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.     

The Gestational Effects of Maternal Bone Marker Molecules on Fetal Growth,Metabolism and Long-Term Metabolic Health: A Systematic Review

Fetal exposure in adverse environmental factors during intrauterine life can lead to various biological adjustments, affecting not only in utero development of the conceptus, but also its later metabolic and endocrine wellbeing. During human gestation, maternal bone turnover increases, as reflected by molecules involved in bone metabolism, such as vitamin D, osteocalcin, sclerostin, sRANKL, and osteoprotegerin; however, recent studies support their emerging role in endocrine functions and glucose homeostasis regulation. Herein, we sought to systematically review current knowledge on the effects of aforementioned maternal bone biomarkers during pregnancy on fetal intrauterine growth and metabolism, neonatal anthropometric measures at birth, as well as on future endocrine and metabolic wellbeing of the offspring. A growing body of literature converges on the view that maternal bone turnover is likely implicated in fetal growth, and at least to some extent, in neonatal and childhood body composition and metabolic wellbeing. Maternal sclerostin and sRANKL are positively linked with fetal abdominal circumference and subcutaneous fat deposition, contributing to greater birthweights. Vitamin D deficiency correlates with lower birthweights, while research is still needed on intrauterine fetal metabolism, as well as on vitamin D dosing supplementation during pregnancy, to diminish the risks of low birthweight or SGA neonates in high-risk populations.     

A Systematic Review and Meta-Analysis of Pharmacogenetic Studies in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is an important global public health problem due to its high prevalence and morbidity. Although the treatment of nephrology patients has changed considerably, ineffectiveness and side effects of medications represent a major issue. In an effort to elucidate the contribution of genetic variants located in several genes in the response to treatment of patients with CKD, we performed a systematic review and meta-analysis of all available pharmacogenetics studies. The association between genotype distribution and response to medication was examined using the dominant, recessive, and additive inheritance models. Subgroup analysis based on ethnicity was also performed. In total, 29 studies were included in the meta-analysis, which examined the association of 11 genes (16 polymorphisms) with the response to treatment regarding CKD. Among the 29 studies, 18 studies included patients with renal transplantation, 8 involved patients with nephrotic syndrome, and 3 studies included patients with lupus nephritis. The present meta-analysis provides strong evidence for the contribution of variants harbored in the ABCB1, IL-10, ITPA, MIF, and TNF genes that creates some genetic predisposition that reduces effectiveness or is associated with adverse events of medications used in CKD.     

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.     

A spectro-microscopic investigation of Fe-Co bimetallic catalysts supported on MgO for the production of thin carbon nanotubes

Fe-Co bimetallic catalysts supported on MgO were studied for the catalytic chemical vapor deposition growth of carbon nanotubes (CNTs). Different wt.% metal loadings were investigated at various deposition temperatures and times. Characterization of the products involved thermal analysis (DTA-TGA), X-ray diffraction, spectroscopy (Raman, UPS, EELS and STS) and microscopy (SEM, TEM and STM) techniques. It was found that the metal content is critical, not only to the yield and the structural quality of the synthesized carbon nanotubes, but it can be also used to tune the desired type of synthesized nanotubes. Lower (2 wt.%) loadings of Fe-Co catalysts favor the formation of single- and/or double-wall CNTs for deposition time and temperature 30 min and 800 °C, respectively. Thermal analysis and Raman measurements showed that these thin CNTs were synthesized at high amounts (CNT-per-catalyst wt.% of more than 100%), exhibiting high graphitization degree with only traces of by-products (mainly amorphous carbon) among them. Microscopy results revealed the formation of CNTs bundles, consisting of individual nanotubes with less than 2 nm outer diameter, while additional energy loss measurements pointed out that the deposited CNTs are mainly single wall. Higher (10 wt.%) Fe-Co loadings resulted to the formation of multi-wall CNTs.     

Cyclooxygenase Inhibition Alters Proliferative,Migratory, and Invasive Properties of Human Glioblastoma Cells In Vitro

Prostaglandin E₂ (PGE₂) is known to increase glioblastoma (GBM) cell proliferation and migration while cyclooxygenase (COX) inhibition decreases proliferation and migration. The present study investigated the effects of COX inhibitors and PGE₂ receptor antagonists on GBM cell biology. Cells were grown with inhibitors and dose response, viable cell counting, flow cytometry, cell migration, gene expression, Western blotting, and gelatin zymography studies were performed. The stimulatory effects of PGE₂ and the inhibitory effects of ibuprofen (IBP) were confirmed in GBM cells. The EP2 and EP4 receptors were identified as important mediators of the actions of PGE₂ in GBM cells. The concomitant inhibition of EP2 and EP4 caused a significant decrease in cell migration which was not reverted by exogenous PGE₂. In T98G cells exogenous PGE₂ increased latent MMP2 gelatinolytic activity. The inhibition of COX1 or COX2 caused significant alterations in MMP2 expression and gelatinolytic activity in GBM cells. These findings provide further evidence for the importance of PGE₂ signalling through the EP2 and the EP4 receptor in the control of GBM cell biology. They also support the hypothesis that a relationship exists between COX1 and MMP2 in GBM cells which merits further investigation as a novel therapeutic target for drug development.     

Vegetable waste treatment: comparison and critical presentation of methodologies

Vegetable industries have been considered responsible for a great amount of pollution; hence, there has been a strong need for the optimization of vegetable waste treatment systems. The currently employed systems are numerous and fall in the following large categories; thermal processes, evaporation, membrance processes, anaerobic digestion, anaerobic co-digestion, biodiesel spraying, combustion, transesterification, coagulation, and composting. Respective methodologies in conjunction with waste treatment methods were presented per waste treatment method. The comparative presentation of the various vegetable waste treatment methodologies showed that though anaerobic digestion stands for the most enviromentally friendly technique, its required longer treatment time in conjuction with its weakness to deal with elemental contaminants makes imperative the employment of a second alternative technique which could either be a membrance process (low energy cost, reliability, reduced capital cost) or a coagulation/flocculation method because of its low cost and high effectiveness. Biogas production appears to be another promising and energy effective waste treatment method. On the other hand, methods like distillation and ozonation (high cost) and electrolysis (experimental level) have not been employed in the field. Finally, new waste management technologies have been described.     

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.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.