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    Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

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    © The Author 2017. In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets

    Unlocking value for a circular economy through 3D printing: a research agenda

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    The circular economy (CE) aims to radically improve resource efficiency by eliminating the concept of waste and leading to a shift away from the linear take-make-waste model. In a CE, resources are flowing in a circular manner either in a biocycle (biomass) or technocycle (inorganic materials). While early studies indicate that 3D printing (3DP) holds substantial promise for sustainability and the creation of a CE, there is no guarantee that it will do so. There is great uncertainty regarding whether the current trajectory of 3DP adoption is creating more circular material flows or if it is leading to an alternative scenario in which less eco-efficient localised production, demands for customised goods, and a higher rate of product obsolescence combine to bring about increased resource consumption. It is critical that CE principles are embedded into the new manufacturing system before the adoption of 3DP reaches a critical inflection point in which negative practices become entrenched. This paper, authored by both academic and industry experts, proposes a research agenda to determine enablers and barriers for 3DP to achieve a CE. We explore the two following overarching questions to discover what specific issues they entail: (1) How can a more distributed manufacturing system based on 3DP create a circular economy of closed-loop material flows? (2) What are the barriers to a circular 3D printing economy? We specifically examine six areas—design, supply chains, information flows, entrepreneurship, business models and education—with the aim of formulating a research agenda to enable 3DP to reach its full potential for a CE

    A continuum damage model for transverse cracking in UD composites of linear viscoelastic behaviour

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    A damage model for linear viscoelastic unidirectional (UD) composites undergoing transverse matrix cracking is proposed. The damage representation for the corresponding elastic UD composite with an array of dispersed matrix cracks was derived from Li's work based on continuum damage mechanics (CDM). The elastic-viscoelastic correspondence principle (CP) was used to obtain the damage representation for corresponding linear viscoelastic UD composites in the Laplace domain, and re-expressed the time domain by taking the inverse Laplace transformation. A damage evolution law was constructed using the Weibull distribution of defects which will develop into cracks as a result of deformation. The time-temperature superposition principle (TTSP) approach has been incorporated into this model. Applications of this damage model are described in detail, and the predictions are compared with experimental data

    Chinese student migration, social networking, and local engagement in the UK

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    The unprecedented growth in the number of Chinese international students since the twenty-first century raises questions about their links to and impact on local communities in host countries. Viewing Chinese students as an important part of diasporic Chinese community, this paper sheds new light on Chinese students’ social networking with different groups internally and externally, both Chinese and non-Chinese, on campus or in the wider community. Many questions arise: What is the contribution of Chinese students to the growth and transformation of diasporic Chinese communities in major destinations? What is the scope of their social networking and what are their functions in regard to Chinese community cohesion and integration? What are the differences between Chinese students and local residents and between Chinese students from mainland China and those from Hong Kong and Singapore in terms of network building and local engagement? The above questions are addressed by a combination of official data analysis and a questionnaire survey conducted in Nottingham. The evidence suggests a correlation between the local engagement of Chinese students in the wider community and their social networking, which offers a key to understand the new momentum for the transformation of diasporic Chinese community in major HE destinations. Theoretical and policy implications are discussed

    Hospital admission for hyperemesis gravidarum: a nationwide study of occurrence, reoccurrence and risk factors among 8.2 million pregnancies

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    STUDY QUESTION: What are the maternal risk factors for hyperemesis gravidarum (HG) hospital admission, readmission and reoccurrence in a following pregnancy?SUMMARY ANSWER: Young age, less socioeconomic deprivation, nulliparity, Asian or Black ethnicity, female fetus, multiple pregnancy, history of HG in a previous pregnancy, thyroid and parathyroid dysfunction, hypercholesterolemia and Type 1 diabetes are all risk factors for HG.WHAT IS KNOWN ALREADY: Women with Black or Asian ethnicity, of young age, carrying multiple babies or singleton females, with Type 1 diabetes or with a history of HG were previously reported to be at higher risk of developing HG; however, most evidence is from small studies. Little is known about associations with other comorbidities and there is controversy over other risk factors such as parity. Estimates of HG prevalence vary and there is a little understanding of the risks of HG readmission in a current pregnancy and reoccurrence rates in subsequent pregnancies, all of which are needed for planning measures to reduce onset or worsening of the condition.STUDY DESIGN, SIZE, DURATION: We performed a population-based cohort study of pregnancies ending in live births and stillbirths using prospectively recorded secondary care records (Hospital Episode Statistics) from England. We analysed those computerized and anonymized clinical records from over 5.3 million women who had one or more pregnancies between 1997 and 2012.PARTICIPANTS/MATERIALS, SETTING, METHODS: We obtained 8 215 538 pregnancies from 5 329 101 women of reproductive age, with a total of 186 800 HG admissions occurring during 121 885 pregnancies. Multivariate logistic regression with generalized estimating equations was employed to estimate odds ratios (aOR) to assess sociodemographic, pregnancy and comorbidity risk factors for HG onset, HG readmission within a pregnancy and reoccurrence in a subsequent pregnancy.MAIN RESULTS AND THE ROLE OF CHANCE: Being younger, from a less socioeconomically deprived status, of Asian or Black ethnicity, carrying a female fetus or having a multiple pregnancy all significantly increased HG and readmission risk but only ethnicity increased reoccurrence. Comorbidities most strongly associated with HG were parathyroid dysfunction (aOR = 3.83, 95% confidence interval 2.28–6.44), hypercholesterolemia (aOR = 2.54, 1.88–3.44), Type 1 diabetes (aOR = 1.95, 1.82–2.09), and thyroid dysfunction (aOR = 1.85, 1.74–1.96). History of HG was the strongest independent risk factor (aOR = 4.74, 4.46–5.05). Women with higher parity had a lower risk of HG compared with nulliparous women (aOR = 0.90, 0.89–0.91), which was not explained by women with HG curtailing further pregnancies.LIMITATIONS, REASONS FOR CAUTION: Although this represents the largest population-based study worldwide on the topic, the results could have been biased by residual and unmeasured confounding considering that some potential important risk factors such as smoking, BMI or prenatal care could not be measured with these data. Underestimation of non-routinely screened comorbidities such as hypercholesterolemia or thyroid dysfunction could also be a cause of selection bias.WIDER IMPLICATIONS OF THE FINDINGS: The estimated prevalence of 1.5% from our study was similar to the average prevalence reported in the literature and the representativeness of our data has been validated by comparison to national statistics. Also the prevalence of comorbidities was mostly similar to other studies estimating these in the UK and other developed countries. Women with Black or Asian ethnicity, of young age, carrying multiple babies or singleton females, with Type 1 diabetes or with history of HG were confirmed to be at higher risk of HG with an unprecedented higher statistical power. We showed for the first time that socioeconomic status interacts with maternal age, that hypercholesterolemia is a potential risk factor for HG and that carrying multiple females increases risk of hyperemesis compared with multiple males. We also provided robust evidence for the association of parity with HG. Earlier recognition and management of symptoms via gynaecology day-case units or general practitioner services can inform prevention and control of consequent hospital admissions.STUDY FUNDING/COMPETING INTEREST(S): The work was founded by The Rosetrees Trust and the Stoneygate Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. C.N.-P. reports personal fees from Sanofi Aventis, Warner Chilcott, Leo Pharma, UCB and Falk, outside the submitted work and she is one of the co-developers of the RCOG Green Top Guideline on HG; all other authors did not report any potential conflicts of interest

    Neural substrates of early executive function development

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    In the last decade, advances in neuroimaging technologies have given rise to a large number of research studies that investigate the neural underpinnings of executive function (EF). EF has long been associated with the prefrontal cortex (PFC) and involves both a unified, general element, as well as the distinct, separable elements of working memory, inhibitory control and set shifting. We will highlight the value of utilising advances in neuroimaging techniques to uncover answers to some of the most pressing questions in the field of early EF development. First, this review will explore the development and neural substrates of each element of EF. Second, the structural, anatomical and biochemical changes that occur in the PFC during infancy and throughout childhood will be examined, in order to address the importance of these changes for the development of EF. Third, the importance of connectivity between regions of the PFC and other brain areas in EF development is reviewed. Finally, throughout this review more recent developments in neuroimaging techniques will be addressed, alongside the implications for further elucidating the neural substrates of early EF development in the future

    Injectable and porous PLGA microspheres that form highly porous scaffolds at body temperature

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    Injectable scaffolds are of interest in the field of regenerative medicine because of their minimally invasive mode of delivery. For tissue repair applications, it is essential that such scaffolds have the mechanical properties, porosity and pore diameter to support the formation of new tissue. In the current study, porous poly(DL-lactic acid-co-glycolic acid) (PLGA) microspheres were fabricated with an average size of 84 ± 24 lm for use as injectable cell carriers. Treatment with ethanolic sodium hydroxide for 2 min was observed to increase surface porosity without causing the microsphere structure to disintegrate. This surface treatment also enabled the microspheres to fuse together at 37 C to form scaffold structures. The average compressive strength of the scaffolds after 24 h at 37 C was 0.9 ± 0.1 MPa, and the average Young’s modulus was 9.4 ± 1.2 MPa. Scaffold porosity levels were 81.6% on average, with a mean pore diameter of 54 ± 38 lm. This study demonstrates a method for fabricating porous PLGA microspheres that form solid porous scaffolds at body temperature, creating an injectable system capable of supporting NIH-3T3 cell attachment and proliferation in vitro

    Sample size and number of outcome measures of veterinary randomised controlled trials of pharmaceutical interventions funded by different sources, a cross-sectional study

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    BackgroundRandomised controlled trials (RCTs) are a key component of the veterinary evidence base. Sample sizes and defined outcome measures are crucial components of RCTs.To describe the sample size and number of outcome measures of veterinary RCTs either funded by the pharmaceutical industry or not, published in 2011.MethodsA structured search of PubMed identified RCTs examining the efficacy of pharmaceutical interventions. Number of outcome measures, number of animals enrolled per trial, whether a primary outcome was identified, and the presence of a sample size calculation were extracted from the RCTs. The source of funding was identified for each trial and groups compared on the above parameters.ResultsLiterature searches returned 972 papers; 86 papers comprising 126 individual trials were analysed. The median number of outcomes per trial was 5.0; there were no significant differences across funding groups (p = 0.133). The median number of animals enrolled per trial was 30.0; this was similar across funding groups (p = 0.302). A primary outcome was identified in 40.5% of trials and was significantly more likely to be stated in trials funded by a pharmaceutical company. A very low percentage of trials reported a sample size calculation (14.3%).ConclusionsFailure to report primary outcomes, justify sample sizes and the reporting of multiple outcome measures was a common feature in all of the clinical trials examined in this study. It is possible some of these factors may be affected by the source of funding of the studies, but the influence of funding needs to be explored with a larger number of trials. Some veterinary RCTs provide a weak evidence base and targeted strategies are required to improve the quality of veterinary RCTs to ensure there is reliable evidence on which to base clinical decisions

    Translating advances in the molecular basis of schizophrenia into novel cognitive treatment strategies

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    The presence and severity of cognitive symptoms, including working memory, executive dysfunction and attentional impairment, contributes materially to functional impairment in schizophrenia. Cognitive symptoms have proven resistant to both first- and second-generation antipsychotic drugs. Efforts to develop a consensus set of cognitive domains that are both disrupted in schizophrenia and are amenable to cross-species validation (e.g. the NIMH CNTRICS and RDoC initiatives) are an important step towards standardisation of outcome measures that can used in preclinical testing of new drugs. While causative genetic mutations have not been identified, new technologies have identified novel genes as well as hitherto candidate genes previously implicated in the pathophysiology of schizophrenia and/or mechanisms of antipsychotic efficacy. This review comprises a selective summary of these developments, particularly phenotypic data arising from preclinical genetic models for cognitive dysfunction in schizophrenia, with the aim of indicating potential new directions for pro-cognitive therapeutics
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