Using an interpretable deep learning model for the prediction of riverine suspended sediment load

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

An interpretable deep learning model to map land subsidence hazard

The main goal of this research is the interpretability of deep learning (DL) model output (e.g., CNN and LSTM) used to map land susceptibility to subsidence hazard by means of different techniques. For this purpose, an inventory map of land subsidence (LS) is prepared based on fieldwork and a record of LS presence points, and 16 features controlling LS were mapped. Thereafter, 11 effective features controlling LS were identified by means of a particle swarm optimization (PSO) algorithm, which was then used as input in the CNN and LSTM predictive models. To address the inherent black box nature of DL models, six interpretation methods (feature interaction, permutation importance plot (PFIM), bar plot, SHapley Additive exPlanations (SHAP) main plot, heatmap plot, and waterfall plot) were used to interpret the predictive model outputs. Both models (CNN and LSTM) had AUC > 90 and therefore provided excellent accuracy for mapping LS hazard. According to the most accurate model-the CNN predictive model-the range from very low to very high hazard classes occupied 20%, 20%, 25%, 16.3%, and 18.7% of the study area, respectively. According to three plots (bar plot, SHAP main plot, and heatmap plot), which were constructed based on the SHAP value, distance from the well, GDR and DEM were identified as the three most important features with the highest impact on the DL model output. The results of the waterfall plot indicate two effective features consisting of distance from the well and coarse fragment, and two effective features comprising landuse and DEM, contributed negatively and positively to LS, respectively. Overall, these explanation techniques can address critical concerns with respect to the interpretability of sophisticated DL predictive models.

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m-3 and 73 μg m-3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.

Interpretability of simple RNN and GRU deep learning models used to map land susceptibility to gully erosion

Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modelling techniques and interpretation methods used in this study proved to be helpful in generating spatial maps of soil erosion hazard, especially gully erosion. Their interpretability can support watershed sustainable management.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning - an updated version of machine learning techniques - and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

The effect of beta-lactoglobulin nanocapsules containing astaxanthin and 5-fluorouracil on the antioxidant enzymes activity of superoxide dismutase, catalase and glutathione peroxidase in HCT116 colorectal cancer cell line

Background:

The use of nanoparticle drug delivery systems to enhance the therapeutic efficacy and reduce the side effects of anticancer drugs is taken into consideration. Astaxanthin (ATX) is a natural xanthophyll carotenoid with antioxidant, anti-inflammatory, and antiapoptotic properties used to prevent and treat some cancers.

background:

The use of nanoparticle drug delivery systems to enhance the therapeutic efficacy and reduce the side effects of anti-cancer drugs is taken into consideration. Astaxanthin (ATX) is a natural xanthophyll carotenoid with antioxidant, anti-inflammatory, and anti-apoptotic properties that has been used in the prevention and treatment of some cancers.

Objectives:

In the present study, the antioxidant effect of beta-lactoglobulin (β-LG) nanocapsules containing ATX and 5-fluorouracil (5-FU; the first-line therapy for colorectal cancer) on the antioxidant enzymes activity of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX) in HCT116 colorectal cancer cell line was examined.

objective:

In the present study, the antioxidant effect of beta-lactoglobulin (β-LG) nanocapsules containing ATX and 5-fluorouracil (5-FU; the first-line therapy for colorectal cancer) on the antioxidant enzymes activity of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX) in HCT116 colorectal cancer cell line was examined.

Methods:

In this experimental study, HCT116 cells were treated with different treatments of encapsulation of ATX in β-LG, encapsulation 5-FU in β-LG, co-encapsulation of ATX and 5-FU in β-LG, free ATX, free 5-FU, free ATX and free 5-FU, or β-LG nanocapsules without drugs for 24, 48 and 72 hours. There is a control group in which HCT116 cells were not treated with any drug. Then, 50% inhibitory concentration (IC50) and cell viability were determined using an MTT assay. The antioxidant enzyme activity of SOD, CAT, and GPX was measured by a colorimetric method in HCT116 cells.

method:

In this experimental study, HCT116 cells were treated with different treatments of encapsulation of ATX in β-LG, encapsulation 5-FU in β-LG, co-encapsulation of ATX and 5-FU in β-LG, free ATX, free 5-FU, free ATX and free 5-FU, or β-LG nanocapsules without drugs for 24, 48 and 72 hours. There is a control group that HCT116 cells not treated with any drug. Then, 50% inhibitory concentration (IC50) and cell viability were determined using an MTT assay. The antioxidant enzyme activity of SOD, CAT and GPX was measured by colorimetric methods in HCT116 cells.

Results:

Different treatments reduced the cell viability and increased apoptotic cells in a time-dependent manner, which was significant for beta-lactoglobulin nanocapsules treatment (P<0.05). It means receiving more 5-FU or ATX in the encapsulated form by HCT116 cells. The antioxidant enzyme activity of SOD, CAT, and GPX in HCT116 cells treated with beta-lactoglobulin nanocapsule treatment significantly increased compared to the control group (P<0.001). Moreover, the antioxidant activity of these enzymes in different treatments containing ATX (free or encapsulation) was significantly higher than in other treatments (P<0.05). The most increase in the activity of antioxidant enzymes is recorded in the treatment of nanocapsules containing ATX and 5-FU simultaneously.

result:

Different treatments reduced the cell viability and increased apoptotic cells in a time-dependent manner, which this reduction was significant for beta-lactoglobulin nanocapsules treatments (P&amp;amp;amp;amp;lt;0.05). It means receiving more 5-FU or ATX in encapsulated form by HCT116 cells. The antioxidant enzymes activity of SOD, CAT and GPX in HCT116 cells treated with beta-lactoglobulin nanocapsules treatments significantly increased compared to the control group (P&amp;amp;amp;amp;lt;0.001). Also, the antioxidant activity of these enzymes in different treatments containing ATX (free or encapsulation) was significantly higher than other treatments (P&amp;amp;amp;amp;lt;0.05). The most increase in the activity of antioxidant enzymes is recorded in the treatment of nanocapsules containing ATX and 5-FU simultaneously.

Conclusion:

Increased activity of antioxidant enzymes in addition to the induction of apoptosis in colorectal cancer cells by various treatments of beta-lactoglobulin nanocapsules indicates more effective drug administration in encapsulated form as well as synergistic thera[peutic effects of ATX and 5-FU. Moreover, the main increase in antioxidant enzyme activity may be related to ATX.

other:

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Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence

This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two important plains - the Minab and Shamil-Nian plains - in the Hormozgan province, southern Iran. The important features controlling LS hazard were identified by ridge regression. Then, two EDL models were constructed by stacking (SEDL) and voting (VEDL) five dense deep learning (DL) models (model 1 to model 5) for mapping LS hazard. Thereafter, the predictive model performance was assessed by a precision-recall curve and Kolmogorov-Smirnov (KS) plot. A partial dependence plot (PDP), individual conditional expectation plots (ICEP), game theory, and a sensitivity analysis were used for the interpretability of the predictive DL model. According to SEDL - a model with higher accuracy - 34% (1624 km²), 14.7% (698 km²), and 19.2% (912 km²) of the total area were classified as being of very low, low, and moderate hazards, whereas 17.7% (845 km²) and 14.4% (683 km²) of area were classified as being of high and very high hazards, respectively. Based on all interpretability techniques, aquifer loss or groundwater drawdown is the most important feature controlling LS hazard, and it having the greatest impact on the SEDL model output. Based on a Taylor diagram and R² as model performance assessment indicators, SEDL-AL (with R² > 95% for training and test datasets) performed better than SEDL for quantify LS rate, the rate of LS ranging between 0 and 48.1 cm. The highest rate of LS occurred in the Minab plain - an area located downstream of the Minab Esteghlal dam. SEDL-AL was used to quantify the uncertainty associated with the LS rate. The observed values fell within predictions provided by SEDL-AL, which indicates a high accuracy of our predictive model. Overall, our newly developed modeling techniques are helpful tools for the spatial mapping of LS susceptibility and rate, and its uncertainty.

High-resolution, spatially resolved quantification of wind erosion rates based on UAV images (case study: Sistan region, southeastern Iran)

Estimating the quantitative distribution of wind erosion rates is one of the most important requirements for managing affected environments and optimizing locations to control wind erosion. This study develops high-resolution maps for wind erosion with unmanned aerial vehicles or UAV images in the Sistan region-an area in southeastern Iran with severe wind erosion. Aerial imaging by UAV was done during period of erosive winds. Changes in the amount of wind erosion were measured for 7 months. Digital elevation models or DEM with a spatial resolution of 6 mm, orthophoto mosaic images with a resolution of 3 mm, were prepared before and after the erosion event. Three erosive facies consisting of surface, edge, and blowout were identified. The amount of erosion in different geomorphological landscapes or facies was measured according to differences of DEMs (DOD). The effect of physical factors of the geomorphological landscapes on wind erosion was investigated by calculating the correlation between the erosion, roughness, and slope in the geomorphological landscapes. The results showed that the highest and lowest mean of eroded soil were 22 mm and 4 mm in the blowout and surface facies, respectively. The average rate of wind erosion was 201 t/ha during the study period, which indicates the high intensity of wind erosion in the Sistan plain. Overall, UAV-as an aerial imaging technique collecting ground data-can be a helpful tool in the aeolian geomorphology especially for collecting data for measuring the rate of soil erosion by the wind in the aeolian landscapes located in remote regions.

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source

Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative effects. This study aimed to classify the susceptibility of dust sources in the Middle East (ME) by developing two novel deep learning (DL) hybrid models based on the convolutional neural network-gated recurrent unit (CNN-GRU) model, and the dense layer deep learning-random forest (DLDL-RF) model. The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. Game theory was used for the interpretability of the DL model's output. Predictive DL models were constructed by dividing datasets randomly into train (70%) and test (30%) groups, six statistical indicators being then applied to assess the DL hybrid model performance for both datasets (train and test). Among 13 potential features (or variables) controlling dust sources, seven variables were selected as important and six as non-important by DA, respectively. Based on the DLDL-RF hybrid model - a model with higher accuracy in comparison with CNN-GRU-23.1, 22.8, and 22.2% of the study area were classified as being of very low, low and moderate susceptibility, whereas 20.2 and 11.7% of the area were classified as representing high and very high susceptibility classes, respectively. Among seven important features selected by DA, clay content, silt content, and precipitation were identified as the three most important by game theory through permutation values. Overall, DL hybrid models were found to be efficient methods for prediction purposes on large spatial scales with no or incomplete datasets from ground-based measurements.

Fingerprinting the spatial sources of fine-grained sediment deposited in the bed of the Mehran River, southern Iran

Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: (1) traditional range test (TRT), Kruskal–Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT + KW + DFA); (2) traditional range test using mean values (RTM) and two additional tests (RTM + KW + DFA); (3) TRT + KW + PCA (principle component analysis), and; 4) RTM + KW + PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT + KW + DFA and RTM + KW + DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10–60% (overall mean contribution 36%), 0.3–16% (overall mean contribution 6%) and 38–77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT + KW + PCA generated respective corresponding contributions of 1–42% (overall mean 20%), 0.5–30% (overall mean 12%) and 55–84% (overall mean 68%) compared with 17–69% (overall mean 41%), 0.2–12% (overall mean 5%) and 29–76% (overall mean 54%) using the final tracers selected by RTM + KW + PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT + KW + PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT + KW + DFA and RTM + KW + DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM + KW + PCA (mean MAE = 14% and mean RMSE = 16%) and GLUE with TRT + KW + PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.

Evaluation of pollution indices, health hazards and source identification of heavy metal in dust particles and storm trajectory simulation using HYSPLIT model (Case study: Hendijan center dust, southwest of Iran)

Atmospheric dust is one of the most recent environmental pollutions in Iran. This study examines the concentration of heavy metals and the assessment of environmental and human health risk in the dust samples of Hendijan region as one of the most important centers of wind erosion in the southwestern of Iran. ICP-MSS analysis was performed on 18 samples of fine dust to specify the concentration of heavy metals. Studies showed that the highest concentrations of metals in these fine dust samples belong to Cr, Ni, Zn, Cu, As, Pb and Cd, respectively. Examining fine dust's pollution assessment showed that the highest enrichment and geo-accumulation index belong to As, Ni and Cr metals. Environmental risk assessment shows the low environmental risk of these fine dusts. The hazard quotient in children and adults belongs to Cr, As and Ni, respectively. Human health risk assessment also showed that the highest absorption of metals in both children and adults is through ingestion. The non-carcinogenic risk of heavy metals of dust samples in children is about 9 times more than adults. The highest risk of cancer in the adult group belongs to Ni metal and in the group of children belongs to As and Ni metal. PCA analysis showed that As, Cu, Cd, Cr and Ni are of anthropogenic origin and Zn and Pb are of geogenic origin. The source of the dust phenomenon with the HYSPLIT model and the backward method indicates the tracking of this dust mass through Iraq, and its probable origin was assessed in the centers of northern Iraq and southeastern Syria.

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001-2015 via a combination of three indices for vegetation (NPP-net primary production, NDVI-normalized difference vegetation index, LAI-leaf area index) and two climate indices (LST-land surface temperature, P-precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann-Kendall non-parametric test, and the Theil-Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest-southeast belt spanning central Iran, which has experienced significant vegetation decline (2001-2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran's major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.

Spatial modelling of soil salinity: deep or shallow learning models?

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0-5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.

Detection and prediction of lake degradation using landscape metrics and remote sensing dataset

Monitoring changes in natural ecosystems is considered essential to natural resource management. Despite the global importance of the lakes' quality monitoring, there is currently a research gap in the simultaneous predictive modeling of lakes' land-use changes and ecosystem measurements. In the present study for projecting the water bodies of lakes and their surrounding ecosystems, the land-use changes and the landscape analysis of different periods, i.e., 1987, 2002, 2018, and 2030, are studied using remote sensing data and various metrics. The trend of land-use and landscape changes is projected for 2030. The results indicate significant degradation of rangelands and forests due to the conversion to agriculture and construction and the declining trend of lakes' water body and their transformation to salt lake and salt lands. The increase of agricultural lands and the overuse of groundwater wells upstream of the lakes could be one of the reasons for this decline. Decreasing the lakes' water body and subsequently increasing salt lands are considered a severe threat to human health and the ecosystem services of the lakes. Besides, the dust generated by salt lands could also decrease crop yield in the study area.

Mapping wind erosion hazard with regression-based machine learning algorithms

Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.

A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM_2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.

Mapping the spatial sources of atmospheric dust using GLUE and Monte Carlo simulation

Atmospheric dust has many negative impacts within different ecosystems and it is therefore beneficial to assemble reliable evidence on the key sources of the dust problem. In this study, for first time, two different source modelling approaches comprising generalized likelihood uncertainty estimation (GLUE) and Monte Carlo simulation were applied to map spatial source contributions to atmospheric dust samples collected in Ahvaz, Khuzestan province, Iran. A total of 264 surficial soil samples were collected from five potential spatial dust sources. Additionally, nine dust samples were collected in February 2015. The performance of both GLUE and Monte Carlo simulation for quantifying uncertainty associated with the source contributions predicted using an un-mixing model were assessed and compared using mean absolute fit (MAF) and goodness-of-fit (GOF) estimators as well as 14 virtual sediment mixtures (VSM). Finally, the erodible fraction (EF) of topsoils and HYSPLIT model were used as further tests for validating the results of the GLUE and Monte Caro simulation. Based on both uncertainty modelling approaches, the loamy sand soil texture was recognized as the main spatial source of the target dust samples. Silty clay soils were estimated to be the least important spatial source of the target dust samples using the two modelling approaches. Both GLUE and Monte Carlo simulation returned MAF and GOF estimates >80%, with Monte Carlo performing slightly better. Based on the virtual mixture tests, the RMSE and MAE of the Monte Carlo simulation (<13.5% and <11%, respectively) was better than for GLUE (<20% and <16.3%, respectively). Spatial source maps generated using both GLUE and Monte Carlo simulation were consistent with the EF map generated using multiple regression (MR) and with routes dust transportation detected by HYSPLIT. Therefore, we recommend that future research into to the sources of atmospheric dust pollution integrates modelling approaches, VSM, EF and HYSPLIT model to quantify and map dust provenance reliably.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

PURPOSE

This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science.

METHODS

Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017.

SCOPE

Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing.

CONCLUSIONS

The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

Correction to: Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

The original publication of this paper contains a mistake. The correct University name of the 3rd affiliation is shown in this paper.

Monte Carlo fingerprinting of the terrestrial sources of different particle size fractions of coastal sediment deposits using geochemical tracers: some lessons for the user community

A sediment source fingerprinting method, including a Monte Carlo simulation framework, was used to quantify the contributions of terrestrial sources of fine- (< 63 μm) and coarse-grained (63-500 μm) sediments sampled from three categories of coastal sediment deposits in the Jagin catchment, south-east of Jask, Hormozgan province, southern Iran: coastal dunes (CD), terrestrial sand dunes or onshore sediments (TSD), and marine or offshore sediments (MD). Forty-nine geochemical properties were measured in the two size fractions and a three-stage statistical process consisting of a conservation test, the Kruskal-Wallis H test, and stepwise discriminant function analysis (DFA) was applied to select final composite fingerprints for terrestrial source discrimination. Based on the statistical tests, four final fingerprints comprising Be, Ni, K and Cu and seven final fingerprints consisting Cu, Th, Be, Al, La, Mg and Fe were selected for discriminating terrestrial sources of the coastal fine- and coarse-grained sediments, respectively. Two geological spatial sources, including Quaternary (clay flat, high and low level fans and valley terraces) and Palaeocene age deposits, were identified as the main terrestrial sources of the fine-grained sediment sampled from the coastal deposits. A geological spatial source consisting of sandstone with siltstone, mudstone and minor conglomerate (Palaeocene age deposits) was identified as the main terrestrial source for coarse-grained sediment sampled from the coastal deposits.

Fingerprinting sources of reservoir sediment via two modelling approaches

Reliable quantitative information about sediment sources is a key requirement for river catchment management, especially in settings with high sediment loads. This study explores the potential for using source fingerprinting techniques to establish the relative contribution of three sub-basins to the sediment deposited in a reservoir impounded by an earth dam located at the outlet of the Lavar watershed, in Hormozgan Province, southern Iran. The three sub-basins feeding the reservoir are characterized by complex topography and underlying geology. The source material and target sediment samples were analyzed for 53 potential geochemical tracers, including trace elements and rare earth elements (REEs) and their ratios. Stepwise discriminant function analysis (DFA) was applied to select optimum composite fingerprints from those fingerprint properties passing the range test and we compared two different modelling procedures to estimate the relative contribution of the three sub-basins to the sediment deposited in the reservoir. The first involves a Bayesian mixing model within a Markov Chain Monte Carlo framework (BM) and, the second, an un-mixing model within a Monte Carlo simulation framework (UM). The latter model permits the use of ratio properties, which represents a novel aspect of our study. Particular attention was directed to the uncertainty associated with the source contribution estimates provided by the two models. A goodness of fit estimator was employed to evaluate the results of the UM. Both modelling procedures demonstrated that the southern sub-basin was the main source of the majority of samples we collected from the reservoir. The BM model indicated that the central sub-basin was the dominant source of two samples (S6 and S8). Overall, the results provided by the BM model for the source of seven sediment samples (S1, S2, S3, S4, S5, S7 and S9) are compatible with those provided by the UM model and the central sub-basin was recognized as the most important source supplying sediment in the study area. Both approaches offer potential for using geochemical fingerprinting to quantify spatial sediment source contributions and the uncertainty associated with those estimates.

XtalFluor-E® mediated proto-functionalization of N-vinyl amides: access to N-acetyl N,O-acetals

XtalFluor-E® has been extensively used in a broad range of reactions in the past few years. Here we report its use with protic nucleophiles in a catalytic manner for the in situ generation of protons that lead to the proto-functionalization of activated olefins. Utilizing the latter protocol, proto etherification of enamides gives rise to N,O-acetals in nearly quantitative yields.

Effect of 5% benzocaine gel on relieving pain caused by fixed orthodontic appliance activation. A double-blind randomized controlled trial

AIM

To compare the effectiveness of 5% benzocaine gel and placebo gel on reducing pain caused by fixed orthodontic appliance activation.

SETTING AND SAMPLE POPULATION

Thirty subjects (15-25 years) undergoing fixed orthodontics.

METHODS AND MATERIALS

A randomized, double-blind, placebo-controlled and cross-over clinical trial study was conducted. Subjects were asked to apply a placebo gel and 5% benzocaine gel, exchangeable in two consecutive appointments, twice a day for 3 days and mark their level of pain on a VAS scale. The pain severity was evaluated by means of Mann-Whitney U-test for comparing two gel groups, Kruskal-Wallis nonparametric test for overall differences and post hoc test of Dunnett for paired multiple comparisons. p-value was assigned <0.05.

RESULTS

The overall mean value of pain intensity for benzocaine and placebo gels was 0.89 and 1.15, respectively. The Mann-Whitney U-test indicated that there was no significant difference between overall pain in both groups (mean difference = 0.258 p ˂ 0.21). For both groups, pain intensity was significantly lower at 2, 6 and 24 h compared with pain experienced at days 2, 3 and 7.

CONCLUSION

Benzocaine gel caused a decrease in pain perception at 2 h compared with placebo gel. Peak pain intensity was at 2 h for placebo gel and at 6 h for benzocaine gel, followed by a decline in pain perception from that point to day 7 for both gels.

The safety of non-incineration waste disposal devices in four hospitals of Tehran

BACKGROUND

The safe management of hospital waste is a challenge in many developing countries.

OBJECTIVES

The aim of this study was to compare volatile organic compounds (VOCs) emissions and the microbial disinfectant safety in non-incineration waste disposal devices.

METHODS

VOC emissions and microbial infections were measured in four non-incineration waste disposal devices including: autoclave with and without a shredder, dry heat system, and hydroclave. Using NIOSH and US EPA-TO14 guidelines, the concentration and potential risk of VOCs in emitted gases from four devices were assessed. ProSpore2 biological indicators were used to assess the microbial analysis of waste residue.

RESULTS

There was a significant difference in the type and concentration of VOCs and microbial infection of residues in the four devices. Emissions from the autoclave with a shredder had the highest concentration of benzene, ethyl benzene, xylene, and BTEX, and emissions from the hydroclave had the highest concentration of toluene. The highest level of microbial infection was observed in the residues of the autoclave without a shredder.

CONCLUSIONS

There is an increased need for proper regulation and control of non-incinerator devices and for monitoring and proper handling of these devices in developing countries.

Chemical composition, antioxidant, antimicrobial and cytotoxic activities of Tagetes minuta and Ocimum basilicum essential oils

Chemical composition, antioxidant, antimicrobial and cytotoxic activities of Tagetes minuta (TM) essential oil (TMO) and Ocimum basilicum (OB) essential oil (OBO) were examined. The main components for TMO were dihydrotagetone (33.9%), E-ocimene (19.9%), tagetone (16.1%), cis-β-ocimene (7.9%), Z-ocimene (5.3%), limonene (3.1%) and epoxyocimene (2.03%). The main components for OBO were methylchavicol (46.9%), geranial (19.1%), neral (15.15%), geraniol (3.0%), nerol (3.0%), caryophyllene (2.4%). Inhibitory concentrations (IC₅₀) for reactive oxygen species (ROS) and reactive nitrogen species (RNS) scavenging were 12–17 and 200–250 μg/mL of TMO and OBO, respectively. Minimal inhibitory concentration (MIC) against Salmonella typhi,Escherichia coli,Staphylococcus aureus,Bacillus subtilis,Aspergillus niger, and Candida albicans were 150 ± 8, 165 ± 9, 67 ± 8, 75 ± 7, 135 ± 15, and 115 ± 8 μg/mL of TMO, respectively. MIC for S. typhi,E. coli,S. aureus,B. subtilis,A. niger, and C. albicans were 145 ± 8, 160 ± 7, 45 ± 4, 40 ± 3, 80 ± 9, and 95 ± 7 μg/mL of OBO, respectively. IC₅₀ for nasopharyngeal cancer cell line (KB) and liver hepatocellular carcinoma cell line (HepG2) were 75 ± 5 and 70 ± 4 μg/mL of TMO, respectively. IC₅₀ for KB and HepG2 were 45 ± 4 and 40 ± 3 μg/mL of OBO, respectively. Thus, they could be used as an effective source of natural antioxidant and antibacterial additive to protect foods from oxidative damages and foodborne pathogens. Furthermore, they could be promising candidate for antitumor drug design.

InvasiveVibrio cholerae01 E1 Tor Inaba in a 3-day-old neonate: case report

A 3-day-old girl with invasive V. cholerae infection is described. Her mother had cholera in the perinatal period. Because of retracted nipples, she expressed milk and fed her infant by bottle. The infant died on the 2nd day of admission.

Sentinel node biopsy in endometrial cancer: systematic review and meta-analysis of the literature

PURPOSE

Sentinel lymph node biopsy is a fairly new approach for staging of gynecological malignancies. In the current study, the authors comprehensively reviewed the available reports on sentinel node biopsy of endometrial cancer.

MATERIALS AND METHODS

The authors searched Medline, SCOPUS, ISI web of knowledge, Science Direct, Springer, OVID SP, and Google Scholar with the following search terms: "endometrium OR endometrial OR uterine OR uterus AND sentinel". The outcomes of interest were detection rate and sensitivity.

RESULTS

Overall, 35 studies had enough information for false negative rate evaluation and 51 studies (including the sub-groups of individual studies) for detection rate evaluation (2,071 patients overall). Pooled detection rate was 77.8% (95% CI: 73.5-81.5%) and pooled sensitivity was 89% (95% CI: 83-93%). Cervical injection, as well as using both blue dye and radiotracer, results in higher detection rate and sensitivity. New techniques such as fluorescent dye injection and robotic-assisted surgery showed high detection rate and sensitivity.

CONCLUSION

Sentinel node mapping is feasible in endometrial cancer. Using both blue dye and radiotracer and cervical injection of the mapping material can optimize the sensitivity and detection rate of this technique. Larger studies are still needed to evaluate the false negative rate and the factors influencing the sensitivity before considering this method safe.

PCR-SSCP variation of GH and STAT5A genes and their association with estimated breeding values of growth traits in Baluchi sheep

Growth hormone (GH) selected for its important role in economically relevant traits and signal transducers and activators of transcription 5A (STAT5A) is also known as a main mediator of growth hormone action on target genes. A total number of 190 lambs of Iranian purebred Baluchi sheep were genotyped at exon 5 of GH and exons 7 and 8 of STAT5A genes by using PCR-SSCP analysis. GH gene revealed three (G1, G2, and G3) conformational patterns; however, STAT5A loci were not polymorphic. Breeding values of growth traits including birth weight, weaning weight, 6 months weight, 9 months weight, and yearling weight were estimated by using the Best Linear Unbiased Prediction based on an animal model with a relationship matrix. Studied growth traits were examined for association analysis. Our findings suggest that animals with G2 genotype have highest breeding value for six month weight, while these animals have lowest breeding value for pre-weaning traits. Higher performance of G2 animals in adult ages may be related to the growth hormone role in puberty ages. The other traits showed no relationship to the genotypes examined.

The relationship of plasma leptin concentration and puberty in Holstein bull calves (Bos taurus)

The objective of this experiment was to study the changes of plasma leptin concentration during puberty and its relationship with testosterone level and testis dimensions in Holstein bull calves. Six Iranian Holstein bull calves with approximately 6 months of age were used. Semen evaluation was conducted at 1-month interval to determine the puberty state. To detect the plasma leptin and testosterone changes, blood samples were collected from the jugular vein during pre-puberty (6-7 months of age), puberty (8-9 months of age) and post-puberty (10-11 months of age). In addition, body weight (BW), body condition score (BCS) and testicular width and length were measured at 3-week intervals. The effects of time (age) on total sperm number and percentage of progressive motility of sperm, plasma concentration of leptin and testosterone, amplitude and frequencies of testosterone, BW, BCS, testicular dimensions were significant. Sperm number and progressive motility during post-puberty were higher than those during puberty and pre-puberty. Plasma concentration of leptin during the pre-puberty was higher than those during puberty and post-puberty (p < 0.01). Mean plasma testosterone concentrations during puberty were higher than those during pre-puberty (p < 0.05). BW, BCS and testicular dimensions consistently increased throughout the trial. Results indicated that in growing bull calves, plasma concentrations of leptin decreased during puberty, while circulating testosterone increased.

Sentinel node mapping in the prostate cancer

Aim: Sentinel node (SN) biopsy is becoming a standard procedure in the management of several malignancies. Several groups have evaluated the feasibility and value of this procedure in prostate cancer patients. In the current meta-analysis, we comprehensively and quantitatively summarized the results of these studies. Methods: Several databases including Medline, SCOPUS, Google Scholar, Ovid, Springer, and Science direct were systematically searched for the relevant studies regarding SL biopsy in the prostate cancer (“prostate” AND “sentinel” as search keywords). The outcomes of interest were sensitivity and detection rate of the procedure. Results: For detection rate and sensitivity 21 and 16 studies met the criteria of inclusion respectively. Pooled detection rate was 93.8% (95% CI 89–96.6%). Cochrane Q value was 216.077 (I2 = 89.81% and p < 0.001). Pooled sensitivity was 94% (95% CI 91–96%). Cochrane Q value was 14.12 (I2 = 0.0 and p = 0.516). Conclusion: SL biopsy can prevent unnecessary pelvic lymph node dissection in prostate cancer patients. This procedure is feasible with low false negative rate and high detection rate.

Improvement of Semen Quality in Holstein Bulls during Heat Stress by Dietary Supplementation of Omega-3 Fatty Acids

BACKGROUND

Long-chain polyunsaturated fatty acids (PUFAs) of the omega-3 family are important for sperm membrane integrity, sperm motility and viability. There are evidences to suggest that dietary supplementation with omega-3 fatty acids affects reproduction in men and males of different animal species. Therefore, the aim of current study was to investigate changes in the quality parameters of Holstein bull semen during heat stress and the effect of feeding a source of omega-3 fatty acids during this period.

MATERIALS AND METHODS

Samples were obtained from 19 Holstein bulls during the expected time of heat stress in Iran (June to September 2009). Control group (n=10) were fed a standard concentrate feed while the treatment group (n=9) had this feed top dressed with 100 g of an omega-3 enriched nutriceutical. Semen volume, sperm concentration and total sperm production were evaluated on ejaculates collected after 1, 5, 9 and 12 weeks of supplementation. Moreover, computer-assisted assessment of sperm motility, viability (eosin-nigrosin) and hypo-osmotic swelling test (HOST) were conducted.

RESULTS

Heat stress affected sperm quality parameters by weeks five and nine of the study (p<0.05). Supplementation significantly increased total motility, progressive motility, HOST-positive spermatozoa and average path velocity in the fresh semen of bulls (p<0.05).

CONCLUSION

Dietary omega-3 supplementation improved in vitro quality and motility parameters of fresh semen in Holstein bulls. However, this effect was not evident in frozen-thawed semen.

Effect of feeding a docosahexaenoic acid-enriched nutriceutical on the quality of fresh and frozen-thawed semen in Holstein bulls

The aim of the current study was to investigate the effect of feeding a DHA-enriched nutriceutical on the in vitro quality and sperm motility parameters of fresh and frozen-thawed bull semen assessed by CASA. Samples were obtained from nineteen Holstein bulls used for semen collection at Semen Production Center, Karaj, Iran. Control group (n = 10) were fed a standard concentrate feed while treatment group bulls (n = 9) had this standard feed top dressed with 100 g of a commercially available DHA-enriched nutriceutical. Semen quality was assessed on ejaculates collected at the baseline and after 5, 9, and 12 weeks of supplementation. Classical semen evaluation, assessment of sperm motility (subjective and computer-assisted), viability (eosin-nigrosin), and hypo-osmotic swelling test (HOST) were conducted. Semen volume, sperm concentration, and consequently total sperm output were not affected by dietary treatment (P > 0.05). Feeding the nutriceutical was indeed found to affect sperm motility parameters assessed by CASA after 9 weeks of trial. The treatment has improved total motility (P < 0.01), progressive motility (P < 0.05), average path velocity (P < 0.05), HOST-positive (P < 0.01), and proportion of rapid spermatozoa (P < 0.01) in the fresh semen of bulls. Moreover, the proportion of viable spermatozoa increased (P < 0.05) in the ejaculates collected from nutriceutical-fed bulls compared to the control after 12 weeks of feeding trial. The post-thawed HOST and sperm motility data obtained by CASA did not differ between two groups (P > 0.05). On the other hand, dietary supplementation did not affect body weight, BCS and scrotal circumference. Consequently, it can be concluded that dietary DHA supplementation or its precursors, improve in vitro quality and motility parameters of fresh semen assessed by CASA in Holstein bulls. However, this effect was not pronounced in frozen-thawed semen.

Pregnancy outcome following in vitro fertilization-embryo transfer (IVF-ET) in women aged < 37, undergoing ovulation induction with human FSH compared with recombinant FSH: a randomised controlled study

OBJECTIVE

To compare the pregnancy outcome in patients undergoing in vitro fertilization-embryo transfer (IVF-ET) cycles, using human derived follicle-stimulating hormone (FSH) or recombinant FSH for ovarian stimulation protocols.

DESIGN

Prospective, multi-centre, randomized controlled trial.

PATIENTS

115 infertile patients undergoing a first attempt of in vitro fertilization and embryo transfer were included in the study. The inclusion criteria were: female age < 37 years and use of GnRH agonist (GnRH-a) for pituitary downregulation.

INTERVENTIONS

Long Protocol-controlled ovarian stimulation with human derived FSH or recombinant FSH for IVF-ET.

MAIN OUTCOME MEASURES

Primary endpoints were implantation rate, clinical pregnancy rate and spontaneous abortion rate. Secondary end-points were total units of FSH injected, days of stimulation, peak estradiol levels at point of hCG administration, mean number of oocytes at pick-up, fertilization rate and cleavage rate.

RESULTS

No statistically significantly differences in pregnancy outcomes were found in the patients receiving hFSH in comparison to patients receiving rFSH.

CONCLUSIONS

This study did not demonstrate a difference between the use of h-FSH vs r-FSH for ovarian stimulation in terms of pregnancy outcome, in good prognosis patients undergoing their first IVF-ET procedure.

Use of ethinyl estradiol to reverse the antiestrogenic effects of clomiphene citrate in patients undergoing intrauterine insemination: a comparative, randomized study

OBJECTIVE

To compare the effectiveness of clomiphene citrate used alone and in combination with ethinyl E2 for the induction of ovulation in patients undergoing IUI.

DESIGN

Randomized, double-blind study.

SETTING

Four infertility treatment centers.

PATIENT(S)

Women aged 25-35 years with infertility of at least 2 years' duration and oligomenorrhea or amenorrhea associated with a positive menstrual response to an IM progesterone challenge.

INTERVENTION(S)

A total of 64 patients were randomized to treatment with CC (100 mg daily for 5 days) or CC (100 mg daily for 5 days) plus ethinyl E2 (0.05 mg daily for 5 days).

MAIN OUTCOME MEASURE(S)

The uterine artery pulsatility index, number of preovulatory follicles, endometrial thickness, and pregnancy rate.

RESULT(S)

Both treatment regimens increased FSH, LH, and 17beta-E2 levels, with no statistically significant differences. There was a statistically significant difference in endometrial thickness between the two treatment groups. No statistically significant differences were noted in pulsatility index values or in the number of preovulatory follicles.

CONCLUSION(S)

Ethinyl E2 can reverse the deleterious effects of CC on endometrial thickness, which may contribute to higher pregnancy rates.

Ovarian stimulation using a new highly purified urinary FSH: a prospective randomized clinical study

The aim of this study was to determine the effectiveness of a new highly purified urinary FSH. A total of 60 in vitro-fertilization (IVF) patients, undergoing embryo transfer (ET) for the first time, were randomly allocated into two groups: Group A (n = 30). Subcutaneous administration of urinary follicle-stimulating hormone (FSH, Fostimon 75, A.M.S.A., Italy). Group B (n = 30). Subcutaneous administration of urinary follicle-stimulating hormone (FSH, Metrodin 75 HP, Serono, Italy). Statistical analysis was performed using the chi-square test, p < 0.05 was assumed as significant. This prospective randomized clinical study in an IVF-ET program showed that both drugs were equally safe and effective. Except for the number of the high quality embryos (3.16 vs 2.9; p = 0.03) the two groups did not differ in stimulation parameters or clinical pregnancy rates per attempt and per transfer. On the other hand, a mean number of 3.56 vs 2.18 embryos were cryopreserved in group A and in group B, respectively, as a result of the high number of mature oocytes and high quality embryos. When frozen embryos cycles were included, the difference in pregnancy rate became significant.