Automatic classification of takeaway food outlet cuisine type using machine (deep) learning

Background and purpose Neighbourhood exposure to takeaway (‘fast’-) food outlets selling different cuisines may be differentially associated with diet, obesity and related disease, and contributing to population health inequalities. However research studies have not disaggregated takeaways by cuisine type. This is partly due to the substantial resource challenge of de novo manual classification of unclassified takeaway outlets at scale. We describe the development of a new model to automatically classify takeaway food outlets, by 10 major cuisine types, based on business name alone. Material and methods We used machine (deep) learning, and specifically a Long Short Term Memory variant of a Recurrent Neural Network, to develop a predictive model trained on labelled outlets (n=14,145), from an online takeaway food ordering platform. We validated the accuracy of predictions on unseen labelled outlets (n=4000) from the same source. Results Although accuracy of prediction varied by cuisine type, overall the model (or ‘classifier’) made a correct prediction approximately three out of four times. We demonstrated the potential of the classifier to public health researchers and for surveillance to support decision-making, through using it to characterise nearly 55,000 takeaway food outlets in England by cuisine type, for the first time. Conclusions Although imperfect, we successfully developed a model to classify takeaway food outlets, by 10 major cuisine types, from business name alone, using innovative data science methods. We have made the model available for use elsewhere by others, including in other contexts and to characterise other types of food outlets, and for further development.This study is funded by the National Institute of Health Research (NIHR) School of Public Health Research (Grant Reference Number PD-SPH-2015). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the MRC Epidemiology Unit, University of Cambridge (Grant Reference Number MC/UU/00006/7). TBu is funded by the Centre for Diet and Activity Research (CEDAR), a UK Clinical Research Collaboration (UKCRC) Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute of Health Research, and the Wellcome Trust (Grant Reference Number MR/K023187/1), under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. These funders played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication

Cognition and Behaviour in Sotos Syndrome: A Systematic Review

BACKGROUND:Research investigating cognition and behaviour in Sotos syndrome has been sporadic and to date, there is no published overview of study findings. METHOD:A systematic review of all published literature (1964-2015) presenting empirical data on cognition and behaviour in Sotos syndrome. Thirty four journal articles met inclusion criteria. Within this literature, data relating to cognition and/or behaviour in 247 individuals with a diagnosis of Sotos syndrome were reported. Ten papers reported group data on cognition and/or behaviour. The remaining papers employed a case study design. RESULTS:Intelligence quotient (IQ) scores were reported in twenty five studies. Intellectual disability (IQ < 70) or borderline intellectual functioning (IQ 70-84) was present in the vast majority of individuals with Sotos syndrome. Seven studies reported performance on subscales of intelligence tests. Data from these studies indicate that verbal IQ scores are consistently higher than performance IQ scores. Fourteen papers provided data on behavioural features of individuals with Sotos syndrome. Key themes that emerged in the behavioural literature were overlap with ASD, ADHD, anxiety and high prevalence of aggression/tantrums. CONCLUSION:Although a range of studies have provided insight into cognition and behaviour in Sotos syndrome, specific profiles have not yet been fully specified. Recommendations for future research are provided

Germline selection shapes human mitochondrial DNA diversity.

Approximately 2.4% of the human mitochondrial DNA (mtDNA) genome exhibits common homoplasmic genetic variation. We analyzed 12,975 whole-genome sequences to show that 45.1% of individuals from 1526 mother-offspring pairs harbor a mixed population of mtDNA (heteroplasmy), but the propensity for maternal transmission differs across the mitochondrial genome. Over one generation, we observed selection both for and against variants in specific genomic regions; known variants were more likely to be transmitted than previously unknown variants. However, new heteroplasmies were more likely to match the nuclear genetic ancestry as opposed to the ancestry of the mitochondrial genome on which the mutations occurred, validating our findings in 40,325 individuals. Thus, human mtDNA at the population level is shaped by selective forces within the female germ line under nuclear genetic control, which ensures consistency between the two independent genetic lineages.NIHR, Wellcome Trust, MRC, Genomics Englan

Sotos syndrome and scoliosis surgical treatment: a 10-year follow-up

Sotos syndrome is caused by a gene deletion with an autosomal dominant pattern of inheritance. Cerebral gigantism, hypotonia and joint hyperextensibility are characteristic features of this syndrome. A percentage of these patients develop progressive scoliosis early in life. In the literature, few studies on the evolution of scoliosis in Sotos syndrome have been published. We retrospectively evaluated eight patients diagnosed with Sotos syndrome and scoliosis treated at the Garrahan Children Hospital between 1988 and March 2009. Clinical charts and imaging studies were assessed. Eight patients (19%) presented with scoliosis and seven of them (87.5%) required surgical treatment. The mean follow-up was 9.5 years (range 3–18). Mean age at first consultation was 5.2 years (range 1.1–11.2). Mean Cobb angle for scoliosis at first consultation was 34.3° (range 20°–42°) and the mean Cobb angle for kyphosis was 45.6° (range 30°–90°). Mean age at surgery was 11.2 years (range 3.7–18.10). The surgical procedures performed were instrumented posterior arthrodesis, alone or combined with anterior arthrodesis, instrumented anterior arthrodesis, while one patient is currently in treatment with growing rods. Preoperative mean Cobb angle for scoliosis was 72.3° (range 54°–130°) and for kyphosis was 59.8° (range 30°–108°); postoperative mean Cobb angle for scoliosis was 45.5° (range 6°–90°) and for kyphosis was 40.2° (range 30°–80°). There were three early complications (pleural effusion in two cases and death due to sepsis in one) and two late complications (kyphosis above the instrumentation area and dislodgement of the proximal hooks). Incidence of scoliosis in Sotos syndrome is high and thus close monitoring of patients with Sotos syndrome during growth is important for early detection of this entity. Joint hyperextensibility and hypotonia that are characteristic of the syndrome should be considered at the moment of surgery to avoid short fusions

A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles

ower train electrification is promoted as a potential alternative to reduce carbon intensity of transportation. Lithium-ion batteries are found to be suitable for hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), and temperature control on lithium batteries is vital for long-term performance and durability. Unfortunately, battery thermal management (BTM) has not been paid close attention partly due to poor understanding of battery thermal behaviour. Cell performance change dramatically with temperature, but it improves with temperature if a suitable operating temperature window is sustained. This paper provides a review on two aspects that are battery thermal model development and thermal management strategies. Thermal effects of lithium-ion batteries in terms of thermal runaway and response under cold temperatures will be studied, and heat generation methods are discussed with aim of performing accurate battery thermal analysis. In addition, current BTM strategies utilised by automotive suppliers will be reviewed to identify the imposing challenges and critical gaps between research and practice. Optimising existing BTMs and exploring new technologies to mitigate battery thermal impacts are required, and efforts in prioritising BTM should be made to improve the temperature uniformity across the battery pack, prolong battery lifespan, and enhance the safety of large packs