Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This overview will discuss the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property

AI3SD Video: Skills4Scientists - Poster & Careers Symposium - Poster Compilation

This video forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD students. This video is a compilation of posters presented at the Skills4Scientists Posters & Careers Symposium. These poster presentations are predominantly from summer students involved in the AI3SD 2021 summer internship program. Higher resolution versions of the posters are available on the poster symposium website: https://www.ai3sd.org/s4s-symposium20...Not all poster presenters requested a recording of their talk. The following posters recordings are included in this compilation video. Poster 1 - Nearer the nearsightedness principle: Large-scale quantum chemical calculations – Andras Vekassy (University of Southampton) Poster 3 - Combining Ultrasonic Methods and Machine Learning Techniques to Assess Baked Products Quality – Erhan Gulsen (University of Nottingham) Poster 4 - Interactive Knowledge-Based Solvent Selection Tool – Hewan Zewdu (University of Nottingham)Poster 5 - CV in High Throughput Chemistry – Jamie Longino (University of Strathclyde)Poster 9 - Dewetting in Thin Liquid Films: Using Sparse Optimization to Learn Evolution Equations – Aspen Fenzl (University of Sheffield)Poster 12 - Creating a merged dataset and its exploration with different Machine Learning algorithms – Maximilian Hoffman (Freie Universität of Berlin)Poster 14 - Bayesian optimisation in Chemistry – Rubaiyat Khondaker (University of Cambridge) Poster 15 - A deep neural network for generation of functional organic materials – Rhyan Barrett (University of Warwick) Thank you to our sponsors Optibrium (https://www.optibrium.com/) and Dotmatics (https://www.dotmatics.com/) who supported this event. These poster presentations were live cartooned by ErrantScience (errantscience.com) which is also available on our YouTube Channel. Sections Intro: (0:00) Andras Vekassy - Nearer the nearsighted principle: Large-scale quantum chemical calculations: (0:17) Erhan Gulsen - Combining Ultrasonic Methods and Machine Learning Techniques to Assess Baked Products Quality: (06:11) Hewan Zewdu - Interactive Knowledge-Based Solvent Selection Tool: (12:09) Jamie Longino - CV in High Throughput Chemistry: (16:52) Aspen Fenzl - Dewetting in Thin Liquid Films: Using Sparse Optimization to Learn Evolution Equations: (21:21) Maximillian Hoffman - Creating a merged dataset and its exploration with different machine learning algorithms: (27:31) Rubaiyat Khondaker - Bayesian optimisation in Chemistry: (34:33) Rhyan Barrett - A deep neural network for generation of functional organic materials: (40:06) Further details from this series can be found at: https://www.ai3sd.org/skills4scientists This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1 and PSDS (Physical Sciences Data science Service) which is funded by EPSRC under Grant Number EP/S020357/1

Crop Updates 1999 - Pulse Research and Industry Development in Western Australia

This session covers seventy three papers from different authors. CONTRIBUTORS BACKGROUND SUMMARY OF PREVIOUS RESULTS 1997 REGIONAL ROUNDUP Northern Wheatbelt, Bill O’Neill, Agriculture Western Australia Central Wheatbelt, Jeff Russell, Agriculture Western Australia Great Southern and Lakes, Neil Brandon, Agriculture Western Australia Esperance Mallee, Mark Seymour, Agriculture Western Australia PULSE BREEDING AND AGRONOMY Faba Bean Variety evaluation Germplasm evaluation Genotypic variation in waterlogging tolerance, Stephen Loss, Tim Colmer and Tim Pope University of WA Sowing rate Sowing rate demonstrations, Bill O’Neill, Agriculture Western Australia Swathing Effect of seed source on early vigour, Stephen Loss, and Tim Pope University of WA Phosphorus nutrition Phosphorus x zinc interactions Desi chichpea Breeding highlights, Tanveer Khan, Centre for Legumes in Mediterranean Agriculture Germplasm evaluation Variety testing Drought tolerance, Neil Turner, Laurent Leport, Bob French, Mike Barr, Christine Ludwig, Rebecca Kenny, Tanveer Khan, and K.H.M. Siddique, Centre for Legumes in Mediterranean Agriculture, Ashley Corbet and Ivan Mock, Agriculture Victoria, and Colin Edmonson, South Australian Research and Development Institute Remobilised carbon and nitrogen: Significance for seed size and yield, Stephen Davies, Neil Turner K.H.M. Siddique and Julie Plumber, Centre for legumes in Mediterranean Agriculture Molecular markers for cold tolerance and insect resistance Heather Clarke, Centre for legumes in Mediterranean Agriculture Time of sowing 22. Sowing rate 23. Sowing rate demonstrations, Bill O’Neill, Jason Brady Agriculture Western Australia Kabuli chickpea 24. Germplasm evaluation 25. Kabuli research in the Ord Irrigation Area, K.H.M. Siddique, Bob Dhackles and Joe Sherrard, Agriculture Western Australia 26. International screening for Ascochyta blight resistance, K.H.M. Siddique and Clive Francis, Centre for legumes in Mediterranean Agriculture, N. Acikgoz, AARI, Turkey, R.S. Malholtra, ICARDA, Syria, and E.J. Knights, NSW Ag 27. Sowing rate 28. Response to phosphorus Field pea 29. Breeding highlights, Tanveer Khan, Agriculture Western Australia 30. Crop variety testing 31. Variety comparison, Quentin Knight SBS IAMA 32. of sowing 33. Standing stubble demonstration, Neil Brandon and Bill O’Neill, Agriculture Western Australia 34. Intercropping canola improves the productivity of field pea, P. Soetedjo and Lionel Martin, Muresk Institute of Agriculture, K.H.M. Siddique, Stephen Loss, Neil Brandon and Bob French, Agriculture Western Australia 35. Peaola demonstration, Jeff Russell, Agriculture Western Australia Lentil 36. International germplasm evaluation, Jon Clements, K.H.M. Siddique and Clive Francis, Centre for legumes in Mediterranean Agriculture 37. Variety evaluation 38. rate Vetch 39. Germplasm evaluation 40. Sowing rate Narbon bean 41. Germplasm evaluation 42. Agronomy, Mark Seymour, Agriculture Western Australia 43. Herbicides, Mark Seymour, Agriculture Western Australia 44. Lathyrus development, Colin Hanbury, and K.H.M. Siddique, Centre for Legumes in Mediterranean Agriculture 45. Species comparison 46. Seed priming 47. Crop desiccation Glen Riethmuller, Stephen Loss and K.H.M. Siddique, Agriculture Western Australia 48. Gypsum Neil Brandon and Stephen Loss, Agriculture Western Australia 49. Antitranspirants 50. Rhizobial inoculant improvement John Howieson, Jane Malden and Ron Yates, Murdoch University 51. Water use in cropping systems David Hall and David Tennant, Agriculture Western Australia DISEASE AND PEST MANAGEMENT 52. Chocolate spot in faba beans, Bill MacLeod and Mark Sweetingham, Agriculture Western Australia 53. Chocolate spot management 54. Botrytis grey mould of chickpea, Bill MacLeod and Mark Sweetingham, Agriculture Western Australia 55. BGM management 56. Ascochyta in chickpea, Bill MacLeod and Mark Sweetingham, Agriculture Western Australia 57. Chickpea disease survey, Simon McKirdy, Sean Kelly, Sharon Collins and Domminie Wright, Agriculture Western Australia 58. Lentil diseases, Bill MacLeod and Mark Sweetingham, Agriculture Western Australia 59. Ascochyta blight 60. Ascochyta management 61. Botrytis grey mould management 62. Virus disease, Lindrea Latham, Centre for Legumes in Mediterranean Agriculture, Roger Jones, Agriculture Western Australia 63. Alfalfa mosaic virus 64. Alfalfa mosaic and cucumber mosaic virus in lentil 65. Virus survey of faba bean. Field pea and dwarf chickling crops 66. Screening chickpea and lentil for CMV and BTMV Insect pests 67. Red-legged earth mite, Anyou Liu, James Ridsdill-Smith, Tanveer Khan, K.H.M.Siddique,, Centre for Legumes in Mediterranean Agriculture 68. Aphids and their parasites, Owain Edwards, James Ridsdill-Smith, and Rick Horbury, CSIRO Entomology 69. Budworm resistance in chickpeas, Krishna Mann, James Ridsdill-Smith, Emilio Ghisalberti, and K. Silvasithamparam, Centre for Legumes in Mediterranean Agriculture 70. Native budworm management in pulses and canola, Kevin Walden, Agriculture Western Australia 71. PULSE ADOPTION Amir Abadi and Sally Marsh, University of Western Australia 72. Does risk keep farmers from growing pulses? 73. Best Rotations Daniel Fels, Agriculture Western Australia ACKNOWLEDGMENTS PUBLICATION

Time to Peak Glucose and Peak C-Peptide During the Progression to Type 1 Diabetes in the Diabetes Prevention Trial and TrialNet Cohorts

OBJECTIVE To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes. RESEARCH DESIGN AND METHODS We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed. RESULTS In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ2 = 25.76 vs. χ2 = 8.62; PTP: χ2 = 149.19 vs. χ2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time. CONCLUSIONS In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression