GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification

In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolution framework. We investigate the effectiveness of GEML on several supervised, semi-supervised and unsupervised multi-class problems and demonstrate its competitive performance when compared with several well known machine learning algorithms. The GEML framework evolves human readable solutions which provide an explanation of the logic behind its classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. In addition we also examine the possibility of improving the performance of the algorithm through the application of several ensemble techniques

Cyclic in-plane shear behaviour of unreinforced masonry panels retrofitted with fibre reinforced polymer strips

An experimental study was conducted to assess the effect on strength and ductility of retrofitting unreinforced masonry (URM) shear panels with near surface mounted (NSM) fibre reinforced polymer (FRP) strips. A total of six wall panels, 1200mm x 1200mm, were subjected to vertical precompression combined with increasing reversing cycles of in-plane lateral displacement. The panels were tested first as URM, retrofitted with NSM FRP strips and then retested. The URM testing was conducted as part of a previous research study to investigate the effects of a damp proof course (DPC) layer incorporated near the base of the panels. The six panels failed in compression, with crushing at the corners and near-vertical cracking throughout the panels in the URM tests. For the current study, the panels were retrofitted using three different arrangements of FRP strip reinforcement and then retested using the same apparatus but with base sliding at the DPC level prevented. The tests were used to determine the effect on strength, displacement capacity and ductility achieved by FRP retrofitting the damaged URM panels compared to the original undamaged panels. The broader aim of the research is to identify techniques for improving the seismic performance of existing URM walls under in-plane shear loading

The effect of interieukin-17 on hematopoietic cells and cytokine release in mouse spleen

To evaluate whether the response of hematopoietic cells to interleukin-17 (IL-17) depends on the tissue microenvironment in which hematopoiesis occurs, the influence of recombinant mouse IL-17 on spleen hematopoietic cells and cytokine release was assessed in normal mice in vitro and in vivo. In vitro, IL-17 did not significantly affect the growth of granulocyte-macrophage (CFU-GM) and erythroid (BFU-E and CFU-E) derived colonies. A single injection of IL-17 in vivo exhibited stimulatory effects on hematopoietic cells from both granulocytic and erythroid lineages. The increased number of metamyelocytes 48 h after treatment imply to the IL-17-induced stimulation of granulopoiesis. The number of BFU-E was increased at 24 h, while the number of CFU-E increased 6 h and 24 h after treatment. Since the same treatment in the bone marrow decreased the number of CFU-E, it may be concluded that the local microenvironment plays an important role in IL-17-mediated effects on CFU-E. IL-17 increased the release of IL-6 both in vitro and in vivo, but showed tendency to suppress the constitutive secretion of IL-10 by spleen cells. Our results suggest the complexity of target cell response and interplay of secondary induced cytokines by IL-17 in different hematopoietic organs

Characterization of antigen-presenting cells in human apical periodontitis lesions by flow cytometry and immunocytochemistry

Aim To analyse phenotypic characteristics of antigen-presenting cells (APC), isolated from human periapical lesions by flow cytometry and immunocytochemistry. Methodology Sixteen periapical lesions were digested for 15 min with 0.05% collagenase. Mononuclear cells, separated from other inflammatory cells by density centrifugation, were processed for flow cytometry and/or immunocytochemistry. Single and double immunostainings were performed using monoclonal antibodies specific for human CD45, CD3, CD19, CD14, HLA-DR, CD1a, CD83 and CD123. Results Antigen-presenting cells (HLA-DR+ cells) represented 32.9 +/- 17.8% of total mononuclear cells. Amongst them, B cells (HLA-DR+ CD19(+)) were the predominant APC population, followed by activated macrophages (HLA-DR+ CD14(+)), dendritic cells (DC) (HLA-DR+ CD14(-) CD19(-) CD3(-)) and activated T cells (HLA-DR+ CD3(+)). Based on the predominance of T cells (CD3(+)) or B cells and plasma cells (CD19(+) and CD19(lo), respectively) amongst mononuclear cell infiltrates, lesions were divided into T- and B-types. The percentage of DC in T-type lesions (27.1 +/- 6.8% of total HLA-DR+ cells) was higher, compared with B-type lesions (10.3 +/- 5.2%) (P lt 0.01). Within the DC population, the percentages of CD1a (Langerhans cell type) and CD123 (probably plasmacytoid DC type) did not differ significantly between the groups (P gt 0.05). However, the percentage of mature DC (CD83(+)) was significantly higher in T-type periapical lesions (P lt 0.05). Conclusion Flow cytometry and immunocytochemistry are suitable methods for phenotypic analysis of APC after their isolation from human periapical lesions. APC, that were phenotypically heterogeneous, constituted a significant component of infiltrating cells. Lesions with the predominance of T cells were characterized by a higher proportion of mature DC (HLA-DR(+)CD83(+) cells) than lesions with predominance of B cells/plasma cells

Interactions among myeloid regulatory cells in cancer

Mounting evidence has accumulated on the critical role of the different myeloid cells in the regulation of the cancerous process, and in particular in the modulation of the immune reaction to cancer. Myeloid cells are a major component of host cells infiltrating tumors, interacting with each other, with tumor cells and other stromal cells, and demonstrating a prominent plasticity. We describe here various myeloid regulatory cells (MRCs) in mice and human as well as their relevant therapeutic targets. We first address the role of the monocytes and macrophages that can contribute to angiogenesis, immunosuppression and metastatic dissemination. Next, we discuss the differential role of neutrophil subsets in tumor development, enhancing the dual and sometimes contradicting role of these cells. A heterogeneous population of immature myeloid cells, MDSCs, was shown to be generated and accumulated during tumor progression as well as to be an important player in cancer-related immune suppression. Lastly, we discuss the role of myeloid DCs, which can either contribute to effective anti-tumor responses or play a more regulatory role. We believe that MRCs play a critical role in cancer-related immune regulation and suggest that future anti-cancer therapies will focus on these abundant cells

On the Perceptual Organization of Image Databases Using Cognitive Discriminative Biplots

A human-centered approach to image database organization is presented in this study. The management of a generic image database is pursued using a standard psychophysical experimental procedure followed by a well-suited data analysis methodology that is based on simple geometrical concepts. The end result is a cognitive discriminative biplot, which is a visualization of the intrinsic organization of the image database best reflecting the user's perception. The discriminating power of the introduced cognitive biplot constitutes an appealing tool for image retrieval and a flexible interface for visual data mining tasks. These ideas were evaluated in two ways. First, the separability of semantically distinct image classes was measured according to their reduced representations on the biplot. Then, a nearest-neighbor retrieval scheme was run on the emerged low-dimensional terrain to measure the suitability of the biplot for performing content-based image retrieval (CBIR). The achieved organization performance when compared with the performance of a contemporary system was found superior. This promoted the further discussion of packing these ideas into a realizable algorithmic procedure for an efficient and effective personalized CBIR system

A Selective Weighted Late Fusion for Visual Concept Recognition

International audienceWe propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best features for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69% in 2011 which ranked 2nd out of the 79 submitted runs, and a MiAP of 43.67% that ranked 1st out of the 80 submitted runs in 2012