Increasing the proportion of healthier foods available with and without reducing portion sizes and energy purchased in worksite cafeterias:protocol for a stepped-wedge randomised controlled trial

BACKGROUND: Overconsumption of energy from food contributes to high rates of overweight and obesity in many populations. A promising set of interventions tested in pilot studies in worksite cafeterias, suggests energy intake may be reduced by increasing the proportion of healthier - i.e. lower energy - food options available, and decreasing portion sizes. The current study aims to assess the impact on energy purchased of i. increasing the proportion of lower energy options available; ii. combining this with reducing portion sizes, in a full trial. METHODS: A stepped-wedge randomised controlled trial in 19 worksite cafeterias, where the proportion of lower energy options available in targeted food categories (including main meals, snacks, and cold drinks) will be increased; and combined with reduced portion sizes. The primary outcome is total energy (kcal) purchased from targeted food categories using a pooled estimate across all sites. Follow-up analyses will test whether the impact on energy purchased varies according to the extent of intervention implementation. DISCUSSION: This study will provide the most reliable estimate to date of the effect sizes of two promising interventions for reducing energy purchased in worksite cafeterias. TRIAL REGISTRATION: The study was prospectively registered on ISRCTN (date: 24.05.19; TRN: ISRCTN87225572; doi: https://doi.org/10.1186/ISRCTN87225572)

Defining principles for mobile apps and platforms development in citizen science

Apps for mobile devices and web-based platforms are increasingly used in citizen science projects. While extensive research has been done in multiple areas of studies, from Human-Computer Interaction to public engagement in science, we are not aware of a collection of recommendations specific for citizen science that provides support and advice for planning, design and data management of mobile apps and platforms that will assist learning from best practice and successful implementations. In two workshops, citizen science practitioners with experience in mobile application and web-platform development and implementation came together to analyse, discuss and define recommendations for the initiators of technology based citizen science projects. Many of the recommendations produced during the two workshops are applicable to citizen science project that do not use mobile devices to collect data. Therefore, we propose to closely connect the results presented here with ECSA’s Ten Principles of Citizen Science

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. NEW METHOD: A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM. RESULTS: Sleep states were classified with an accuracy of 84% and Cohen\u27s κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered. COMPARISON WITH EXISTING METHOD: On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI

Local and distributed PiB accumulation associated with development of preclinical Alzheimer\u27s disease

Amyloid-beta plaques are a hallmark of Alzheimer\u27s disease (AD) that can be assessed by amyloid imaging (e.g., Pittsburgh B compound [PiB]) and summarized as a scalar value. Summary values may have clinical utility but are an average over many regions of interest, potentially obscuring important topography. This study investigates the longitudinal evolution of amyloid topographies in cognitively normal older adults who had normal (N = 131) or abnormal (N = 26) PiB scans at baseline. At 3 years follow-up, 16 participants with a previously normal PiB scan had conversion to PiB scans consistent with preclinical AD. We investigated the multivariate relationship (canonical correlation) between baseline and follow-up PiB topographies. Furthermore, we used penalized regression to investigate the added information derived from PiB topography compared to summary measures. PiB accumulation can be local, that is, a topography predicting the same topography in the future, and/or distributed, that is, one topography predicting another. Both local and distributed PiB accumulation was associated with conversion of PiB status. Additionally, elements of the multivariate topography, and not the commonly used summary scalar, correlated with future PiB changes. Consideration of the entire multivariate PiB topography provides additional information regarding the development of amyloid-beta pathology in very early preclinical AD

A Formal Library for Elliptic Curves in the Coq Proof Assistant

International audienceA preliminary step towards the verification of elliptic curve cryptographic algorithms is the development of formal libraries with the corresponding mathematical theory. In this paper we present a formaliza-tion of elliptic curves theory, in the SSReflect extension of the Coq proof assistant. Our central contribution is a library containing many of the objects and core properties related to elliptic curve theory. We demonstrate the applicability of our library by formally proving a non-trivial property of elliptic curves: the existence of an isomorphism between a curve and its Picard group of divisors

The related-key analysis of feistel constructions

Lecture Notes in Computer Science, Volume 8540, 2015.It is well known that the classical three- and four-round Feistel constructions are provably secure under chosen-plaintext and chosen-ciphertext attacks, respectively. However, irrespective of the number of rounds, no Feistel construction can resist related-key attacks where the keys can be offset by a constant. In this paper we show that, under suitable reuse of round keys, security under related-key attacks can be provably attained. Our modification is substantially simpler and more efficient than alternatives obtained using generic transforms, namely the PRG transform of Bellare and Cash (CRYPTO 2010) and its random-oracle analogue outlined by Lucks (FSE 2004). Additionally we formalize Luck’s transform and show that it does not always work if related keys are derived in an oracle-dependent way, and then prove it sound under appropriate restrictions

Matching Catalogues by Probabilistic Pattern Classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HIPASS radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.Comment: 16 pages, 9 (reduced quality) figures. MNRAS (in press) 200

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance