the coastal region of Syria (CRoS) are one of the most fragile components of natural ecosystems. However, they are adversely affected by water erosion processes after extreme land cover modifications such as wildfires or intensive agricultural activities. The main goal of this research was to clarify the dynamic interaction between erosion processes and different ecosystem components (inclination, land cover/land use, and rainy storms) along with the vulnerable territory of the CRoS. Experiments were carried out in five different locations using a total of 15 erosion plots. Soil loss and runoff were quantified in each experimental plot, considering different inclinations and land uses (agricultural land (AG), burnt forest (BF), forest/control plot (F)). Observed runoff and soil loss varied greatly according to both inclination and land cover after 750 mm of rainfall (26 events). In the cultivated areas, the average soil water erosion ranged between 0.14 ± 0.07 and 0.74 ± 0.33 kg/m2; in the BF plots, mean soil erosion ranged between 0.03 ± 0.01 and 0.24 ± 0.10 kg/m2. The lowest amount of erosion was recorded in the F plots where the erosion ranged between 0.1 ± 0.001 and 0.07 ± 0.03 kg/m2. Interestingly, the General Linear Model revealed that all factors (i.e., inclination, rainfall and land use) had a significant (p < 0.001) effect on the soil loss. We concluded that human activities greatly influenced soil erosion rates, being higher in the AG lands, followed by BF and F. Therefore, the current study could be very useful to policymakers and planners for proposing immediate conservation or restoration plans in a less studied area which has been shown to be vulnerable to soil erosion processes.

An integrated modeling approach analyzing water demand and supply balances under management options in a river basin is essential for the management and adaptive measures of water resources in the future. This study evaluated the impacts of climate change on the hydrological regime by predicting the change in both monthly and seasonal streamflow, and identified water supply and demand relations under supply management options and environmental flow maintenance. To reach a better understanding of the consequences of possible climate change scenarios and adaptive management options on water supply, an integrated modeling approach was conducted by using the soil and water assessment tool (SWAT) and water evaluation and planning model (WEAP). Future scenarios were developed for the future period: 2060s (2051–2070), using an ensemble of three general circulation model (GCM) simulations: GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR, driven by the climate projection for representative concentration pathways (RCPs): 6.0 (medium emission scenario). The results indicated that, firstly, the future streamflow will decrease, resulting in a decline of future water availability. Secondly, water supply under natural flow conditions would support 46,167 ha of irrigation schemes and the water shortages will be more noticeable when environmental flow maintenance was considered. The study concludes that reservoir construction would be necessary for agriculture mitigation and adaptation to climate change. Furthermore, the water resources management options considering both supply and demand management are more effective and useful than supply management only, particularly in dealing with climate change impacts.