Immunomodulatory effects of human umbilical cord wharton's Jelly-Derived mesenchymal stem cells on differentiation, maturation and endocytosis of monocyte-derived dendritic cells

The Wharton's jelly of the umbilical cord is believed to be a source of mesenchymal stem cells (MSCs) which can be therapeutically applied in degenerative diseases. In this study, we investigated the immunomodulatory effect of umbilical cord derivedmesenchymal stem cells (UC-MSCs) and bone marrow-derived-mesenchymal stem cells (BM-MSCs) on differentiation, maturation, and endocytosis of monocyte-derived dendritic cells in a transwell culture system under laboratory conditions. Monocytes were differentiated into immature dendritic cells (iDCs) in the presence of GM-CSF and IL-4 for 6 days and then differentiated into mature dendritic cells (mDCs) in the presence of TNF-for 2 days. In every stage of differentiation, immature and mature dendritic cells were separately cocultured with UC-MSCs and BM-MSCs. The findings showed that UC-MSCs and BM-MSCs inhibited strongly differentiation and maturation of dendritic cells at higher dilution ratios (1:1). The BM-MSCs and UC-MSCs showed more inhibitory effect on CD1a, CD83, CD86 expression, and dendritic cell endocytic activity, respectively. On the other hand, these cells severely up-regulated CD14 marker expression. We concluded that UC-MSCs and BM-MSCs could inhibit differentiation, maturation and endocytosis in monocyte-derived DCs through the secreted factors and free of any cellcell contacts under laboratory conditions. As DCs are believed to be the main antigen presenting cells for naive T cells in triggering immune responses, it would be logical that their inhibitory effect on differentiation, maturation and function can decrease or modulate immune and inflammatory responses. Copyright © Spring 2013, Iran J Allergy Asthma Immunol. All rights reserved

Entrapment of two-spotted spider mite, Tetranychus urticae (Acari: Prostigmata: Tetranychidae), by type IV glandular trichomes of Lycopersicon species

Two-spotted spider mite (TSSM) is becoming resistant to many of the pesticides used in the tomatofields. A potential alternative method of control is host plant resistance, which may be mediated byglandular trichomes. In this experiment, level of entrapment and mortality of the pest by type IVglandular trichomes were studied on accessions of Lycopersicon hirsutum Humb & Bonpl, L. pennellii(Cor.) DÂArcy and L. esculentum Mill on leaflets with trichomes intact or with exudates removed. Threeaccessions of L. hirsutum (LA1740, LA1777 and LA2860), two accessions of L. pennellii (LA2963 andLA2580) and one susceptible variety (Sankranthi) of L. esculentum were used. Leaflets from the terminalpair of the third-last fully expanded leaves were excised and leaf disks prepared. Ten female mites ofTetranychus orticae Koch were placed on the adaxial leaflet surface of each disk and after 2 h, thenumber of mites trapped, untrapped and moved into water were recorded. Again after 24 and 48 h, miteswere recorded either dead, morbid or moved into water. Results indicated that the highest number ofentrapment was on L. hirsutum LA1777, followed by L. pennellii accessions. None of the mites wasentrapped on L. esculentum. Moreover, L. hirsutum (LA1777 and LA1740) and L. pennellii accessionshad greater number of dead mites than L. esculentum after 48 h. Lycopersicon esculentum did not possesstype IV glandular trichomes. The highest density of type IV glandular trichomes was recorded on abaxialand adaxial leaf surfaces of L. hirsutum LA1777 (86.72 and 34.44 trichomes/mm², respectively). Removalof trichome exudates has significantly increased the mite survival in resistant accessions. Correlationbetween the density of type IV glandular trichomes and entrapment rate of TSSM was significantlypositive

Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

© 2019 SPIE. Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early prediction of landslide prone areas is vital. Variety of causative factors such as glaciers melting, excessive raining, mining, volcanic activities, active faults, earthquake, logging, erosion, urbanization, construction, and other human activities can trigger landslide occurrence. Then, identification of factors that directly influences the slide events is highly in demand. Some topographical, geological, and hydrological datasets (e.g., slope, aspect, geology, terrain roughness, vegetation index, distance to stream, distance to road, distance to fault, land use, precipitation, profile curvature, plan curvature) are considered to be effective conditioning factors. However, the importance of each factor differs from one study to another. This study investigates the effectiveness of four sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into four groups G1, G2, G3, and G4. Three machine learning algorithms namely, Random Forest (RF), Naive Bayes (NB), and Boosted Logistic Regression (LogitBoost) were constructed based on each dataset in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 227 landslide inventory datasets of the study area were used where 70% was used for training and 30% for testing. To this end, in the present research, the two main objectives were: 1) Investigation on effectiveness of 14 landslides conditioning factors (altitude, slope, aspect, total curvature, profile curvature, plan curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), distance to fault, distance to road, distance to stream, land use, and geology) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson's correlation and Chi-square techniques. Consequently, 4 categories of datasets were defined; first dataset included all 14 conditioning factors, second dataset included Digital Elevation Models (DEM) derivatives (morphometrice factors), third dataset was only based on 5 factors namely lithology, land use, distance to stream, distance to road, and distance to fault, and last dataset was included 8 factors selected using factor analysis and optimization. 2) Evaluate the sensitivity of each modeling technique (NB, RF and LogitBoost) to different conditioning factors using the area under curve (AUC). Eventually, RF technique using optimized variables (G4) performed well with AUC of 0.940 followed by LogitBoost (0.898) and NB (0.864)

EBSD Study of Damage Mechanisms in a High-Strength Ferrite-Martensite Dual-Phase Steel

Electron backscattered diffraction (EBSD) analyses were performed on a fine-grained dual-phase (DP) sheet steel subjected to uniform tensile deformation and the preferred void nucleation sites as well as the micro-mechanisms of void formation were examined. EBSD study of grain average misorientation, grain orientation spread and kernel average misorientation of the deformed microstructure revealed that voids nucleation initially happened at ferrite-martensite interfaces neighboring rather large ferrite grains. This is believed to be mainly due to the higher shear deformation ability of the larger ferrite grains, the higher number of dislocation pile-ups at the martensite particles and the less uniform strain distribution within the larger ferrite grains compared to the smaller ones. The results demonstrated the impact of increasing uniform strain distribution within the DP microstructure on lowering the void nucleation probability.open111112sciescopu

Resistance of 14 accessions/cultivars of Lycopersicon spp. to two-spotted spider mite, Tetranychus urticae (Acari: Tetranychidae), in laboratory and greenhouse

Fourteen accessions and cultivars of Lycopersicon spp. were studied to evaluate their resistance to two-spotted spider mite, Tetranychus urticae Koch, under laboratory and greenhouse conditions. In vitro studies were carried out using thumbtack and leaf disk bioassays. Lycopersicon hirsutum and L. pennellii accessions supported more mites on the tack. The highest number of eggs (5.15 ± 0.48 eggs / â / d) was recorded on L. pimpinellifolium LA2533 and the lowest number (0 egg / â / d) was recorded on L. hirsutum and L. pennellii accessions. The highest mite mortality and lowest damage score occurred on the leaf disks of L. pennellii and L. hirsutum accessions that were strikingly in contrast to our observations on L. esculentum varieties (Nandi and Sankranthi). The developmental time of the mite was longest (8.61 ± 0.18 days) on leaf disks of L. esculentum NDTVR-73 and shortest (7.18 ± 0.51 days) on L. pimpinellifolium LA2533. In the greenhouse, the mite was unable to establish on L. hirsutum and L. pennellii accessions. Amongst the rest, Nandi and Sankranthi accessions/cultivars supported the highest, while NDTVR-73 supported the lowest mite population. The highest and lowest density of type VI glandular trichomes were recorded on L. esculentum NDTVR-73 (67.33 ± 6.34 trichomes/mm²) and L. pennellii LA2963 (0.79 ± 0.15 trichomes/mm²), respectively. No relation was found between density of type VI trichomes and resistance to the mite. Type IV glandular trichomes were observed only on the foliages of L. hirsutum and L. pennellii. A positive relation exists between the density of type IV glandular trichomes and resistance to the mite

A comparison between three conditioning factors dataset for landslide prediction in the sajadrood catchment of iran

This study investigates the effectiveness of three datasets for the prediction of landslides in the Sajadrood catchment (Babol County, Mazandaran Province, Iran). The three datasets (D1, D2 and D3) are constructed based on fourteen conditioning factors (CFs) obtained from Digital Elevation Model (DEM) derivatives, topography maps, land use maps and geological maps. Precisely, D1 consists of all 14 CFs namely altitude, slope, aspect, topographic wetness index (TWI), terrain roughness index (TRI), distance to fault, distance to stream, distance to road, total curvature, profile curvatures, plan curvature, land use, steam power index (SPI) and geology. D2, on the other hand, is a subset of D1, consisting of eight CFs. This reduction was achieved by exploiting the Variance Inflation Factor, Gini Importance Indices and Chi-Square factor optimization methods. Dataset D3 includes only selected factors derived from the DEM. Three supervised classification algorithms were trained for landslide prediction namely the Support Vector Machine (SVM), Logistic Regression (LR), and Artificial Neural Network (ANN). Experimental results indicate that D2 performed the best for landslide prediction with the SVM producing the best overall accuracy at 82.81%, followed by LR (81.71%) and ANN (80.18%). Extensive investigations on the results of factor optimization analysis indicate that the CFs distance to road, altitude, and geology were significant contributors to the prediction results. Land use map, slope, total-, plan-, and profile curvature and TRI, on the other hand, were deemed redundant. The analysis also revealed that sole reliance on Gini Indices could lead to inefficient optimization

A modified empirical criterion for strength of transversely anisotropic rocks with metamorphic origin

A modified empirical criterion is proposed to determine the strength of transversely anisotropic rocks. In this regard, mechanical properties of intact anisotropic slate obtained from three different districts of Iran were taken into consideration. Afterward, triaxial rock strength criterion introduced by Rafiai was modified for transversely anisotropic rocks. The criterion was modified by adding a new parameter α for taking the influence of strength anisotropy into consideration. The results obtained have shown that the parameter α can be considered as the strength reduction parameter due to rock anisotropy. The modified criterion was compared to the modified Hoek–Brown (Saroglou and Tsiambaos) and Ramamurthy criteria for different anisotropic rocks. It was concluded that the criterion proposed in this paper is a more accurate and precise criterion in predicting the strength of anisotropic rocks