Cell stress-regulated human major histocompatibility complex class I gene expressed in gastrointestinal epithelium

Conventional major histocompatibility complex (MHC) class I genes encode molecules that present intracellular peptide antigens to T cells. They are ubiquitously expressed and regulated by interferon gamma. Two highly divergent human MHC class I genes, MICA and MICB, are regulated by promoter heat shock elements similar to those of HSP70 genes. MICA encodes a cell surface glycoprotein, which is not associated with beta 2-microglobulin, is conformationally stable independent of conventional class I peptide ligands, and almost exclusively expressed in gastrointestinal epithelium. Thus, this MHC class I molecule may function as an indicator of cell stress and may be recognized by a subset of gut mucosal T cells in an unusual interaction.     

Theory of contact for geometric continuity of parametric curves

This paper develops a theory of contact for piecewise parametric curves based on the differential geometry of evolutes, polar curves, and binormal indicatrices. This theory is completely geometric, independent of parametrization and generalizes to any order. Two sets of dimensionless, characteristic numbers describing the local geometry of a curve up tonth order are defined. These characteristic numbers can be used to describe conditions for higher order contacts in an algebraic fashion. The same characteristic numbers can also be used to interpret contact conditions of up tonth order in terms of the geometry of higher evolutes and binormal indicatrices. The resulting geometric contact conditions are used to design piecewise parametric curves for Computer Aided Geometric Design (CAGD) applications.     

Laminated Perovskite Photovoltaics: Enabling Novel Layer Combinations and Device Architectures

High‐efficiency perovskite‐based solar cells can be fabricated via either solution‐processing or vacuum‐based thin‐film deposition. However, both approaches limit the choice of materials and the accessible device architectures, due to solvent incompatibilities or possible layer damage by vacuum techniques. To overcome these limitations, the lamination of two independently processed half‐stacks of the perovskite solar cell is presented in this work. By laminating the two half‐stacks at an elevated temperature (≈90 °C) and pressure (≈50 MPa), the polycrystalline perovskite thin‐film recrystallizes and the perovskite/charge transport layer (CTL) interface forms an intimate electrical contact. The laminated perovskite solar cells with tin oxide and nickel oxide as CTLs exhibit power conversion efficiencies of up to 14.6%. Moreover, they demonstrate long‐term and high‐temperature stability at temperatures of up to 80 °C. This freedom of design is expected to access both novel device architectures and pairs of CTLs that remain usually inaccessible.     

The application of discriminant analysis for mapping cereals and pasture using object-based features

High mapping accuracies occur where crops differ spectrally (e.g.>90.0%; canola, corn, soybeans) and vice versa (e.g. <75.0%; cereals and pasture). Developing improved mapping methods has been an ongoing priority of Agriculture and Agri-Food Canada (AAFC) remote-sensing science. To this end, this study tests a data-driven object-based classification method using Discriminant Analysis (DA) method for mapping cereals and pasture from satellite data. In this approach, variables (number >400) derived from the image segmentation and object-based feature extraction of multi-date and multi-band optical (RapidEye) and microwave (RADARSAT-2) imagery were applied in a data-driven approach. We use in situ and satellite information collected over two study sites with different levels of heterogeneity (Winnipeg, Brandon) situated in the Canadian Prairies during the 2013 growing season to assess: (a) the type of DA model that most accurately classifies the cereals and pasture cover classes; and (b) how the classification accuracies obtained by the application of this DA model compare to those obtained from more traditional Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifications. We found that our DA-based approach was able to map cereals and pastures at our two study sites with the highest accuracies, but these accuracies did not improve significantly with the use of more complex DA model (including priori classification probabilities, more input principle components (PCs), the use of weights proportional to field area). Our results are encouraging for the wider application of the data-driven pre-processing of the inputs to the image classification by DA.     

Design and analysis of a novel MEMS spiral inductor with high quality factor

Microsystem Technologies - In this paper a novel spiral inductor with high quality (Q)-factor is presented. Non-uniform current density distribution, especially in inner turn, increases the...     

Design and simulation of a novel RF MEMS shunt capacitive switch with low actuation voltage and high isolation

Microsystem Technologies - This paper presents a new RF MEMS capacitive shunt switch with low voltage, low loss and high isolation for K-band applications. In this design, we have proposed the step...     

Evaluation of the Bankruptcy Approach for Water Resources Allocation Conflict Resolution at Basin Scale,Iran’s Lake Urmia Experience

Water conflicts appear when there are insufficient and less available water resources than water demands claimed by different agents. In this study, a new bankruptcy approach is investigated to resolve water conflicts in the Zarrinehroud River Basin, the largest and most important sub-basin of Lake Urmia’s Basin in the northwest of Iran. The new bankruptcy method is compared with the proportional rule (PRO) and another alternative based on the cessation of irrigated agriculture in the region proposed to supply and save environmentally in danger Lake Urmia. Four scenarios consisting of the current situation, optimistic, average and pessimistic scenarios regarding the future of water resources of the basin and agricultural developments were considered. According to the results, both bankruptcy rules helped Lake Urmia to receive more water, but neither could utterly overcome the water shortage of the Lake, so can be used as supplementary actions alongside other solutions. The cessation of irrigated agriculture throughout the basin overcame the average annual shortage of Lake Urmia in the first and second scenarios equal to 137 and 148 million cubic meters respectively. It showed disability to fully supply the lake in the third and fourth scenarios. These three methods must be combined with a social-economic policy like the purchase of decreased water allocations to farmers to be socially acceptable.     

Spatiotemporal Modelling of Water Balance Components in Response to Climate and Landuse Changes in a Heterogeneous Mountainous Catchment

Landuse change and climate change are the main drivers of hydrological processes. The purpose of this study was to analyse the separate and combined future effects of climate and landuse changes on water balance components on different spatial and temporal scales using the integrated hydrological Soil and Water Assessment Tool model. The study focused on the changes and relationship between water yield (WYLD) and sediment yield (SYLD) in the heterogeneous Taleghan Catchment in Iran. For future climate scenarios, RCP 4.5 and RCP 8.5 of GFDL-ESM2M GCM were used for 2020–2040. A Markov chain model was used to predict landuse change in the catchment. The results indicated an increase in precipitation and evapotranspiration. The findings also showed that the relationship between WYLD and SYLD is direct and synergic. Climate change has a stronger effect on WYLD than landuse change, whereas landuse change has a stronger effect on SYLD. The conversion of rangelands to barren land is the most critical landuse change that could increase SYLD. The highest increase in WYLD and SYLD in scenario RCP4.5 resulted from the combined effects of climate and landuse change. We estimated WYLD of about 295 mm and SYLD of around 17 t/ha. The proposed methodology is universal and can be applied to similar settings to identify the most vulnerable regions. This can help prioritize management strategies to improve water and soil management in watersheds.

     

Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction

Water Resources Management - Due to the rapidly increasing demand for groundwater, as one of the principal freshwater resources, there is an urge to advance novel prediction systems to more...