Fog Data: Enhancing Telehealth Big Data Through Fog Computing

The size of multi-modal, heterogeneous data collected through various sensors is growing exponentially. It demands intelligent data reduction, data mining and analytics at edge devices. Data compression can reduce the network bandwidth and transmission power consumed by edge devices. This paper proposes, validates and evaluates Fog Data, a service-oriented architecture for Fog computing. The center piece of the proposed architecture is a low power embedded computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth applications. The embedded computer collects the sensed data as time series, analyzes it, and finds similar patterns present. Patterns are stored, and unique patterns are transmited. Also, the embedded computer extracts clinically relevant information that is sent to the cloud. A working prototype of the proposed architecture was built and used to carry out case studies on telehealth big data applications. Specifically, our case studies used the data from the sensors worn by patients with either speech motor disorders or cardiovascular problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of magnitude data reduction and hence transmission power savings. Quantitative evaluations were conducted for comparing various data mining techniques and standard data compression techniques. The obtained results showed substantial improvement in system efficiency using the Fog Data architecture

Nematode Symbiont for Photorhabdus asymbiotica

Photorhabdus asymbiotica is an emerging bacterial pathogen that causes locally invasive soft tissue and disseminated bacteremic infections in the United States and Australia. Although the source of infection was previously unknown, we report that the bacterium is found in a symbiotic association with an insect-pathogenic soil nematode of the genus Heterorhabditis

From insect to man: Photorhabdus sheds light on the emergence of human pathogenicity

Photorhabdus are highly effective insect pathogenic bacteria that exist in a mutualistic relationship with Heterorhabditid nematodes. Unlike other members of the genus, Photorhabdus asymbiotica can also infect humans. Most Photorhabdus cannot replicate above 34°C, limiting their host-range to poikilothermic invertebrates. In contrast, P. asymbiotica must necessarily be able to replicate at 37°C or above. Many well-studied mammalian pathogens use the elevated temperature of their host as a signal to regulate the necessary changes in gene expression required for infection. Here we use RNA-seq, proteomics and phenotype microarrays to examine temperature dependent differences in transcription, translation and phenotype of P. asymbiotica at 28°C versus 37°C, relevant to the insect or human hosts respectively. Our findings reveal relatively few temperature dependant differences in gene expression. There is however a striking difference in metabolism at 37°C, with a significant reduction in the range of carbon and nitrogen sources that otherwise support respiration at 28°C. We propose that the key adaptation that enables P. asymbiotica to infect humans is to aggressively acquire amino acids, peptides and other nutrients from the human host, employing a so called “nutritional virulence” strategy. This would simultaneously cripple the host immune response while providing nutrients sufficient for reproduction. This might explain the severity of ulcerated lesions observed in clinical cases of Photorhabdosis. Furthermore, while P. asymbiotica can invade mammalian cells they must also resist immediate killing by humoral immunity components in serum. We observed an increase in the production of the insect Phenol-oxidase inhibitor Rhabduscin normally deployed to inhibit the melanisation immune cascade. Crucially we demonstrated this molecule also facilitates protection against killing by the alternative human complement pathway

Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine

Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server. A wide range of physiological features from various analysis domains, including modeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features which will be considered as inputs for the classifier. In order to record the PCG data set from multiple subjects over one year, an electronic stethoscope was used for collecting data that was connected to a mobile device. In this study, we used different types of classifiers in order to distinguish between healthy and pathological heart sounds, and a comparison on the performances revealed that support vector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training, on a dataset of 116 samples

A Comparative Characterization of Smart Textile Pressure Sensors

This research study investigates the impact of various insulating textile materials on the performance of smart textile pressure sensors made of conductive threads and piezo resistive material. We designed four sets of identical textile-based pressure sensors each of them integrating a different insulating textile substrate material. Each of these sensors underwent a series of tests that linearly increased and decreased a uniform pressure perpendicular to the surface of the sensors. The controlled change of the integration layer altered the characteristics of the pressure sensors including both the sensitivity and pressure ranges. Our experiments highlighted that the manufacturing design technique of textile material has a significant impact on the sensor; with evidence of reproducibility values directly relating to fabric dimensional stability and elasticity

A smartwatch-based service towards home exercise therapy for patients with peripheral arterial disease

Utilizing a consumer-grade smartwatch in conjunction with a prescribed exercise therapy plan can help to reduce the patient-level entry barriers into programs designed for patients with peripheral arterial disease, which affects millions of people worldwide. Currently, the alternative to this physical therapy plan is surgical therapy which costs between 3 and 5 billion annually. This paper presents the development and testing of WalkCoach app, a smart service system integrating a consumer-grade smartwatch (Polar M600) in the monitoring of supervised walking exercises. By monitoring a participant\u27s baseline activity and improvements with time, it will be possible to provide personalized exercise prescriptions that can be easily modified or personalized to adjust and optimize for improved walking ability as the therapy progresses. This paper demonstrates the accuracy of the smartwatch-based WalkCoach app in a pilot cohort study of 10 healthy older adults (\u3e65 yrs) who were recruited to perform a 400m overground walking task. Results are promising and show that the consumer-grade smartwatch accurately measures steps (step count = 637) compared to a video/manual step count (650 steps; Pearson\u27s r = 0.96, P \u3c0.001). In the future, WalkCoach will be improved to produce granular analytics on a patient\u27s compliance and performance to the supervised walking exercises

The Yersinia pseudotuberculosis and Yersinia pestis toxin complex is active against cultured mammalian cells.

The toxin complex (Tc) genes were first identified in the insect pathogen Photorhabdus luminescens and encode approximately 1 MDa protein complexes which are toxic to insect pests. Subsequent genome sequencing projects have revealed the presence of tc orthologues in a range of bacterial pathogens known to be associated with insects. Interestingly, members of the mammalian-pathogenic yersiniae have also been shown to encode Tc orthologues. Studies in Yersinia enterocolitica have shown that divergent tc loci either encode insect-active toxins or play a role in colonization of the gut in gastroenteritis models of rats. So far little is known about the activity of the Tc proteins in the other mammalian-pathogenic yersiniae. Here we present work to suggest that Tc proteins in Yersinia pseudotuberculosis and Yersinia pestis are not insecticidal toxins but have evolved for mammalian pathogenicity. We show that Tc is secreted by Y. pseudotuberculosis strain IP32953 during growth in media at 28 degrees C and 37 degrees C. We also demonstrate that oral toxicity of strain IP32953 to Manduca sexta larvae is not due to Tc expression and that lysates of Escherichia coli BL21 expressing the Yersinia Tc proteins are not toxic to Sf9 insect cells but are toxic to cultured mammalian cell lines. Cell lysates of E. coli BL21 expressing the Y. pseudotuberculosis Tc proteins caused actin ruffles, vacuoles and multi-nucleation in cultured human gut cells (Caco-2); similar morphology was observed after application of a lysate of E. coli BL21 expressing the Y. pestis Tc proteins to mouse fibroblast NIH3T3 cells, but not Caco-2 cells. Finally, transient expression of the individual Tc proteins in Caco-2 and NIH3T3 cell lines reproduced the actin and nuclear rearrangement observed with the topical applications. Together these results add weight to the growing hypothesis that the Tc proteins in Y. pseudotuberculosis and Y. pestis have been adapted for mammalian pathogenicity. We further conclude that Tc proteins from Y. pseudotuberculosis and Y. pestis display differential mammalian cell specificity in their toxicity