Interaction between Fish Skin Gelatin and Pea Protein at Air-Water Interface after Ultrasound Treatment

The interaction between fish skin gelatin (FG) and pea protein isolate (PPI) was investigated at the air-water interface (A-W) before and after a high intensity (275 W, 5 min) ultrasound treatment (US). We analyzed the properties of the single protein suspensions as well as an equal ratio of FG:PPI (MIX), in terms of ζ-potential, particle size, molecular weight, bulk viscosity and interfacial tension. The foaming properties were then evaluated by visual analysis and by Turbiscan Tower. Confocal laser scanning microscopy (CLSM) was employed to explore the role of the proteins on the microstructure of foams. The results showed that the ultrasound treatment slightly influenced physicochemical properties of the proteins, while in general, did not significantly affect their behavior both in bulk and at the air-water interface. In particular, PPI aggregate size was reduced (−48 nm) while their negative charges were increased (−1 mV) after the treatment. However, when the proteins were combined, higher molecular weight of aggregates, higher foam stability values (+14%) and lower interfacial tension (IFT) values (47.2 ± 0.2 mN/m) were obtained, leading us to assume that a weak interaction was developed between them

Largest ancient fortress of South-West Asia and the western world?:Recent fieldwork at Sasanian Qaleh Iraj at Pishva, Iran

The article presents recent works at Qale Iraj, near Varamin, Iran. My short contribution is on the Middle Persian ostraka found at the site

Pre-agricultural plant management in the uplands of the central Zagros: the archaeobotanical evidence from Sheikh-e Abad

Prior to the emergence of agriculture in southwest Asia, sedentarising human communities were experimenting with a diverse range of wild plant species over a prolonged period. In some cases, this involved the cultivation of species that would go on to be domesticated and form the foundation of future agricultural economies. However, many forms of plant use did not follow this trajectory, and in multiple places farming was only taken up later as an established ‘package’. In this paper, we present new archaeobotanical evidence from the Early Neolithic site of Sheikh-e Abad in the central Zagros of western Iran. Sheikh-e Abad is unique in being the only settlement known to date within southwest Asia that lies at an altitude above 1000m and which has occupation spanning the agricultural transition. Thus, it provides a rare opportunity to examine pre-agricultural plant management strategies in an upland zone. Our analyses of the plant remains from Sheikh-e Abad suggest that from its earliest occupation inhabitants were unconsciously ‘auditioning’ a suite of locally available wild grasses which ultimately were never domesticated. We discuss the possible reasons for this from a socio-ecological perspective, considering both the biology and ecology of the plant species in question, as well as the ways in which they were potentially managed

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

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 PM2.5-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

Using the Boruta algorithm and deep learning models for mapping land susceptibility to atmospheric dust emissions in Iran

Wind erosion have many negative effects on global terrestrial and aquatic ecosystems and these phenomena are controlled by several factors including climatic, meteorological, topographic, vegetation, surface and soil characteristics. This study applied, for the first time, the Boruta algorithm for identification of effective variables controlling wind erosion. The novelty of the study was increased further using application of two deep learning (DL) models comprising a simple recurrent neural network (RNN) and restricted boltzmann machine (RBM). Collectively, these tools were used to map land susceptibility to wind erosion in parts of Kerman province, southeastern Iran. Among 18 potential variables for controlling dust emissions via wind erosion, 4 and 14 were identified as non-important and important, respectively, by the Boruta algorithm, while three (precipitation, digital elevation model and soil organic carbon) were selected as the most important factors. An inventory map of the wind erosion confirmed using both a test dataset (30%) and a training dataset (70%) was used to construct predictive models of land susceptibility to wind erosion. Both DL predictive models exhibited highly satisfactory performance according to a Taylor diagram, but the simple RNN performed slightly better than RBM. Based on the simple RNN, 35.6%, 5%, 2.4%, 22.7% and 34.3% of the total study area were characterized by very low, low, moderate, high and very high susceptibility, respectively. Convergent prediction of the same susceptibility classes by intersecting the maps generated by both models classified 17.4%, 0.07%, 0.06%, 7.4% and 34% of the total study area as very low, low, moderate, high and very high susceptibility classes, respectively. We conclude that applying the Boruta algorithm and DL models as new methods in aeolian geomorphology, may provide accurate spatial maps of dust sources to help target mitigation of detrimental dust effects on climate, ecosystems and human health

Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory

This study undertook a comprehensive application of 15 data mining (DM) models, most of which have, thus far, not been commonly used in environmental sciences, to predict land susceptibility to water erosion hazard in the Kahorestan catchment, southern Iran. The DM models were BGLM, BGAM, Cforest, CITree, GAMS, LRSS, NCPQR, PLS, PLSGLM, QR, RLM, SGB, SVM, BCART and BTR. We identified 18 factors usually considered as key controls for water erosion, comprising 10 factors extracted from a digital elevation model (DEM), three indices extracted from Landsat 8 images, a sediment connectivity index (SCI) and three other intrinsic factors. Three indicators consisting of MAE, MBE, RMSE, and a Taylor diagram were applied to assess model performance and accuracy. Game theory was applied to assess the interpretability of the DM models for predicting water erosion hazard. Among the 15 predictive models, BGAM and PLS respectively returned the best and worst performance in predicting water erosion hazard in the study area. The most accurate model, BGAM predicted that 22%, 8.2%, 9.4% and 60.4% of the total area should be classified as low, moderate, high and very high susceptibility to soil erosion by water, respectively. Based on BGAM and game theory, the factors extracted from the DEM (e.g., DEM, TWI, Slope, TST, TRI, and SPI) were considered the most important ones controlling the predicted severity of soil erosion by water. We conclude that overall, game theory is a valuable technique for assessing the interpretability of predictive models because this theory through SHAP (Shapley additive explanations) and PFIM (permutation feature importance measure) addresses the important concerns regarding the interpretability of more complex DM models

Phytochemical constituents, antioxidant activity and toxicity potential of Phlomis olivieri Benth.

Background and objectives: Phlomis olivieri Benth. (Lamiaceae) is a medicinal plant widely distributed in Iran. In the present study, we have investigated the phytochemical constituents, antioxidant activity and general toxicity potential of the aerial parts of this species. Methods: Silica gel (normal and reversed phases) and Sephadex LH-20 column chromatographies were used for isolation of compounds from methanol-soluble portion (MSP) of the total extract obtained from P. olivieri aerial parts. The structures of isolated compounds were elucidated using 1H-NMR, 13C-NMR and UV spectral analyses. Antioxidant activity and general toxicity potential of MSP were also evaluated in DPPH free radical-scavenging assay and brine shrimp lethality test (BSLT), respectively. Results: One caffeoylquinic acid derivative, chlorogenic acid (1), one iridoid glycoside, ipolamiide (2), two phenylethanoid glycosides, phlinoside C (3) and verbascoside (5), along with two flavonoids, isoquercetin (4) and naringenin (6) were isolated and identified from MSP. The MSP exhibited considerable antioxidant activity in DPPH method (IC50; 50.4 ± 4.6 µg/mL), compared to BHT (IC50; 18.7 ± 2.1 µg/mL), without any toxic effect in BSLT at the highest tested dose (1000 µg/mL). Conclusion: the results of the present study introduce P. olivieri as a medicinal plant with valuable biological and pharmacological potentials

Phytochemical constituents, antioxidant activity and toxicity potential of Phlomis olivieri Benth

Abstract Background and objectives: Phlomis olivieri Benth. (Lamiaceae) is a medicinal plant widely distributed in Iran. In the present study, we have investigated the phytochemical constituents, antioxidant activity and general toxicity potential of the aerial parts of this species. Methods: Silica gel (normal and reversed phases) and Sephadex LH-20 column chromatographies were used for isolation of compounds from methanol-soluble portion (MSP) of the total extract obtained from P. olivieri aerial parts. The structures of isolated compounds were elucidated usin