ORCID Profile
0000-0001-9841-1518
Current Organisations
Australian National University
,
CSIRO Black Mountain Laboratories
,
University of Oxford
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Publisher: Elsevier BV
Date: 03-2019
Publisher: Elsevier BV
Date: 08-2020
Publisher: Springer Science and Business Media LLC
Date: 29-01-2019
DOI: 10.1038/S41398-019-0396-7
Abstract: Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 ( LPHN3 ) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal s les from disparate regions of the world ( n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large s le of in iduals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD.
Publisher: Springer Science and Business Media LLC
Date: 11-01-2022
Publisher: Cold Spring Harbor Laboratory
Date: 16-07-2019
DOI: 10.1101/704080
Abstract: The observation of in iduals attaining remarkable ages, and their concentration into geographic sub-regions or ‘blue zones’, has generated considerable scientific interest. Proposed drivers of remarkable longevity include high vegetable intake, strong social connections, and genetic markers. Here, we reveal new predictors of remarkable longevity and ‘supercentenarian’ status. In the United States supercentenarian status is predicted by the absence of vital registration. In the UK, Italy, Japan, and France remarkable longevity is instead predicted by regional poverty, old-age poverty, material deprivation, low incomes, high crime rates, a remote region of birth, worse health, and fewer 90+ year old people. In addition, supercentenarian birthdates are concentrated on the first of the month and days isible by five: patterns indicative of widespread fraud and error. As such, relative poverty and missing vital documents constitute unexpected predictors of centenarian and supercentenarian status, and support a primary role of fraud and error in generating remarkable human age records.
Publisher: Springer Science and Business Media LLC
Date: 19-10-2021
DOI: 10.1186/S13007-021-00806-6
Abstract: The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Publisher: Elsevier BV
Date: 2018
Publisher: F1000 Research Ltd
Date: 11-05-2016
DOI: 10.12688/F1000RESEARCH.8713.1
Abstract: Kin and group interactions are important determinants of reproductive success in many species. Their optimization could, therefore, potentially improve the productivity and breeding success of managed populations used for agricultural and conservation purposes. Here we demonstrate this potential using a novel approach to measure and predict the effect of kin and group dynamics on reproductive output in a well-known species, the meerkat Suricata suricatta . Variation in social dynamics predicts 30% of the in idual variation in reproductive success of this species in managed populations, and accurately forecasts reproductive output at least two years into the future. Optimization of social dynamics in captive meerkat populations doubles their projected reproductive output. These results demonstrate the utility of a quantitative approach to breeding programs informed by social and kinship dynamics. They suggest that this approach has great potential for improvements in the management of social endangered and agricultural species.
Publisher: Cold Spring Harbor Laboratory
Date: 05-04-2017
DOI: 10.1101/124800
Abstract: We respond to claims by Dong et al . that human lifespan is limited below 125 years. Using the log-linear increase in mortality rates with age to predict the upper limits of human survival we find, in contrast to Dong et al ., that the limit to human lifespan is historically flexible and increasing. This discrepancy can be explained by Dong et al.’s use of data with variable s le sizes, age-biased rounding errors, and log(0) instead of log(1) values in linear regressions. Addressing these issues eliminates the proposed 125-year upper limit to human lifespan.
Publisher: Springer Science and Business Media LLC
Date: 04-10-2021
Publisher: Springer Science and Business Media LLC
Date: 23-04-2021
DOI: 10.1038/S41597-021-00898-8
Abstract: A critical shortage of ‘big’ agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial ersity in vegetation and environmental patterns across the continent-spanning NVT network.
Publisher: Public Library of Science (PLoS)
Date: 21-01-2015
Publisher: figshare
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 22-07-2022
Publisher: Cold Spring Harbor Laboratory
Date: 09-03-2021
DOI: 10.1101/2021.03.08.434495
Abstract: Four species of grass generate half of all human-consumed calories 1 . However, abundant biological data on species that produce our food remains largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we assemble and analyse a continent-wide database of field experiments spanning ten years and hundreds of thousands of machine-phenotyped populations of ten major crop species. Training an ensemble of machine learning models, using thousands of variables capturing weather, ground-sensor, soil, chemical and fertiliser dosage, management, and satellite data, produces robust cross-continent yield models exceeding R 2 = 0.8 prediction accuracy. In contrast to ‘black box’ analytics, detailed interrogation of these models reveals fundamental drivers of crop behaviour and complex interactions predicting yield and agronomic traits. These results demonstrate the capacity of machine learning models to build unified, interpretable, and explainable models of crop behaviour, and highlight the powerful role of data in the future of food.
Publisher: Cold Spring Harbor Laboratory
Date: 05-04-2017
DOI: 10.1101/124792
Publisher: Public Library of Science (PLoS)
Date: 20-12-2018
Publisher: F1000 Research Ltd
Date: 09-06-2017
DOI: 10.12688/F1000RESEARCH.11438.2
Abstract: We respond to claims by Dong et al . that human lifespan is limited below 125 years. Using the log-linear increase in mortality rates with age to predict the upper limits of human survival we find, in contrast to Dong et al ., that the limit to human lifespan is historically flexible and increasing. This discrepancy can be explained by Dong et al .’s use of data with variable s le sizes, age-biased rounding errors, and log(0) instead of log(1) values in linear regressions. Addressing these issues eliminates the proposed 125-year upper limit to human lifespan.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 19-05-2023
Abstract: Athleticism and the mortality rates begin a lifelong trajectory of decline during early adulthood. Because of the substantial follow-up time required, however, observing any longitudinal link between early-life physical declines and late-life mortality and aging remains largely inaccessible. Here, we use longitudinal data on elite athletes to reveal how early-life athletic performance predicts late-life mortality and aging in healthy male populations. Using data on over 10,000 baseball and basketball players, we calculate age at peak athleticism and rates of decline in athletic performance to predict late-life mortality patterns. Predictive capacity of these variables persists for decades after retirement, displays large effect sizes, and is independent of birth month, cohort, body mass index, and height. Furthermore, a nonparametric cohort-matching approach suggests that these mortality rate differences are associated with differential aging rates, not just extrinsic mortality. These results highlight the capacity of athletic data to predict late-life mortality, even across periods of substantial social and medical change.
Publisher: F1000 Research Ltd
Date: 26-04-2017
DOI: 10.12688/F1000RESEARCH.11438.1
Abstract: We respond to claims by Dong et al . that human lifespan is limited below 125 years. Using the log-linear increase in mortality rates with age to predict the upper limits of human survival we find, in contrast to Dong et al ., that the limit to human lifespan is historically flexible and increasing. This discrepancy can be explained by Dong et al .’s use of data with variable s le sizes, age-biased rounding errors, and log(0) instead of log(1) values in linear regressions. Addressing these issues eliminates the proposed 125-year upper limit to human lifespan.
Publisher: Public Library of Science (PLoS)
Date: 20-12-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Saul Newman.