Bluetooth and Wi-Fi MAC address based crowd data collection and monitoring : benefits, challenges and enhancement

This paper firstly presents the benefits and critical challenges on the use of Bluetooth and Wi-Fi for crowd data collection and monitoring. The major challenges include antenna characteristics, environment’s complexity and scanning features. Wi-Fi and Bluetooth are compared in this paper in terms of architecture, discovery time, popularity of use and signal strength. Type of antennas used and the environment’s complexity such as trees for outdoor and partitions for indoor spaces highly affect the scanning range. The aforementioned challenges are empirically evaluated by “real” experiments using Bluetooth and Wi-Fi Scanners. The issues related to the antenna characteristics are also highlighted by experimenting with different antenna types. Novel scanning approaches including Overlapped Zones and Single Point Multi-Range detection methods will be then presented and verified by real-world tests. These novel techniques will be applied for location identification of the MAC IDs captured that can extract more information about people movement dynamics.

Tyrosine phosphorylation mediates ConA-induced membrane type 1-matrix metalloproteinase expression and matrix metalloproteinase-2 activation in MDA-MB-231 human breast carcinoma cells

ConA-induced cell surface activation of pro-matrix metalloproteinase-2 (pro-MMP-2) by MDA-MB-231 human breast cancer cells is apparently mediated by up-regulation of membrane type 1 MMP (MT1-MMP) through transcriptional and posttranscriptional mechanisms. Here, we have explored the respective roles of cell surface clustering and protein tyrosine phosphorylation in the ConA- induction effects. Treatment with succinyl-ConA, a variant lacking significant clusterability, partially stimulated MT1-MMP mRNA and protein levels but did not induce MMP-2 activation, suggesting that clustering contributes to the transcriptional regulation by ConA but appears to be critical for the nontranscriptional component. We further found that genistein, an inhibitor of tyrosine phosphorylation, blocked ConA-induced pro-MMP-2 activation and ConA-induced MT1-MMP mRNA level in a dose-dependent manner, implicating tyrosine phosphorylation in the transcriptional aspect. This was confirmed by the dose-dependent promotion of pro-MMP-2 activation by sodium orthovanadate in the presence of suboptimal concentrations of ConA (7.5 μg/ml), with optimal effects seen at 25 μg/g orthovanadate. Genistein did not inhibit the ConA potentiation of MMP-2 activation in MCF-7 cells, in which transfected MT1-MMP is driven by a heterologous promoter, supporting the major implication of phosphotyrosine in the transcriptional component of ConA regulation. These data describe a major signaling event upstream of MT1- MMP induction by ConA and set the stage for further analysis of the nontranscriptional component.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Mortality following hip arthroplasty—inappropriate use of National Joint Registry (NJR) data

Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.

Implication of collagen type I-induced membrane-type 1-matrix metalloproteinase expression and matrix metalloproteinase-2 activation in the metastatic progression of breast carcinoma

We have previously demonstrated that fibroblasts and invasive human breast carcinoma (HBC) cells specifically activate matrix metalloproteinase- 2 (MMP-2) when cultured on 3-dimensional gels of type I collagen but not a range of other substrates. We show here the constitutive expression of membrane-type 1 (MT1)-MMP in both fibroblasts, and invasive HBC cell lines, that have fibroblastic attributes presumably acquired through an epithelial- to-mesenchymal transition (EMT). Treatment with collagen type I increased the steady-state MT1-MMP mRNA levels in these cells but did not induce either MT1-MMP expression or MMP-2 activation in noninvasive breast carcinoma cell lines, which retain epithelial features. Basal MT3-MMP mRNA expression had a pattern similar to that of MT1-MMP but was not up-regulated by collagen. MT4- MMP mRNA was seen in both invasive and noninvasive HBC cell lines and was also not collagen-regulated, and MT2-MMP mRNA was not detected in any of the HBC cell lines tested. These data support a role for MT1-MMP in the collagen- induced MMP-2-activation seen in these cells. In situ hybridization analysis of archival breast cancer specimens revealed a close parallel in expression of both collagen type I and MT1-MMP mRNA in peritumoral fibroblasts, which was correlated with aggressiveness of the lesion. Relatively high levels of expression of both mRNA species were seen in fibroblasts close to invasive tumor nests and, although only focally, in certain areas close to preinvasive tumors. These foci may represent hot spots for local degradation and invasive progression. Collectively, these results implicate MT1-MMP in collagen- stimulated MMP-2 activation and suggest that this mechanism may be employed in vivo by both tumor-associated fibroblasts and EMT-derived carcinoma cells to facilitate increased invasion and/or metastasis.

Twitter data analytics – or : the pleasures and perils of studying Twitter

It might still sound strange to dedicate an entire journal issue exclusively to a single internet platform. But it is not the company Twitter Inc. that draws our attention; this issue is not about a platform and its features and services. It is about its users and the ways in which they interact with one another via the platform, about the situations that motivate people to share their thoughts publicly, using Twitter as a means to reach out to one another. And it is about the digital traces people leave behind when interacting with Twitter, and most of all about the ways in which these traces – as a new type of research data – can also enable new types of research questions and insights.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Controlling current and voltage type interfaces in power-hardware-in-the-loop simulations

The usual practice to study a large power system is through digital computer simulation. However, the impact of large scale use of small distributed generators on a power network cannot be evaluated strictly by simulation since many of these components cannot be accurately modelled. Moreover, the network complexity makes the task of practical testing on a physical network nearly impossible. This study discusses the paradigm of interfacing a real-time simulation of a power system to real-life hardware devices. This type of splitting a network into two parts and running a real-time simulation with a physical system in parallel is usually termed as power-hardware-in-the-loop (PHIL) simulation. The hardware part is driven by a voltage source converter that amplifies the signals of the simulator. In this paper, the effects of suitable control strategy on the performance of PHIL and the associated stability aspects are analysed in detail. The analyses are validated through several experimental tests using an real-time digital simulator.

Functional role for senataxin, defective in ataxia oculomotor apraxia type 2, in transcriptional regulation

Ataxia oculomotor apraxia type 2 (AOA2) is an autosomal recessive neurodegenerative disorder characterized by cerebellar ataxia and oculomotor apraxia. The gene mutated in AOA2, SETX, encodes senataxin, a putative DNA/RNA helicase which shares high homology to the yeast Sen1p protein and has been shown to play a role in the response to oxidative stress. To investigate further the function of senataxin, we identified novel senataxin-interacting proteins, the majority of which are involved in transcription and RNA processing, including RNA polymerase II. Binding of RNA polymerase II to candidate genes was significantly reduced in senataxin deficient cells and this was accompanied by decreased transcription of these genes, suggesting a role for senataxin in the regulation/modulation of transcription. RNA polymerase II-dependent transcription termination was defective in cells depleted of senataxin in keeping with the observed interaction of senataxin with poly(A) binding proteins 1 and 2. Splicing efficiency of specific mRNAs and alternate splice-site selection of both endogenous genes and artificial minigenes were altered in senataxin depleted cells. These data suggest that senataxin, similar to its yeast homolog Sen1p, plays a role in coordinating transcriptional events, in addition to its role in DNA repair.

Molecular analysis of type 3 fimbrial genes from Escherichia coli, Klebsiella and Citrobacter species

Background Catheter-associated urinary tract infection (CAUTI) is the most common nosocomial infection in the United States and is caused by a range of uropathogens. Biofilm formation by uropathogens that cause CAUTI is often mediated by cell surface structures such as fimbriae. In this study, we characterised the genes encoding type 3 fimbriae from CAUTI strains of Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca, Citrobacter koseri and Citrobacter freundii. Results Phylogenetic analysis of the type 3 fimbrial genes (mrkABCD) from 39 strains revealed they clustered into five distinct clades (A-E) ranging from one to twenty-three members. The majority of sequences grouped in clade A, which was represented by the mrk gene cluster from the genome sequenced K. pneumoniae MGH78578. The E. coli and K. pneumoniae mrkABCD gene sequences clustered together in two distinct clades, supporting previous evidence for the occurrence of inter-genera lateral gene transfer. All of the strains examined caused type 3 fimbriae mediated agglutination of tannic acid treated human erythrocytes despite sequence variation in the mrkD-encoding adhesin gene. Type 3 fimbriae deletion mutants were constructed in 13 representative strains and were used to demonstrate a direct role for type 3 fimbriae in biofilm formation. Conclusions The expression of functional type 3 fimbriae is common to many Gram-negative pathogens that cause CAUTI and is strongly associated with biofilm growth. Our data provides additional evidence for the spread of type 3 fimbrial genes by lateral gene transfer. Further work is now required to substantiate the clade structure reported here by examining more strains as well as other bacterial genera that make type 3 fimbriae and cause CAUTI.

A cost-effectiveness analysis of a telephone-linked care intervention for individuals with Type 2 diabetes

AIM: To assess the cost-effectiveness of an automated telephone-linked care intervention, Australian TLC Diabetes, delivered over 6 months to patients with established Type 2 diabetes mellitus and high glycated haemoglobin level, compared to usual care. METHODS: A Markov model was designed to synthesize data from a randomized controlled trial of TLC Diabetes (n=120) and other published evidence. The 5-year model consisted of three health states related to glycaemic control: 'sub-optimal' HbA1c ≥58mmol/mol (7.5%); 'average' ≥48-57mmol/mol (6.5-7.4%) and 'optimal' <48mmol/mol (6.5%) and a fourth state 'all-cause death'. Key outcomes of the model include discounted health system costs and quality-adjusted life years (QALYS) using SF-6D utility weights. Univariate and probabilistic sensitivity analyses were undertaken. RESULTS: Annual medication costs for the intervention group were lower than usual care [Intervention: £1076 (95%CI: £947, £1206) versus usual care £1271 (95%CI: £1115, £1428) p=0.052]. The estimated mean cost for intervention group participants over five years, including the intervention cost, was £17,152 versus £17,835 for the usual care group. The corresponding mean QALYs were 3.381 (SD 0.40) for the intervention group and 3.377 (SD 0.41) for the usual care group. Results were sensitive to the model duration, utility values and medication costs. CONCLUSION: The Australian TLC Diabetes intervention was a low-cost investment for individuals with established diabetes and may result in medication cost-savings to the health system. Although QALYs were similar between groups, other benefits arising from the intervention should also be considered when determining the overall value of this strategy.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

The OnTrack Diabetes web-based program for type 2 diabetes and dysphoria self-management: A randomized controlled trial protocol

Background: The prevalence of type 2 diabetes is rising with the majority of patients practicing inadequate disease self-management. Depression, anxiety, and diabetes-specific distress present motivational challenges to adequate self-care. Health systems globally struggle to deliver routine services that are accessible to the entire population, in particular in rural areas. Web-based diabetes self-management interventions can provide frequent, accessible support regardless of time and location Objective: This paper describes the protocol of an Australian national randomized controlled trial (RCT) of the OnTrack Diabetes program, an automated, interactive, self-guided Web program aimed to improve glycemic control, diabetes self-care, and dysphoria symptoms in type 2 diabetes patients. Methods: A small pilot trial is conducted that primarily tests program functionality, efficacy, and user acceptability and satisfaction. This is followed by the main RCT, which compares 3 treatments: (1) delayed program access: usual diabetes care for 3 months postbaseline followed by access to the full OnTrack Diabetes program; (2) immediate program: full access to the self-guided program from baseline onward; and (3) immediate program plus therapist support via Functional Imagery Training (FIT). Measures are administered at baseline and at 3, 6, and 12 months postbaseline. Primary outcomes are diabetes self-care behaviors (physical activity participation, diet, medication adherence, and blood glucose monitoring), glycated hemoglobin A1c (HbA1c) level, and diabetes-specific distress. Secondary outcomes are depression, anxiety, self-efficacy and adherence, and quality of life. Exposure data in terms of program uptake, use, time on each page, and program completion, as well as implementation feasibility will be conducted. Results: This trial is currently underway with funding support from the Wesley Research Institute in Brisbane, Australia. Conclusions: This is the first known trial of an automated, self-guided, Web-based support program that uses a holistic approach in targeting both type 2 diabetes self-management and dysphoria. Findings will inform the feasibility of implementing such a program on an ongoing basis, including in rural and regional locations.

Does school type affect cognitive and non-cognitive development in children? Evidence from Australian primary schools

This paper investigates the effects of primary school choices on cognitive and non-cognitive development in children using data from the Longitudinal Study of Australian Children (LSAC). We militate against the measurement problems that are associated with individual unobserved heterogeneity by exploiting the richness of LSAC data and applying contemporary econometric approaches. We find that sending children to Catholic or other independent primary schools has no significant effect on their cognitive and non-cognitive outcomes. The literature now has evidence from three different continents that the returns to attending Catholic primary schools are no different than public schools.