Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Community participation and recovery for mental health service users: An action research inquiry

Introduction: The social inclusion of individuals with mental health problems is an issue for mental health services, for the individuals who experience stigma, discrimination and exclusion, and for society at large. To develop community-orientated services that are capable of promoting inclusion it will, therefore, be advantageous to all parties to understand what service users find most helpful. Method: A 2-year action research project explored the recovery journeys of a group of assertive outreach service users who had progressed from being socially excluded and occupationally deprived to being participants in their local communities. The research aimed to understand how these outcomes were produced and to use this knowledge to inform local service development. Findings: This paper focuses on eight qualitative interviews, where service users recounted their stories of community participation and inclusion. The findings show how assertive outreach practitioners harnessed occupation as a basis for building relationships between practitioners and service users, and how this became a conduit towards participation in the mainstream community. Conclusion: Facilitating engagement in community-based occupations through creative collaboration helped participants reconnect with cherished roles, achieve long-standing goals and develop feelings of self-efficacy, belonging and wellbeing.© The College of Occupational Therapists Ltd

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Towards S-NAMO: Socially-aware Navigation Among Movable Obstacles

International audienc

The antioxidant enzyme peroxiredoxin-2 is depleted in lymphocytes seven days after ultra-endurance exercise

Purpose: Peroxiredoxin-2 (PRDX-2) is an antioxidant and chaperone-like protein critical for cell function. This study examined whether the levels of lymphocyte PRDX-2 are altered over one month following ultra-endurance exercise. Methods: Nine middle-aged men undertook a single-stage, multi-day 233 km (145 mile) ultra-endurance running race. Blood was collected immediately before (PRE), upon completion/retirement (POST), and following the race at DAY 1, DAY 7 and DAY 28. Lymphocyte lysates were examined for PRDX-2 by reducing SDS-PAGE and western blotting. In a sub-group of men who completed the race (n = 4) PRDX-2 oligomeric state (indicative of redox status) was investigated. Results: Ultra-endurance exercise caused significant changes in lymphocyte PRDX-2 (F (4,32) 3.409, p=0.020, ?(2) =0.299): seven-days after the race, PRDX-2 levels in lymphocytes had fallen to 30% of pre-race values (p=0.013) and returned to near-normal levels at DAY 28. Non-reducing gels demonstrated that dimeric PRDX-2 (intracellular reduced PRDX-2 monomers) was increased in 3 of 4 race completers immediately post-race, indicative of an "antioxidant response". Moreover, monomeric PRDX-2 was also increased immediately post-race in 2 of 4 race-completing subjects, indicative of oxidative damage, which was not detectable by DAY 7. Conclusions: Lymphocyte PRDX-2 was decreased below normal levels 7 days after ultra-endurance exercise. Excessive accumulation of reactive oxygen species induced by ultra-endurance exercise may underlie depletion of lymphocyte PRDX-2 by triggering its turnover after oxidation. Low levels of lymphocyte PRDX-2 could influence cell function and might, in part, explain reports of dysregulated immunity following ultra-endurance exercise

Neutrophils in cancer: neutral no more

Neutrophils are indispensable antagonists of microbial infection and facilitators of wound healing. In the cancer setting, a newfound appreciation for neutrophils has come into view. The traditionally held belief that neutrophils are inert bystanders is being challenged by the recent literature. Emerging evidence indicates that tumours manipulate neutrophils, sometimes early in their differentiation process, to create diverse phenotypic and functional polarization states able to alter tumour behaviour. In this Review, we discuss the involvement of neutrophils in cancer initiation and progression, and their potential as clinical biomarkers and therapeutic targets

Physical health behaviours and health locus of control in people with schizophrenia-spectrum disorder and bipolar disorder: a cross-sectional comparative study with people with non-psychotic mental illness

<p>Abstract</p> <p>Background</p> <p>People with mental illness experience high levels of morbidity and mortality from physical disease compared to the general population. Our primary aim was to compare how people with severe mental illness (SMI; i.e. schizophrenia-spectrum disorders and bipolar disorder) and non-psychotic mental illness perceive their: (i) global physical health, (ii) barriers to improving physical health, (iii) physical health with respect to important aspects of life and (iv) motivation to change modifiable high-risk behaviours associated with coronary heart disease. A secondary aim was to determine health locus of control in these two groups of participants.</p> <p>Methods</p> <p>People with SMI and non-psychotic mental illness were recruited from an out-patient adult mental health service in London. Cross-sectional comparison between the two groups was conducted by means of a self-completed questionnaire.</p> <p>Results</p> <p>A total of 146 people participated in the study, 52 with SMI and 94 with non-psychotic mental illness. There was no statistical difference between the two groups with respect to the perception of global physical health. However, physical health was considered to be a less important priority in life by people with SMI (OR 0.5, 95% CI 0.2-0.9, <it>p </it>= 0.029). There was no difference between the two groups in their desire to change high risk behaviours. People with SMI are more likely to have a health locus of control determined by powerful others (<it>p </it>< 0.001) and chance (<it>p </it>= 0.006).</p> <p>Conclusions</p> <p>People with SMI appear to give less priority to their physical health needs. Health promotion for people with SMI should aim to raise awareness of modifiable high-risk lifestyle factors. Findings related to locus of control may provide a theoretical focus for clinical intervention in order to promote a much needed behavioural change in this marginalised group of people.</p

Genome-Wide Profiling Identified a Set of miRNAs that Are Differentially Expressed in Glioblastoma Stem Cells and Normal Neural Stem Cells

A major challenge in cancer research field is to define molecular features that distinguish cancer stem cells from normal stem cells. In this study, we compared microRNA (miRNA) expression profiles in human glioblastoma stem cells and normal neural stem cells using combined microarray and deep sequencing analyses. These studies allowed us to identify a set of 10 miRNAs that are considerably up-regulated or down-regulated in glioblastoma stem cells. Among them, 5 miRNAs were further confirmed to have altered expression in three independent lines of glioblastoma stem cells by real-time RT-PCR analysis. Moreover, two of the miRNAs with increased expression in glioblastoma stem cells also exhibited elevated expression in glioblastoma patient tissues examined, while two miRNAs with decreased expression in glioblastoma stem cells displayed reduced expression in tumor tissues. Furthermore, we identified two oncogenes, NRAS and PIM3, as downstream targets of miR-124, one of the down-regulated miRNAs; and a tumor suppressor, CSMD1, as a downstream target of miR-10a and miR-10b, two of the up-regulated miRNAs. In summary, this study led to the identification of a set of miRNAs that are differentially expressed in glioblastoma stem cells and normal neural stem cells. Characterizing the role of these miRNAs in glioblastoma stem cells may lead to the development of miRNA-based therapies that specifically target tumor stem cells, but spare normal stem cells

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.Background: Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. \ud \ud Methodology and Principal Findings: A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. \ud \ud Conclusion and Significance: Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level

Proton Magnetic Resonance Spectroscopy in 22q11 Deletion Syndrome

OBJECTIVE: People with velo-cardio-facial syndrome or 22q11 deletion syndrome (22q11DS) have behavioral, cognitive and psychiatric problems. Approximately 30% of affected individuals develop schizophrenia-like psychosis. Glutamate dysfunction is thought to play a crucial role in schizophrenia. However, it is unknown if and how the glutamate system is altered in 22q11DS. People with 22q11DS are vulnerable for haploinsufficiency of PRODH, a gene that codes for an enzyme converting proline into glutamate. Therefore, it can be hypothesized that glutamatergic abnormalities may be present in 22q11DS. METHOD: We employed proton magnetic resonance spectroscopy ((1)H-MRS) to quantify glutamate and other neurometabolites in the dorsolateral prefrontal cortex (DLPFC) and hippocampus of 22 adults with 22q11DS (22q11DS SCZ+) and without (22q11DS SCZ-) schizophrenia and 23 age-matched healthy controls. Also, plasma proline levels were determined in the 22q11DS group. RESULTS: We found significantly increased concentrations of glutamate and myo-inositol in the hippocampal region of 22q11DS SCZ+ compared to 22q11DS SCZ-. There were no significant differences in levels of plasma proline between 22q11DS SCZ+ and 22q11DS SCZ-. There was no relationship between plasma proline and cerebral glutamate in 22q11DS. CONCLUSION: This is the first in vivo(1)H-MRS study in 22q11DS. Our results suggest vulnerability of the hippocampus in the psychopathology of 22q11DS SCZ+. Altered hippocampal glutamate and myo-inositol metabolism may partially explain the psychotic symptoms and cognitive impairments seen in this group of patients