Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data.

Neurodevelopmental maturation takes place in a social environment in addition to a neurobiological one. Characterization of social environmental factors that influence this process is therefore an essential component in developing an accurate model of adolescent brain and neurocognitive development, as well as susceptibility to change with the use of marijuana and other drugs. The creation of the Culture and Environment (CE) measurement component of the ABCD protocol was guided by this understanding. Three areas were identified by the CE Work Group as central to this process: influences relating to CE Group membership, influences created by the proximal social environment, influences stemming from social interactions. Eleven measures assess these influences, and by time of publication, will have been administered to well over 7,000 9-10 year-old children and one of their parents. Our report presents baseline data on psychometric characteristics (mean, standard deviation, range, skewness, coefficient alpha) of all measures within the battery. Effectiveness of the battery in differentiating 9-10 year olds who were classified as at higher and lower risk for marijuana use in adolescence was also evaluated. Psychometric characteristics on all measures were good to excellent; higher vs. lower risk contrasts were significant in areas where risk differentiation would be anticipated

Do Patients Want to Die at Home? A Systematic Review of the UK Literature, Focused on Missing Preferences for Place of Death.

BACKGROUND: End-of-life care policy has a focus on enabling patients to die in their preferred place; this is believed for most to be home. This review assesses patient preferences for place of death examining: the extent of unreported preferences, the importance of patient factors (place of care and health diagnosis) and who reports preferences. METHODS AND FINDINGS: Systematic literature review of 7 electronic databases, grey literature, backwards citations from included studies and Palliative Medicine hand search. Included studies published between 2000-2015, reporting original, quantifiable results of adult UK preferences for place of death. Of 10826 articles reviewed, 61 met the inclusion criteria. Summary charts present preferences for place of death by health diagnosis, where patients were asked and who reported the preference. These charts are recalculated to include 'missing data,' the views of those whose preferences were not asked, expressed or reported or absent in studies. Missing data were common. Across all health conditions when missing data were excluded the majority preference was for home: when missing data were included, it was not known what proportion of patients with cancer, non-cancer or multiple conditions preferred home. Patients, family proxies and public all expressed a majority preference for home when missing data were excluded: when included, it was not known what proportion of patients or family proxies preferred home. Where patients wished to die was related to where they were asked their preference. Missing data calculations are limited to 'reported' data. CONCLUSIONS: It is unknown what proportion of patients prefers to die at home or elsewhere. Reported preferences for place of death often exclude the views of those with no preference or not asked: when 'missing data' are included, they supress the proportion of preferences for all locations. Caution should be exercised if asserting that most patients prefer to die at home.This is the final version of the article. It was first available from PLOS via http://dx.doi.org/10.1371/journal.pone.014272

Planning a method for covariate adjustment in individually randomised trials: a practical guide

Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. // Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. // Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually transmitted infection testing and results service. // Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely

Congruences modulo prime powers of Hecke eigenvalues in level 11

We continue the study of strong, weak, and $dc$-weak eigenforms introduced by Chen, Kiming, and Wiese. We completely determine all systems of Hecke eigenvalues of level $1$ modulo $128$, showing there are finitely many. This extends results of Hatada and can be considered as evidence for the more general conjecture formulated by the author together with Kiming and Wiese on finiteness of systems of Hecke eigenvalues modulo prime powers at any fixed level. We also discuss the finiteness of systems of Hecke eigenvalues of level $1$ modulo $9$, reducing the question to the finiteness of a single eigenvalue. Furthermore, we answer the question of comparing weak and $dc$-weak eigenforms and provide the first known examples of non-weak $dc$-weak eigenforms.Comment: 28 pages; Minor revisio

Linkage Disequilibrium Mapping via Cladistic Analysis of Single-Nucleotide Polymorphism Haplotypes

We present a novel approach to disease-gene mapping via cladistic analysis of single-nucleotide polymorphism (SNP) haplotypes obtained from large-scale, population-based association studies, applicable to whole-genome screens, candidate-gene studies, or fine-scale mapping. Clades of haplotypes are tested for association with disease, exploiting the expected similarity of chromosomes with recent shared ancestry in the region flanking the disease gene. The method is developed in a logistic-regression framework and can easily incorporate covariates such as environmental risk factors or additional unlinked loci to allow for population structure. To evaluate the power of this approach to detect disease-marker association, we have developed a simulation algorithm to generate high-density SNP data with short-range linkage disequilibrium based on empirical patterns of haplotype diversity. The results of the simulation study highlight substantial gains in power over single-locus tests for a wide range of disease models, despite overcorrection for multiple testing

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected

Characteristics of patients with missing information on stage: a population-based study of patients diagnosed with colon, lung or breast cancer in England in 2013.

BACKGROUND: Stage is a key predictor of cancer survival. Complete cancer staging is vital for understanding outcomes at population level and monitoring the efficacy of early diagnosis initiatives. Cancer registries usually collect details of the disease extent but staging information may be missing because a stage was never assigned to a patient or because it was not included in cancer registration records. Missing stage information introduce methodological difficulties for analysis and interpretation of results. We describe the associations between missing stage and socio-demographic and clinical characteristics of patients diagnosed with colon, lung or breast cancer in England in 2013. We assess how these associations change when completeness is high, and administrative issues are assumed to be minimal. We estimate the amount of avoidable missing stage data if high levels of completeness reached by some Clinical Commissioning Groups (CCGs), were achieved nationally. METHODS: Individual cancer records were retrieved from the National Cancer Registration and linked to the Routes to Diagnosis and Hospital Episode Statistics datasets to obtain additional clinical information. We used multivariable beta binomial regression models to estimate the strength of the association between socio-demographic and clinical characteristics of patients and missing stage and to derive the amount of avoidable missing stage. RESULTS: Multivariable modelling showed that old age was associated with missing stage irrespective of the cancer site and independent of comorbidity score, short-term mortality and patient characteristics. This remained true for patients in the CCGs with high completeness. Applying the results from these CCGs to the whole cohort showed that approximately 70% of missing stage information was potentially avoidable. CONCLUSIONS: Missing stage was more frequent in older patients, including those residing in CCGs with high completeness. This disadvantage for older patients was not explained fully by the presence of comorbidity. A substantial gain in completeness could have been achieved if administrative practices were improved to the level of the highest performing areas. Reasons for missing stage information should be carefully assessed before any study, and potential distortions introduced by how missing stage is handled should be considered in order to draw the most correct inference from available statistics