Spatiotemporal model or time series model for assessing city-wide temperature effects on mortality?

Most studies examining the temperature���mortality association in a city used temperatures from one site or the average from a network of sites. This may cause measurement error as temperature varies across a city due to effects such as urban heat islands. We examined whether spatiotemporal models using spatially resolved temperatures produced different associations between temperature and mortality compared with time series models that used non-spatial temperatures. We obtained daily mortality data in 163 areas across Brisbane city, Australia from 2000 to 2004. We used ordinary kriging to interpolate spatial temperature variation across the city based on 19 monitoring sites. We used a spatiotemporal model to examine the impact of spatially resolved temperatures on mortality. Also, we used a time series model to examine non-spatial temperatures using a single site and the average temperature from three sites. We used squared Pearson scaled residuals to compare model fit. We found that kriged temperatures were consistent with observed temperatures. Spatiotemporal models using kriged temperature data yielded slightly better model fit than time series models using a single site or the average of three sites' data. Despite this better fit, spatiotemporal and time series models produced similar associations between temperature and mortality. In conclusion, time series models using non-spatial temperatures were equally good at estimating the city-wide association between temperature and mortality as spatiotemporal models.

Effects of temperature on mortality in Chiang Mai city, Thailand : a time series study

Background The association between temperature and mortality has been examined mainly in North America and Europe. However, less evidence is available in developing countries, especially in Thailand. In this study, we examined the relationship between temperature and mortality in Chiang Mai city, Thailand, during 1999���2008. Method A time series model was used to examine the effects of temperature on cause-specific mortality (non-external, cardiopulmonary, cardiovascular, and respiratory) and age-specific non-external mortality (<=64, 65���74, 75���84, and���>���=85���years), while controlling for relative humidity, air pollution, day of the week, season and long-term trend. We used a distributed lag non-linear model to examine the delayed effects of temperature on mortality up to 21���days. Results We found non-linear effects of temperature on all mortality types and age groups. Both hot and cold temperatures resulted in immediate increase in all mortality types and age groups. Generally, the hot effects on all mortality types and age groups were short-term, while the cold effects lasted longer. The relative risk of non-external mortality associated with cold temperature (19.35��C, 1st percentile of temperature) relative to 24.7��C (25th percentile of temperature) was 1.29 (95% confidence interval (CI): 1.16, 1.44) for lags 0���21. The relative risk of non-external mortality associated with high temperature (31.7��C, 99th percentile of temperature) relative to 28��C (75th percentile of temperature) was 1.11 (95% CI: 1.00, 1.24) for lags 0���21. Conclusion This study indicates that exposure to both hot and cold temperatures were related to increased mortality. Both cold and hot effects occurred immediately but cold effects lasted longer than hot effects. This study provides useful data for policy makers to better prepare local responses to manage the impact of hot and cold temperatures on population health.

Made by motion, Series one

���Made by Motion��� is a collaboration between digital artist Paul Van Opdenbosch and performer and choreographer Elise May; a series of studies on captured motion data used to generating experimental visual forms that reverberate in space and time. The project investigates the invisible forces generated by and influencing the movement of a dancer. Along with how the forces can be captured and applied to generating visual outcomes that surpass simple data visualisation, projecting the intent of the performer���s movements. The source or ���seed��� comes from using an Xsens MVN - Inertial Motion Capture system to capture spontaneous dance movements, with the visual generation conducted through a customised dynamics simulation. In this first series the visual investigation focused on manipulating the movement date at the instance of capture, capture been the recording of three-dimensional movement as ���seen��� by the hardware and ���understood��� through the calibration of software. By repositioning the capture hardware on the body we can effectively change how the same sequence of movements is ���seen��� by the motion capture system thus generating a different visual result from effetely identical movement. The outcomes from the experiments clearly demonstrates the effectiveness of using motion capture hardware as a creative tool to manipulate the perception of the capture subject, in this case been a sequence of dance movements. The creative work exhibited is a cross-section of the experiments conducted in practice with the first animated work (Movement A - Control) using the motion capture hardware in its default ���normal��� configuration. Following this is the lower body moved to the upper body (Lb-Ub), right arm moved onto the left arm (Ra-La), right leg moved onto the left leg (Rl-Ll) and finally the left leg moved onto a object that is then held in the left hand (Ll-Pf (Lh)).

Evaluation of brief dietary questions to estimate vegetable and fruit consumption ��� using serum carotenoids and red-cell folate

Objective: To evaluate responses to self-administered brief questions regarding consumption of vegetables and fruit by comparison with blood levels of serum carotenoids and red-cell folate. Design: A cross-sectional study in which participants reported their usual intake of fruit and vegetables in servings per day, and serum levels of five carotenoids (��-carotene, ��-carotene, ��-cryptoxanthin, lutein/zeaxanthin and lycopene) and red-cell folate were measured. Serum carotenoid levels were determined by high-performance liquid chromatography, and red-cell folate by an automated immunoassay system. Settings and subjects: Between October and December 2000, a sample of 1598 adults aged 25 years and over, from six randomly selected urban centres in Queensland, Australia, were examined as part of a national study conducted to determine the prevalence of diabetes and associated cardiovascular risk factors. Results: Statistically significant (P<0.01) associations with vegetable and fruit intake (categorised into groups: ���1 serving, 2���3 servings and ���4 servings per day) were observed for ��-carotene, ��-carotene, ��-cryptoxanthin, lutein/zeaxanthin and red-cell folate. The mean level of these carotenoids and of red-cell folate increased with increasing frequency of reported servings of vegetables and fruit, both before and after adjusting for potential confounding factors. A significant association with lycopene was observed only for vegetable intake before adjusting for confounders. Conclusions: These data indicate that brief questions may be a simple and valuable tool for monitoring vegetable and fruit intake in this population.

Rice ragged stunt oryzavirus genome segments S7 and S10 encode non-structural proteins of M(r) 68 025 (Pns7) and M(r) 32364 (Pns10) : brief report

The nucleotide sequences of genome segments S7 and S10 of a Thai-isolate of rice ragged stunt virus (RRSV) were determined. The 1938 bp S7 sequence contains a single large open reading frame (ORF) spanning nucleotides 20 to 1 843 that is predicted to encode a protein of M(r) 68 025. The 1 162 bp S10 sequence has a major ORF spanning nucleotides 142 to 1 032 that is predicted to encode a protein of M(r) 32364. This S10 ORF is preceded by a small ORF (nt 20-55) which is probably a minicistron. Coupled in vitro transcription-translation from the two major ORFs gave protein products of the expected sizes. However, no protein was visualised from S10 when the small ORF sequence was included. Proteins were expressed in Escherichia coli from the full length ORF of S7 (P7) and from a segment of the S10 ORF (P10) fused to the ORF of glutathione S-transferase (GST). Neither fusion protein was recognised by polyclonal antibodies raised against RRSV particles. Furthermore, polyclonal antibodies raised against GST-P7 fusion protein did not recognise any virion structural polypeptides. These data strongly suggest that the proteins P7 and P10 do not form part of RRSV particle. This is further supported by observed sequence homology (though very weak) of predicted.

Data innovation and open access [Blog]

After nearly fifteen years of the open access (OA) movement and its hard-fought struggle for a more open scholarly communication system, publishers are realizing that business models can be both open and profitable. Making journal articles available on an OA license is becoming an accepted strategy for maximizing the value of content to both research communities and the businesses that serve them. The first blog in this two-part series celebrating Data Innovation Day looks at the role that data-innovation is playing in the shift to open access for journal articles.

The impact of heatwaves on emergency department visits in Brisbane, Australia : a time series study

Introduction The acute health effects of heatwaves in a subtropical climate and their impact on emergency departments (ED) are not well known. The purpose of this study is to examine overt heat-related presentations to EDs associated with heatwaves in Brisbane. Methods Data were obtained for the summer seasons (December to February) from 2000-2012. Heatwave events were defined as two or more successive days with daily maximum temperature >=34[degree sign]C (HWD1) or >=37[degree sign]C (HWD2). Poisson generalised additive model was used to assess the effect of heatwaves on heat-related visits (International Classification of Diseases (ICD) 10 codes T67 and X30; ICD 9 codes 992 and E900.0). Results Overall, 628 cases presented for heat-related illnesses. The presentations significantly increased on heatwave days based on HWD1 (relative risk (RR) = 4.9, 95% confidence interval (CI): 3.8, 6.3) and HWD2 (RR = 18.5, 95% CI: 12.0, 28.4). The RRs in different age groups ranged between 3-9.2 (HWD1) and 7.5-37.5 (HWD2). High acuity visits significantly increased based on HWD1 (RR = 4.7, 95% CI: 2.3, 9.6) and HWD2 (RR = 81.7, 95% CI: 21.5, 310.0). Average length of stay in ED significantly increased by >1 hour (HWD1) and >2 hours (HWD2). Conclusions Heatwaves significantly increase ED visits and workload even in a subtropical climate. The degree of impact is directly related to the extent of temperature increases and varies by socio-demographic characteristics of the patients. Heatwave action plans should be tailored according to the population needs and level of vulnerability. EDs should have plans to increase their surge capacity during heatwaves.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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A brief guide to synchrotron radiation-based microtomography in (structural) geology and rock mechanics

This contribution outlines Synchrotron-based X-ray micro-tomography and its potential use in structural geology and rock mechanics. The paper complements several recent reviews of X-ray microtomography. We summarize the general approach to data acquisition, post-processing as well as analysis and thereby aim to provide an entry point for the interested reader. The paper includes tables listing relevant beamlines, a list of all available imaging techniques, and available free and commercial software packages for data visualization and quantification. We highlight potential applications in a review of relevant literature including time-resolved experiments and digital rock physics. The paper concludes with a report on ongoing developments and upgrades at synchrotron facilities to frame the future possibilities for imaging sub-second processes in centimetre-sized samples.

The importance of accurate beam data measurement for SRT/SRS treatment planning

Introduction This study investigated the sensitivity of calculated stereotactic radiotherapy and radiosurgery doses to the accuracy of the beam data used by the treatment planning system. Methods Two sets of field output factors were acquired using fields smaller than approximately 1 cm2, for inclusion in beam data used by the iPlan treatment planning system (Brainlab, Feldkirchen, Germany). One set of output factors were measured using an Exradin A16 ion chamber (Standard Imaging, Middleton, USA). Although this chamber has a relatively small collecting volume (0.007 cm3), measurements made in small fields using this chamber are subject to the effects of volume averaging, electronic disequilibrium and chamber perturbations. The second, more accurate, set of measurements were obtained by applying perturbation correction factors, calculated using Monte Carlo simulations according to a method recommended by Cranmer-Sargison et al. [1] to measurements made using a 60017 unshielded electron diode (PTW, Freiburg, Germany). A series of 12 sample patient treatments were used to investigate the effects of beam data accuracy on resulting planned dose. These treatments, which involved 135 fields, were planned for delivery via static conformal arcs and 3DCRT techniques, to targets ranging from prostates (up to 8 cm across) to meningiomas (usually more than 2 cm across) to arterioveinous malformations, acoustic neuromas and brain metastases (often less than 2 cm across). Isocentre doses were calculated for all of these fields using iPlan, and the results of using the two different sets of beam data were evaluated. Results While the isocentre doses for many fields are identical (difference = 0.0 %), there is a general trend for the doses calculated using the data obtained from corrected diode measurements to exceed the doses calculated using the less-accurate Exradin ion chamber measurements (difference\0.0 %). There are several alarming outliers (circled in the Fig. 1) where doses differ by more than 3 %, in beams from sample treatments planned for volumes up to 2 cm across. Discussion and conclusions These results demonstrate that treatment planning dose calculations for SRT/SRS treatments can be substantially affected when beam data for fields smaller than approximately 1 cm2 are measured inaccurately, even when treatment volumes are up to 2 cm across.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Design of a garment for data collection of toddler language and physical activity

Design process phases of development, evaluation and implementation were used to create a garment to simultaneously collect reliable data of speech production and intensity of movement of toddlers (18-36 months). A series of prototypes were developed and evaluated that housed accelerometer-based motion sensors and a digital transmitter with microphone. The approved test garment was a top constructed from loop-faced fabric with interior pockets to house devices. Extended side panels allowed for sizing. In total, 56 toddlers (28 male; 28 female; 16-36 months of age) participated in the study providing pilot and baseline data. The test garment was effective in collecting data as evaluated for accuracy and reliability using ANOVA for accelerometer data, transcription of video for type of movement, and number and length of utterances for speech production. The data collection garment has been implemented in various studies across disciplines.

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

Audiencing through social media : a brief overview

Notwithstanding the problems with identifying audiences (c.f. Hartley, 1987), nor with sampling them (c.f. Turner, 2005), we contend that by using social media, it is at least possible to gain an understanding of the habits of those who chose to engage with content through social media. In this chapter, we will broadly outline the ways in which networks such as Twitter and Facebook can stand as proxies for audiences in a number of scenarios, and enable content creators, networks and researchers to understand the ways in which audiences come into existence, change over time, and engage with content. Beginning with the classic audience ��� television ��� we will consider the evolution of metrics from baseline volume metrics to the more sophisticated ���telemetrics��� that are the focus of our current work. We discuss the evolution of these metrics, from principles developed in the field of ���sabermetrics���, and highlight their effectiveness as both a predictor and a baseline for producers and networks to measure the success of their social media campaigns. Moving beyond the evaluation of the audiences engagement, we then move to consider the ���audiences��� themselves. Building on Hartley���s argument that audiences are ���imagined��� constructs (1987, p. 125), we demonstrate the continual shift of Australian television audiences, from episode to episode and series to series, demonstrating through our map of the Australian Twittersphere (Bruns, Burgess & Highfield, 2014) both the variation amongst those who directly engage with television content, and those who are exposed to it through their social media networks. Finally, by exploring overlaps between sporting events (such as the NRL and AFL Grand Finals), reality TV (such as Big Brother, My Kitchen Rules & Biggest Loser), soaps (e.g. Bold & The Beautiful, Home & Away), and current affairs programming (e.g. Morning Television & A Current Affair), we discuss to what extent it is possible to profile and categorize Australian television audiences. Finally, we move beyond television audiences to consider audiences around social media platforms themselves. Building on our map of the Australian Twittersphere (Bruns, Burgess & Highfield, 2014), and a pool of 5000 active Australian accounts, we discuss the interconnectedness of audiences around particular subjects, and how specific topics spread throughout the Twitter Userbase. Also, by using Twitter as a proxy, we consider the career of a number of popular YouTuber���s, utilizing a method we refer to as Twitter Accession charts (Bruns & Woodford, 2014) to identify the growth curves, and relate them to specific events in the YouTubers career, be that ���viral��� videos or collaborations, to discuss how audiences form around specific content creators.

A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples

A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series.