Development of smart inner city recreational facilities to encourage active living

Lowfield Park in Sheffield, UK is a green recreational space main-tained by the City Council. Lowfield Park was selected as the primary Sheffield FieldLab for the ProFit project which ended in 2015. The ProFit project was European Interreg IVbNWE funded with the aim of encouraging physical activ-ity through innovations in products, services and ICT systems. In 2014 the Sheffield Hallam University City Athletics Stadium (SHUCAS) was introduced as a secondary FieldLab. A number of innovative systems have been installed into the FieldLabs, these include: Pan Tilt Zoom cameras, automatically timed sprint and running tracks, outdoor displays/touchscreen and a gait analyser. This paper describes the hardware, software and cloud infrastructure created to enable these systems. Pilot testing has been carried out over the last year and has found a positive effect on both sites. The systems created will be taken for-ward to Sheffield’s Olympic Legacy Park, which is currently under develop-ment

Software Development Support for Shared Sensing Infrastructures: A Generative and Dynamic Approach

International audienceSensors networks are the backbone of large sensing infras-tructures such as Smart Cities or Smart Buildings. Classical approaches suffer from several limitations hampering developers' work (e.g., lack of sensor sharing, lack of dynamicity in data collection policies, need to dig inside big data sets, absence of reuse between implementation platforms). This paper presents a tooled approach that tackles these issues. It couples (i) an abstract model of developers' requirements in a given infrastructure to (ii) timed automata and code generation techniques, to support the efficient deployment of reusable data collection policies on different infrastructures. The approach has been validated on several real-world scenarios and is currently experimented on an academic campus

Distributed Anomaly Detection using Minimum Volume Elliptical Principal Component Analysis

Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models, however, in this environment links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighbouring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model

Quantitative in situ hybridization with enhanced sensitivity in soft, bone and tooth tissue using digoxigenin tagged RNA probes

Uvod: Kvantitativna neradioaktivna in situ hibridizacija je moćna tehnika za lokalizaciju ekspresije transkripata mRNA. Ove metode omogućavaju otkrivanje mRNA uz visoku rezoluciju na razini jedne jedine stanice. Dosad najšire opisani neradioaktivni protokoli rabili su deblje kriostatske rezove mekog tkiva i lokalizirali visoko zastupljene gene bez kvantificiranja. Materijali i metode: Mi smo razvili metodu neradioaktivne in situ hibridi-zacije pomoću tankih rezova demineraliziranog koštanog tkiva uklopljenog u parafin, uz otkrivanje nisko zastupljenih gena i kvantifikaciju in situ signala. Naš protokol zasniva se na optimalnoj sintezi digoksigeninom obilježenih DNA probi za vizualiziranje vrlo nisko zastupljenih gena u mekom, koštanom i zubnom tkivu uklopljenom u parafin. Naša nova tehnika prikazuje in situ signal uz umnožavanje i pojačanje boje kroz reakciju alkalnefosfataze. Rezultati: Osjetljivost je na razini radioaktivnih protokola, a rezolucija je na razini pojedine stanice, štoje bolje nego kod radioaktivnih protokola. Kvantificiranje in situ hibridizacijskog signala, što se ranije radilo pomoću radioaktivnog ili fluorescentnog obilježavanja, sad je moguće s alkalnom fosfatazom pomoću naše tehnike i programa ImageJ. Zaključak: Prikazani primjeri pokazuju kako slijedeća metoda ima bolju dokazanu rezoluciju i jednaku osjetljivost kao radioaktivno obilježene metode u različitim tkivima s mogućom kvantifikacijom;to pak pokazuje da se ova metoda općenito može rabiti u istraživačkim kao i u kliničkim laboratorijima.Introduction: Quantitative non-radioactive in situ hybridization is a powerful technique for localizing the expression of mRNA transcripts. These methods enable mRNAs to be detected with great resolution on a single cell level. Up to date the most published non-radioactive protocols used thick cryostat sections in soft tissue localizing high abundant genes without quantification. Material and methods: We developed a non-radioactive in situ hybridization method using thin sections of demineralized bone paraffin embedded tissue with low abundant gene detection and in situ signal quantification. Our protocol is based on the optimal synthesis of digoxigenin labeled RNA probes to visualize very low abundant genes in soft, bone and tooth paraffin embedded tissues. Our new technique visualizes an in situ signal with amplification and enhanced color by developing alkaline phosphatase reaction. Results: The sensitivity is at the level of radioactivity and the resolution is at the level of a single cell, which is better than with radioactivity. Quantification of in situ hybridization signal, previously used with radioactive or fluorescent labeling, is now possible with alkaline phosphatase using our technique and ImageJ program. Conclusion: The presented examples show that the following method has better proven resolution and equivalent sensitivity to radioactive labeled methods in different tissues with quantification ability, thus indicating that this method can generally be used in research and clinical laboratories

Inhibition of Osteoclastogenesis by Mechanically Loaded Osteocytes: Involvement of MEPE

In regions of high bone loading, the mechanoresponsive osteocytes inhibit osteoclastic bone resorption by producing signaling molecules. One possible candidate is matrix extracellular phosphoglycoprotein (MEPE) because acidic serine- and aspartate-rich MEPE-associated motif peptides upregulate osteoprotegerin (OPG) gene expression, a negative regulator of osteoclastogenesis. These peptides are cleaved from MEPE when relatively more MEPE than PHEX (phosphate-regulating gene with homology to endopeptidases on the X chromosome) is present. We investigated whether mechanical loading of osteocytes affects osteocyte-stimulated osteoclastogenesis by involvement of MEPE. MLO-Y4 osteocytes were mechanically loaded by 1-h pulsating fluid flow (PFF; 0.7 ± 0.3 Pa, 5 Hz) or kept under static control conditions. Recombinant MEPE (0.05, 0.5, or 5 μg/ml) was added to some static cultures. Mouse bone marrow cells were seeded on top of the osteocytes to determine osteoclastogenesis. Gene expression of MEPE, PHEX, receptor activator of nuclear factor kappa-B ligand (RANKL), and OPG by osteocytes was determined after PFF. Osteocytes supported osteoclast formation under static control conditions. Both PFF and recombinant MEPE inhibited osteocyte-stimulated osteoclastogenesis. PFF upregulated MEPE gene expression by 2.5-fold, but not PHEX expression. PFF decreased the RANKL/OPG ratio at 1-h PFF treatment. Our data suggest that mechanical loading induces changes in gene expression by osteocytes, which likely contributes to the inhibition of osteoclastogenesis after mechanical loading of bone. Because mechanical loading upregulated gene expression of MEPE but not PHEX, possibly resulting in the upregulation of OPG gene expression, we speculate that MEPE is a soluble factor involved in the inhibition of osteoclastogenesis by osteocytes