RESPIRE: A Spectral Kurtosis-Based Method to Extract Respiration Rate from Wearable PPG Signals

In this paper, we present the design of a wearable photoplethysmography (PPG) system, R-band for acquiring the PPG signals. PPG signals are influenced by the respiration or breathing process and hence can be used for estimation of respiration rate. R-Band detects the PPG signal that is routed to a Bluetooth low energy device such as a nearby-placed smartphone via microprocessor. Further, we developed an algorithm based on Extreme Learning Machine (ELM) regression for the estimation of respiration rate. We proposed spectral kurtosis features that are fused with the state-of the-art respiratory-induced amplitude, intensity and frequency variations-based features for the estimation of respiration rate (in units of breaths per minute). In contrast to the neural network (NN), ELM does not require tuning of hidden layer parameter and thus drastically reduces the computational cost as compared to NN trained by the standard backpropagation algorithm. We evaluated the proposed algorithm on Capnobase data available in the public domain

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

The increasing use of wearables in smart telehealth system led to the generation of large medical big data. Cloud and fog services leverage these data for assisting clinical procedures. IoT Healthcare has been benefited from this large pool of generated data. This paper suggests the use of low-resource machine learning on Fog devices kept close to wearables for smart telehealth. For traditional telecare systems, the signal processing and machine learning modules are deployed in the cloud that processes physiological data. This paper presents a Fog architecture that relied on unsupervised machine learning big data analysis for discovering patterns in physiological data. We developed a prototype using Intel Edison and Raspberry Pi that was tested on real-world pathological speech data from telemonitoring of patients with Parkinson\u27s disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smart watches worn by the patients with PD. Results show that proposed architecture is promising for low-resource machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Comparative genomics of the emerging human pathogen Photorhabdus asymbiotica with the insect pathogen Photorhabdus luminescens

<p>Abstract</p> <p>Background</p> <p>The Gram-negative bacterium <it>Photorhabdus asymbiotica </it>(Pa) has been recovered from human infections in both North America and Australia. Recently, Pa has been shown to have a nematode vector that can also infect insects, like its sister species the insect pathogen <it>P. luminescens </it>(Pl). To understand the relationship between pathogenicity to insects and humans in <it>Photorhabdus </it>we have sequenced the complete genome of Pa strain ATCC43949 from North America. This strain (formerly referred to as <it>Xenorhabdus luminescens </it>strain 2) was isolated in 1977 from the blood of an 80 year old female patient with endocarditis, in Maryland, USA. Here we compare the complete genome of Pa ATCC43949 with that of the previously sequenced insect pathogen <it>P. luminescens </it>strain TT01 which was isolated from its entomopathogenic nematode vector collected from soil in Trinidad and Tobago.</p> <p>Results</p> <p>We found that the human pathogen Pa had a smaller genome (5,064,808 bp) than that of the insect pathogen Pl (5,688,987 bp) but that each pathogen carries approximately one megabase of DNA that is unique to each strain. The reduced size of the Pa genome is associated with a smaller diversity in insecticidal genes such as those encoding the Toxin complexes (Tc's), Makes caterpillars floppy (Mcf) toxins and the <it>Photorhabdus </it>Virulence Cassettes (PVCs). The Pa genome, however, also shows the addition of a plasmid related to pMT1 from <it>Yersinia pestis </it>and several novel pathogenicity islands including a novel Type Three Secretion System (TTSS) encoding island. Together these data suggest that Pa may show virulence against man via the acquisition of the <it>pMT1</it>-like plasmid and specific effectors, such as SopB, that promote its persistence inside human macrophages. Interestingly the loss of insecticidal genes in Pa is not reflected by a loss of pathogenicity towards insects.</p> <p>Conclusion</p> <p>Our results suggest that North American isolates of Pa have acquired virulence against man via the acquisition of a plasmid and specific virulence factors with similarity to those shown to play roles in pathogenicity against humans in other bacteria.</p

The role of TcdB and TccC subunits in secretion of the photorhabdus Tcd toxin complex

The Toxin Complex (TC) is a large multi-subunit toxin encoded by a range of bacterial pathogens. The best-characterized examples are from the insect pathogens Photorhabdus, Xenorhabdus and Yersinia. They consist of three large protein subunits, designated A, B and C that assemble in a 5:1:1 stoichiometry. Oral toxicity to a range of insects means that some have the potential to be developed as pest control technology. The three subunit proteins do not encode any recognisable export sequences and as such little progress has been made in understanding their secretion. We have developed heterologous TC production and secretion models in E. coli and used them to ascribe functions to different domains of the crucial B+C sub-complex. We have determined that the B and C subunits use a secretion mechanism that is either encoded by the proteins themselves or employ an as yet undefined system common to laboratory strains of E. coli. We demonstrate that both the N-terminal domains of the B and C subunits are required for secretion of the whole complex. We propose a model whereby the N-terminus of the C-subunit toxin exports the B+C sub-complex across the inner membrane while that of the B-subunit allows passage across the outer membrane. We also demonstrate that even in the absence of the B-subunit, that the C-subunit can also facilitate secretion of the larger A-subunit. The recognition of this novel export system is likely to be of importance to future protein secretion studies. Finally, the identification of homologues of B and C subunits in diverse bacterial pathogens, including Burkholderia and Pseudomonas, suggests that these toxins are likely to be important in a range of different hosts, including man

Photorhabdus adhesion modification protein (Pam) binds extracellular polysaccharide and alters bacterial attachment

Background Photorhabdus are Gram-negative nematode-symbiotic and insect-pathogenic bacteria. The species Photorhabdus asymbiotica is able to infect humans as well as insects. We investigated the secreted proteome of a clinical isolate of P. asymbiotica at different temperatures in order to identify proteins relevant to the infection of the two different hosts. Results A comparison of the proteins secreted by a clinical isolate of P. asymbiotica at simulated insect (28°C) and human (37°C) temperatures led to the identification of a small and highly abundant protein, designated Pam, that is only secreted at the lower temperature. The pam gene is present in all Photorhabdus strains tested and shows a high level of conservation across the whole genus, suggesting it is both ancestral to the genus and probably important to the biology of the bacterium. The Pam protein shows limited sequence similarity to the 13.6 kDa component of a binary toxin of Bacillus thuringiensis. Nevertheless, injection or feeding of heterologously produced Pam showed no insecticidal activity to either Galleria mellonella or Manduca sexta larvae. In bacterial colonies, Pam is associated with an extracellular polysaccharide (EPS)-like matrix, and modifies the ability of wild-type cells to attach to an artificial surface. Interestingly, Surface Plasmon Resonance (SPR) binding studies revealed that the Pam protein itself has adhesive properties. Although Pam is produced throughout insect infection, genetic knockout does not affect either insect virulence or the ability of P. luminescens to form a symbiotic association with its host nematode, Heterorhabditis bacteriophora. Conclusions We studied a highly abundant protein, Pam, which is secreted in a temperature-dependent manner in P. asymbiotica. Our findings indicate that Pam plays an important role in enhancing surface attachment in insect blood. Its association with exopolysaccharide suggests it may exert its effect through mediation of EPS properties. Despite its abundance and conservation in the genus, we find no evidence for a role of Pam in either virulence or symbiosis

Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus

Vector population control using insecticides is a key element of current strategies to prevent malaria transmission in Africa. The introduction of effective insecticides, such as the organophosphate pirimiphos-methyl, is essential to overcome the recurrent emergence of resistance driven by the highly diverse Anopheles genomes. Here, we use a population genomic approach to investigate the basis of pirimiphos-methyl resistance in the major malaria vectors Anopheles gambiae and A. coluzzii. A combination of copy number variation and a single non-synonymous substitution in the acetylcholinesterase gene, Ace1, provides the key resistance diagnostic in an A. coluzzii population from Côte d’Ivoire that we used for sequence-based association mapping, with replication in other West African populations. The Ace1 substitution and duplications occur on a unique resistance haplotype that evolved in A. gambiae and introgressed into A. coluzzii, and is now common in West Africa primarily due to selection imposed by other organophosphate or carbamate insecticides. Our findings highlight the predictive value of this complex resistance haplotype for phenotypic resistance and clarify its evolutionary history, providing tools to for molecular surveillance of the current and future effectiveness of pirimiphos-methyl based interventions