A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated

Probability of Task Completion and Energy Consumption in Cooperative Pervasive Mobile Computing

It is challenging for multiple smartphones to complete a given task in large-scale pervasive sensing systems cooperatively. Sensing paradigms such as opportunistic sensing, participatory sensing, and hybrid sensing have been used for smartphones to work together seamlessly under different contexts. However, these existing paradigms do not incorporate the energy problem and sharing sensory resources of applications. In this paper, we revisit sensing paradigms regarding the probability of task completion and energy consumption for smartphones to cooperatively complete a sensing task. In addition, we propose a symbiotic sensing paradigm that can significantly save smartphone batteries while maintaining equivalent performance to existing paradigms, provided that the smartphones allow applications to share sensing resources. We also quantitatively evaluate our probabilistic models with a realistic case study. This work is a useful aid to designing and evaluating large-scale smartphone-based sensing systems before deployment, which saves money and effort

SomBe:Self-Organizing Map for Unstructured and Non-Coordinated iBeacon Constellations

Bluetooth Low Energy (BLE) devices such as iBeacons have been popularly deployed for Location Based Services (LBS), including indoor infrastructure monitoring, positioning, and navigation. In these applications, the positions of iBeacons are assumed to be known. However, the location information is often unavailable or inaccurate as most iBeacons were deployed by different external parties. In addition, manual localizing the already-deployed iBeacons is costly and even impractical, especially in large-scale and complex indoor environments. Therefore, we propose a novel method, namely SomeBe, which can localize deployed iBeacons with a minimal effort and invasiveness to existing infrastructures. Specifically, our approach uses cooperative multilateration based on Received Signal Strength (RSS) of available smartphones and WiFi access points (APs) in the environment. Both Bluetooth signal strengths (between smartphones and iBeacons) and WiFi signal strengths (between smartphones and APs) are jointly employed in a single optimization cost function to surpass the local minima. Requiring that the positions of the APs are known only, the proposed cost function can also localize the iBeacons without knowing the positions of smartphones. To improve the localization accuracy, we employ a clustering method based on the RSS values for the coarse estimation of iBeacons' positions. SomBe also can be used to simplify iBeacon deployment as it can localize the iBeacons with a minimal effort. The performance evaluation results of our testbed experiments as well as realistic simulations show that SomBe outperforms non-cooperative approaches with 85% better in terms of accuracy

The global burden of tuberculous meningitis in adults: a modelling study

Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. The incidence and mortality of TBM is unknown due to diagnostic challenges and limited disaggregated reporting of treated TBM by existing surveillance systems. We aimed to estimate the incidence and mortality of TBM in adults (15+ years) globally. Using national surveillance data from Brazil, South Africa, the United Kingdom, the United States of America, and Vietnam, we estimated the fraction of reported tuberculosis that is TBM, and the case fatality ratios for treated TBM in each of these countries. We adjusted these estimates according to findings from a systematic review and meta-analysis and applied them to World Health Organization tuberculosis notifications and estimates to model the global TBM incidence and mortality. Assuming the case detection ratio (CDR) for TBM was the same as all TB, we estimated that in 2019, 164,000 (95% UI; 129,000–199,000) adults developed TBM globally; 23% were among people living with HIV. Almost 60% of incident TBM occurred in males and 20% were in adults 25–34 years old. 70% of global TBM incidence occurred in Southeast Asia and Africa. We estimated that 78,200 (95% UI; 52,300–104,000) adults died of TBM in 2019, representing 48% of incident TBM. TBM case fatality in those treated was on average 27%. Sensitivity analysis assuming improved detection of TBM compared to other forms of TB (CDR odds ratio of 2) reduced estimated global mortality to 54,900 (95% UI; 32,200–77,700); assuming instead worse detection for TBM (CDR odds ratio of 0.5) increased estimated mortality to 125,000 (95% UI; 88,800–161,000). Our results highlight the need for improved routine TBM monitoring, especially in high burden countries. Reducing TBM incidence and mortality will be necessary to achieve the End TB Strategy targets

Recent Developments in Tuberculous Meningitis Pathogenesis and Diagnostics

The pathogenesis of Tuberculous meningitis (TBM) is poorly understood, but contemporary molecular biology technologies have allowed for recent improvements in our understanding of TBM. For instance, neutrophils appear to play a significant role in the immunopathogenesis of TBM, and either a paucity or an excess of inflammation can be detrimental in TBM. Further, severity of HIV-associated immunosuppression is an important determinant of inflammatory response; patients with the advanced immunosuppression (CD4+ T-cell count of &lt;150 cells/μL) having higher CSF neutrophils, greater CSF cytokine concentrations and higher mortality than those with CD4+ T-cell counts &gt; 150 cells/μL. Host genetics may also influence outcomes with LT4AH genotype predicting inflammatory phenotype, steroid responsiveness and survival in Vietnamese adults with TBM. Whist in Indonesia, CSF tryptophan level was a predictor of survival, suggesting tryptophan metabolism may be important in TBM pathogenesis. These varying responses mean that we must consider whether a “one-size-fits-all” approach to anti-bacillary or immunomodulatory treatment in TBM is truly the best way forward. Of course, to allow for proper treatment, early and rapid diagnosis of TBM must occur. Diagnosis has always been a challenge but the field of TB diagnosis is evolving, with sensitivities of at least 70% now possible in less than two hours with GeneXpert MTB/Rif Ultra. In addition, advanced molecular techniques such as CRISPR-MTB and metagenomic next generation sequencing may hold promise for TBM diagnosis. Host-based biomarkers and signatures are being further evaluated in childhood and adult TBM as adjunctive biomarkers as even with improved molecular assays, cases are still missed. A better grasp of host and pathogen behaviour may lead to improved diagnostics, targeted immunotherapy, and possibly biomarker-based, patient-specific treatment regimens.</ns4:p

Sources of Multidrug Resistance in Patients With Previous Isoniazid-Resistant Tuberculosis Identified Using Whole Genome Sequencing: A Longitudinal Cohort Study

Background Meta-analysis of patients with isoniazid-resistant tuberculosis given standard first-line anti-tuberculosis treatment indicated an increased risk of multi-drug resistant tuberculosis (MDR-TB) emerging (8%), compared to drug-sensitive tuberculosis (0.3%). Here we use whole genome sequencing (WGS) to investigate whether treatment of patients with pre-existing isoniazid resistant disease with first-line anti-tuberculosis therapy risks selecting for rifampicin resistance, and hence MDR-TB. Methods Patients with isoniazid-resistant pulmonary TB were recruited and followed up for 24 months. Drug-susceptibility testing was performed by Microscopic observation drug-susceptibility assay (MODS), Mycobacterial Growth Indicator Tube (MGIT) and by WGS on isolates at first presentation and in the case of re-presentation. Where MDR-TB was diagnosed, WGS was used to determine the genomic relatedness between initial and subsequent isolates. De novo emergence of MDR-TB was assumed where the genomic distance was five or fewer single nucleotide polymorphisms (SNPs) whereas reinfection with a different MDR-TB strain was assumed where the distance was 10 or more SNPs. Results 239 patients with isoniazid-resistant pulmonary tuberculosis were recruited. Fourteen (14/239, 5.9%) patients were diagnosed with a second episode of tuberculosis that was multi-drug resistant. Six (6/239, 2.5%) were identified as having evolved MDR-TB de novo and six as having been re-infected with a different strain. In two cases the genomic distance was between 5-10 SNPs and therefore indeterminate. Conclusions In isoniazid-resistant TB, de novo emergence and reinfection of MDR-TB strains equally contributed to MDR development. Early diagnosis and optimal treatment of isoniazid resistant TB are urgently needed to avert the de novo emergence of MDR-TB during treatment