A Role for Human Renal Tubular Epithelial Cells in Direct Allo-Recognition by CD4+ T-Cells and the Effect of Ischemia-Reperfusion

Direct allorecognition is the earliest and most potent immune response against a kidney allograft. Currently, it is thought that passenger donor professional antigen-presenting cells (APCs) are responsible. Further, many studies support that graft ischemia-reperfusion injury increases the probability of acute rejection. We evaluated the possible role of primary human proximal renal tubular epithelial cells (RPTECs) in direct allorecognition by CD4+ T-cells and the effect of anoxia-reoxygenation. In cell culture, we detected that RPTECs express all the required molecules for CD4+ T-cell activation (HLA-DR, CD80, and ICAM-1). Anoxia-reoxygenation decreased HLA-DR and CD80 but increased ICAM-1. Following this, RPTECs were co-cultured with alloreactive CD4+ T-cells. In T-cells, zeta chain phosphorylation and c-Myc increased, indicating activation of T-cell receptor and co-stimulation signal transduction pathways, respectively. T-cell proliferation assessed with bromodeoxyuridine assay and with the marker Ki-67 increased. Previous culture of RPTECs under anoxia raised all the above parameters in T-cells. FOXP3 remained unaffected in all cases, signifying that proliferating T-cells were not differentiated towards a regulatory phenotype. Our results support that direct allorecognition may be mediated by RPTECs even in the absence of donor-derived professional APCs. Also, ischemia-reperfusion injury of the graft may enhance the above capacity of RPTECs, increasing the possibility of acute rejection.     

Graphite Oxide and Aromatic Amines: Size Matters

Experimental and theoretical studies are performed in order to illuminate, for first time, the intercalation mechanism of polycyclic aromatic molecules into graphite oxide. Two representative molecules of this family, aniline and naphthalene amine are investigated. After intercalation, aniline molecules prefer to covalently connect to the graphene oxide matrix via chemical grafting, while napthalene amine molecules bind with the graphene oxide surface through π–π interactions. The presence of intercalated aromatic molecules between the graphene oxide layers is demonstrated by X‐ray diffraction, while the type of interaction between graphene oxide and polycyclic organic molecules is elucidated by X‐ray photoelectron spectroscopy. Combined quantum mechanical and molecular mechanical calculations describe the intercalation mechanism and the aniline grafting, rationalizing the experimental data. The present work opens new perspectives for the interaction of various aromatic molecules with graphite oxide and the so‐called “intercalation chemistry”.     

A higher-order unsplit-field perfectly matched layer for the reflectionless truncation of 3-D spherical FDTD lattices

An enhanced higher-order finite-difference time-domain (FDTD) method for the systematic implementation of 3-D reflectionless perfectly matched layers (PML) in spherical coordinates is presented. By establishing a topologically unsplit-field formulation, the novel technique introduces accurate nonstandard schemes that eliminate the notably intricate lattice dispersion errors. Moreover, the wider spatial increments are treated via self-adaptive compact operators, while a mesh expansion process yields a significant reduction of the absorber's depth. For the temporal variable, the proposed method employs a multistage leapfrog integration that guarantees stability and excitation universality. Hence, because of the optimal configuration of the new PML, it attains a critical annihilation of both propagating and spurious wave families, even for complicated domains. Numerical investigation verifies the superiority of the higher-order algorithm via several unbounded radiation and scattering spherical problems.     

Combining feature spaces for classification

In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.     

Quantifying the water–energy nexus in Greece

In this paper we provide an assessment of the water–energy nexus for Greece. More specifically, the amount of freshwater consumed per unit of energy produced is determined: for both conventional (lignite, diesel and fuel oil-fired) and advanced (combined operation of gas turbine) thermal power plants in the electricity generation sector; for extraction and refining activities in the primary energy production sector; and for the production of biodiesel that is used as a blend in the ultimately delivered automotive diesel fuel. In addition, the amount of electricity consumed for the purposes of water supply and sewerage is presented. In view of the expected effects of climate change in the Mediterranean region, the results of this study highlight the need for authorities to prepare a national strategy that will ensure climate resilience in both energy and water sectors of the country.     

The potential of WHR/batch and cullet preheating for energy efficiency in the EU ETS glass industry and the related energy incentives

The European Emissions Trading Scheme (EU ETS) covers approximately 45% of European greenhouse gases (GHGs), 11,000 stationary installations as well as aircraft operators. The EU ETS particularly affects the energy-intensive industries while it imposes a significant risk of “carbon leakage,” i.e., the risk of EU industry departing to countries with weaker restraints on GHG emissions. The EU glass industry, being capital intensive and also requiring long investment cycles, is the world’s largest glass producer with a market share close to one third of global production. Therefore, it is of significant importance to view the position of the EU ETS glass industry in terms of energy conservation possibilities. The present paper utilizes a vertical approach to provide information on both ETS market evolution and specific technical information to support technological innovation to the glass industry. EU ETS glass industry is analyzed regarding the balance between allocated European Union emission allowances (EUAs), verified CO₂ emissions, and potential shortfall in allowances so as to determine the situation of glass industries and the extent of urge for energy-saving activities towards the strengthening of their position within the requirements of the EU ETS phase III. The replication potential of waste heat recovery (WHR) through batch preheating is specifically addressed since it is considered a promising technology according to the latest Best Available Techniques (BAT) reference document for the glass industry under Directive IED 2010/75/EU. A case study for a container glass furnace based on simulation results is presented investigating the impact of different operating and design configurations on specific energy consumption and CO₂ emissions.     

Impact of Functionalized Polystyrenes as the Electron Injection Layer on Gold and Aluminum Surfaces: A Combined Theoretical and Experimental Study

At metal/organic interfaces, the insertion of an organic monolayer can significantly modify the surface properties of the substrate, especially in terms of charge injection across the interface. Herein, we study the formation of an insulating monolayer of morpholine or amine-functionalized polystyrene on Al(111) and Au(111) surfaces and its impact on surface work-function and charge injection. First-principles calculations based on Density Functional Theory have been carried out and point to a significant decrease in the work-function of modified metal surfaces; this is in very good agreement with ultraviolet photoemission spectroscopy measurements performed on the Au(111) surface. In addition, a bilayer cathode, consisting of a thin film of high-work-function metal, such as Al and Au, and a layer of amine-functionalized polystyrene, was also fabricated and tested in organic light-emitting diodes. Such bilayer structures exhibit substantially enhanced efficiency when compared with controls without the functionalized polymers. Our combined theoretical and experimental investigation gives insight into how a thin layer of a commodity polymer can be used to transform rather high-work-function metals into high-performance cathodes to provide efficient electron injection.     

A Bayesian nonparametric study of a dynamic nonlinear model

A Bayesian nonparametric approach to modeling a nonlinear dynamic model is presented. New techniques for sampling infinite mixture models are used. The inference procedure specifically in the case of the logistic model and when the nonparametric component is applied to the additive errors is demonstrated.