Changing the bonding force of impression tray to edentulous maxillary jaw simulator with impression valve system: In vitro study

Objective: The aim of this study was to evaluate the effect of an impression valve system (IVS) on the bonding force between an  impression tray and an edentulous maxillary jaw.Materials and Methods: In this in vitro study, a polyether.coated maxillary jaw simulator (PM) was used to model an edentulous maxillary jaw. The IVS was placed into individual impression trays. An irreversible hydrocolloid impression was taken of the PM when the IVS was open and closed. The impression tray bonding force was measured using a digital dynamometer. Student's t.test was used to determine the significance of the difference between these two groups.Results: The impression tray was more easily separated from the PM when the IVS was open (108 ± 3.9 N). The separation was more difficult when the IVS was closed (153.7 ±14.2 N). The difference between these two findings (P = 0.000) was significant.Conclusion: The use of an IVS facilitates the removal of the impression tray from the mouth when taking impressions from an edentulous maxillary jaw.Key words: Bonding force, impression tray, maxillary ja

A Concept Language Model for Ad-hoc Retrieval

We propose an extension to language models for information retrieval. Typically, language models estimate the probability of a document generating the query, where the query is considered as a set of independent search terms. We extend this approach by considering the concepts implied by both the query and words in the document. The model combines the probability of the document generating the concept embodied by the query, and the traditional language model probability of the document generating the query terms. We use a word embedding space to express concepts. The similarity between two vectors in this space is estimated using a weighted cosine distance. The weighting significantly enhances the discrimination between vectors. We evaluate our model on benchmark datasets (TREC 6–8) and empirically demonstrate it outperforms state-of-the-art baselines

The role of Matriptase-2 during the early postnatal development in humans

Hepcidin is the hepatic peptide hormone, which regulates the systemic iron homeostasis by degrading the only cellular iron exporter ferroportin. The transmembrane serine protease matriptase-2 (MT-2, encoded by TMPRSS6) is a major hepcidin inhibitor and mutations in TMPRSS6 gene are responsible of inherited iron refractory iron deficiency anemia (IRIDA), characterized by hypochromic microcytic anemia, low transferrin saturation and inappropriate normal/high levels of hepcidin. In this study we retrospectively analyzed the hematological parameters in the (neo) perinatal period of IRIDA patients to understand the role of matriptase-2 at birth and during early development. We found that anemia is not present at birth and thus we inferred that it is absent also in utero but develops after the second month of life. Our results might have implications to better understand iron homeostasis during early development in IRIDA patients and indicate that Mt2 is dispensable during fetal and neonatal life in humans, consistent with the idea that the downregulation of hepcidin by Mt2 becomes effective only with the introduction of dietary iron

Macrophage Subset Sensitivity to Endotoxin Tolerisation by Porphyromonas gingivalis

Macrophages (MΦs) determine oral mucosal responses; mediating tolerance to commensal microbes and food whilst maintaining the capacity to activate immune defences to pathogens. MΦ responses are determined by both differentiation and activation stimuli, giving rise to two distinct subsets; pro-inflammatory M1- and anti-inflammatory/regulatory M2- MΦs. M2-like subsets predominate tolerance induction whereas M1 MΦs predominate in inflammatory pathologies, mediating destructive inflammatory mechanisms, such as those in chronic P.gingivalis (PG) periodontal infection. MΦ responses can be suppressed to benefit either the host or the pathogen. Chronic stimulation by bacterial pathogen associated molecular patterns (PAMPs), such as LPS, is well established to induce tolerance. The aim of this study was to investigate the susceptibility of MΦ subsets to suppression by P. gingivalis. CD14hi and CD14lo M1- and M2-like MΦs were generated in vitro from the THP-1 monocyte cell line by differentiation with PMA and vitamin D3, respectively. MΦ subsets were pre-treated with heat-killed PG (HKPG) and PG-LPS prior to stimulation by bacterial PAMPs. Modulation of inflammation was measured by TNFα, IL-1β, IL-6, IL-10 ELISA and NFκB activation by reporter gene assay. HKPG and PG-LPS differentially suppress PAMP-induced TNFα, IL-6 and IL-10 but fail to suppress IL-1β expression in M1 and M2 MΦs. In addition, P.gingivalis suppressed NFκB activation in CD14lo and CD14hi M2 regulatory MΦs and CD14lo M1 MΦs whereas CD14hi M1 pro-inflammatory MΦs were refractory to suppression. In conclusion, P.gingivalis selectively tolerises regulatory M2 MΦs with little effect on pro-inflammatory CD14hi M1 MΦs; differential suppression facilitating immunopathology at the expense of immunity

SEMANTIC LABELING OF STRUCTURAL ELEMENTS IN BUILDINGS BY FUSING RGB AND DEPTH IMAGES IN AN ENCODER-DECODER CNN FRAMEWORK

In the last decade, we have observed an increasing demand for indoor scene modeling in various applications, such as mobility inside buildings, emergency and rescue operations, and maintenance. Automatically distinguishing between structural elements of buildings, such as walls, ceilings, floors, windows, doors etc., and typical objects in buildings, such as chairs, tables and shelves, is particularly important for many reasons, such as 3D building modeling or navigation. This information can be generally retrieved through semantic labeling. In the past few years, convolutional neural networks (CNN) have become the preferred method for semantic labeling. Furthermore, there is ongoing research on fusing RGB and depth images in CNN frameworks. For pixel-level labeling, encoder-decoder CNN frameworks have been shown to be the most effective. In this study, we adopt an encoder-decoder CNN architecture to label structural elements in buildings and investigate the influence of using depth information on the detection of typical objects in buildings. For this purpose, we have introduced an approach to combine depth map with RGB images by changing the color space of the original image to HSV and then substitute the V channel with the depth information (D) and use it utilize it in the CNN architecture. As further variation of this approach, we also transform back the HSD images to RGB color space and use them within the CNN. This approach allows for using a CNN, designed for three-channel image input, and directly comparing our results with RGB-based labeling within the same network. We perform our tests using the Stanford 2D-3D-Semantics Dataset (2D-3D-S), a widely used indoor dataset. Furthermore, we compare our approach with results when using four-channel input created by stacking RGB and depth (RGBD). Our investigation shows that fusing RGB and depth improves results on semantic labeling; particularly, on structural elements of buildings. On the 2D- 3D-S dataset, we achieve up to 92.1 % global accuracy, compared to 90.9 % using RGB only and 93.6 % using RGBD. Moreover, the scores of Intersection over Union metric have improved using depth, which shows that it gives better labeling results at the boundaries

Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat 3 wild emmer wheat RIL population

Mineral nutrient malnutrition, and particularly deficiency in zinc and iron, afflicts over 3 billion people worldwide. Wild emmer wheat, Triticum turgidum ssp. dicoccoides, genepool harbors a rich allelic repertoire for mineral nutrients in the grain. The genetic and physiological basis of grain protein, micronutrients (zinc, iron, copper and manganese) and macronutrients (calcium, magnesium, potassium, phosphorus and sulfur) concentration was studied in tetraploid wheat population of 152 recombinant inbred lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Wide genetic variation was found among the RILs for all grain minerals, with considerable transgressive effect. A total of 82 QTLs were mapped for 10 minerals with LOD score range of 3.2–16.7. Most QTLs were in favor of the wild allele (50 QTLs). Fourteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. Significant positive correlation was found between grain protein concentration (GPC), Zn, Fe and Cu, which was supported by significant overlap between the respective QTLs, suggesting common physiological and/or genetic factors controlling the concentrations of these mineral nutrients. Few genomic regions (chromosomes 2A, 5A, 6B and 7A) were found to harbor clusters of QTLs for GPC and other nutrients. These identified QTLs may facilitate the use of wild alleles for improving grain nutritional quality of elite wheat cultivars, especially in terms of protein, Zn and Fe