Real-time classification of multivariate olfaction data using spiking neural networks

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an SNN-based solution for the classification of rank-order spiking patterns to provide continuous recognition results in real-time. The SNN classifier is deployed on a neuromorphic hardware system that enables massively parallel and low-power processing on incoming rank-order patterns. Offline learning is used to store the reference rank-order patterns, and an inbuilt nearest neighbor classification logic is applied by the neurons to provide recognition results. The proposed system was evaluated using two different datasets including rank-order spiking data from previously established olfactory systems. The continuous classification that was achieved required a maximum of 12.82% of the total pattern frame to provide 96.5% accuracy in identifying corresponding target gases. Recognition results were obtained at a nominal processing latency of 16ms for each incoming spike. In addition to the clear advantages in terms of real-time operation and robustness to inconsistent rank-orders, the SNN classifier can also detect anomalies in rank-order patterns arising due to drift in sensing arrays

Shared care obesity management in 3-10 year old children: 12 month outcomes of HopSCOTCH randomised trial

Objective: To determine whether general practice surveillance for childhood obesity, followed by obesity management across primary and tertiary care settings using a shared care model, improves body mass index and related outcomes in obese children aged 3-10 years. Design: Randomised controlled trial. Setting: 22 family practices (35 participating general practitioners) and a tertiary weight management service (three paediatricians, two dietitians) in Melbourne, Australia. Participants: Children aged 3-10 years with body mass index above the 95th centile recruited through their general practice between July 2009 and April 2010. Intervention: Children were randomly allocated to one tertiary appointment followed by up to 11 general practice consultations over one year, supported by shared care, web based software (intervention) or “usual care” (control). Researchers collecting outcome measurements, but not participants, were blinded to group assignment. Main outcome measures: Children’s body mass index z score (primary outcome), body fat percentage, waist circumference, physical activity, quality of diet, health related quality of life, self esteem, and body dissatisfaction and parents’ body mass index (all 15 months post-enrolment). Results: 118 (60 intervention, 56 control) children were recruited and 107 (91%) were retained and analysed (56 intervention, 51 control). All retained intervention children attended the tertiary appointment and their general practitioner for at least one (mean 3.5 (SD 2.5, range 1-11)) weight management consultation. At outcome, children in the two trial arms had similar body mass index (adjusted mean difference −0.1 (95% confidence interval −0.7 to 0.5; P=0.7)) and body mass index z score (−0.05 (−0.14 to 0.03); P=0.2). Similarly, no evidence was found of benefit or harm on any secondary outcome. Outcomes varied widely in the combined cohort (mean change in body mass index z score −0.20 (SD 0.25, range −0.97-0.47); 26% of children resolved from obese to overweight and 2% to normal weight. Conclusions: Although feasible, not harmful, and highly rated by both families and general practitioners, the shared care model of primary and tertiary care management did not lead to better body mass index or other outcomes for the intervention group compared with the control group. Improvements in body mass index in both groups highlight the value of untreated controls when determining efficacy.Melissa Wake, Kate Lycett, Susan A Clifford, Matthew A Sabin, Jane Gunn, Kay Gibbons, Cathy Hutton, Zoë McCallum, Sarah J Arnup, Gary Witter

Unknowable bodies, unthinkable sexualities: lesbian and transgender legal invisibility in the Toronto women's bathhouse raid

Although litigation involving sexual orientation and gender identity discrimination claims has generated considerable public attention in recent years, lesbian and transgender bodies and sexualities still remain largely invisible in Anglo-American courts. While such invisibility is generally attributed to social norms that fail to recognize lesbian and transgender experiences, the capacity to 'not see' or 'not know' queer bodies and sexualities also involves wilful acts of ignorance. Drawing from R. v Hornick (2002) a Canadian case involving the police raid of a women's bathhouse, this article explores how lesbian and transgender bodies and sexualities are actively rendered invisible via legal knowledge practices, norms and rationalities. It argues that limited knowledge and limited thinking not only regulate the borders of visibility and belonging, but play an active part in shaping identities, governing conduct and producing subjectivity

Blinded and unblinded sample size reestimation procedures for stepped-wedge cluster randomized trials.

The ability to accurately estimate the sample size required by a stepped-wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are misspecified, the trial could be overpowered, leading to increased cost, or underpowered, enhancing the likelihood of a false negative. We address this issue here for cross-sectional SW-CRTs, analyzed with a particular linear-mixed model, by proposing methods for blinded and unblinded sample size reestimation (SSRE). First, blinded estimators for the variance parameters of a SW-CRT analyzed using the Hussey and Hughes model are derived. Following this, procedures for blinded and unblinded SSRE after any time period in a SW-CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW-CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW-CRTs

The quality of reporting in cluster randomised crossover trials: proposal for reporting items and an assessment of reporting quality

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.SA was supported in part by a Monash University Graduate Scholarship and a National Health and Medical Research Council of Australia Centre of Research Excellence grant (1035261) to the Victorian Centre for Biostatistics (ViCBiostat). Funding was provided to KM through a National Institute for Health Research (NIHR) research methods fellowship (MET-12-16). JM was supported by a National Health and Medical Research Council (NHMRC) Australian Public Health Fellowship (1072366)

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome

NIHR research methods fellowship (MET-12-16)

Exposure to adversity and inflammatory outcomes in mid and late childhood

Background We aimed to estimate the association between exposure to adversity and inflammatory markers in mid (4 years) and late (11-12 years) childhood, and whether effects differ by type and timing of exposure. Methods Data sources: Barwon Infant Study (BIS; N = 510 analyzed) and Longitudinal Study of Australian Children (LSAC; N = 1156 analyzed). Exposures: Adversity indicators assessed from 0 to 4 (BIS) and 0-11 years (LSAC): parent legal problems, mental illness and substance abuse, anger in parenting responses, separation/divorce, unsafe neighborhood, and family member death; a count of adversities; and, in LSAC only, early (0-3), middle (4-7), or later (10-11) initial exposure. Outcomes: Inflammation quantified by high sensitivity C-reactive protein (hsCRP, Log (ug/ml)) and glycoprotein acetyls (GlycA, Log (umol/L)). Analyses: Linear regression was used to estimate relative change in inflammatory markers, adjusted for sociodemographic characteristics, with exposure to adversity. Outcomes were log-transformed. Results Evidence of an association between adversity and hsCRP was weak and inconsistent (e.g., 3+ versus no adversity: BIS: 12% higher, 95%CI -49.4, 147.8; LSAC 4.6% lower, 95%CI: −36.6, 48.3). A small positive association between adversity and GlycA levels was observed at both 4 years (e.g., 3+ versus no adversity: 3.3% higher, 95%CI -3.0, 9.9) and 11-12 years (3.2% higher, 95%CI 0.8, 5.8). In LSAC, we did not find evidence that inflammatory outcomes differed by initial timing of adversity exposure. Conclusions Small positive associations between adversity and inflammation were consistently observed for GlycA, across two cohorts with differing ages. Further work is needed to understand mechanisms, clinical relevance, and to identify opportunities for early intervention.Meredith O'Connor and Sarah Arnup were supported by the Melbourne Children's LifeCourse initiative, funded by a Royal Children's Hospital Foundation Grant (2018-984). Anne-Louise Ponsonby is supported by a National Health and Medical Research Council (NHMRC) Fellowship (1110200). Peter Sly is sup- ported by an NHMRC Fellowship (APP1102590). Naomi Priest is sup- ported by an NHMRC Career Development Fellowship (APP1123677). Kate Lycett is supported by an NHMRC Early Career Fellowship (APP1091124) and Honorary National Heart Foundation Postdoctoral Fellowship (101239). Sharon Goldfeld is supported by an NHMRC Career Development Fellowship (1082922). David Burgner is supported by an NHMRC Investigator Grant (1175744)