21 research outputs found

    Systematic Y2H screening reveals extensive effector-complex formation

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    During infection pathogens secrete small molecules, termed effectors, to manipulate and control the interaction with their specific hosts. Both the pathogen and the plant are under high selective pressure to rapidly adapt and co-evolve in what is usually referred to as molecular arms race. Components of the host’s immune system form a network that processes information about molecules with a foreign origin and damage-associated signals, integrating them with developmental and abiotic cues to adapt the plant’s responses. Both in the case of nucleotide-binding leucine-rich repeat receptors and leucine-rich repeat receptor kinases interaction networks have been extensively characterized. However, little is known on whether pathogenic effectors form complexes to overcome plant immunity and promote disease. Ustilago maydis, a biotrophic fungal pathogen that infects maize plants, produces effectors that target hubs in the immune network of the host cell. Here we assess the capability of U. maydis effector candidates to interact with each other, which may play a crucial role during the infection process. Using a systematic yeast-two-hybrid approach and based on a preliminary pooled screen, we selected 63 putative effectors for one-on-one matings with a library of nearly 300 effector candidates. We found that 126 of these effector candidates interacted either with themselves or other predicted effectors. Although the functional relevance of the observed interactions remains elusive, we propose that the observed abundance in complex formation between effectors adds an additional level of complexity to effector research and should be taken into consideration when studying effector evolution and function. Based on this fundamental finding, we suggest various scenarios which could evolutionarily drive the formation and stabilization of an effector interactome

    National audit of pathways in epileptic seizure referrals (NAPIER) : a national, multicentre audit of first seizure clinics throughout the UK and Ireland

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    Acknowledgements We would like to thank the following collaborators of the NANSIG Collaborative, who conducted local data collection and analysis: Ajitesh Anand, Alena Abraham, Alex Irving, Amogh Prabhakar, Catinca Ciuculete, Cindy Zheng, Daniel King, Declan Browne, Dipesh Kumar Barua, Dorota Duklas, Farhat Mirza, Fumilola Olaifa, Harmani Daler, Hassan Naveed, Heba Elzeky, Hedley Emsley, Honglin Zhu, Ian Morrison, Irtiza Syed, Isabel Summers, Jack Wellington, Jasmine Wall, John O'Dwyer, Jordan Ford, Karthikeyan Sivaganesh, Katja Lassak, Keara Jamison, Khalid Hamandi, Kourosh Parvi, Lareyna McMenemy, Lewis McColm, Lina Aleknaite, Maithili Srikantha, Maja Kaladjiska, Marie Jasim, Mark McCarron, Martina Mockova, Mohammad Marar, Naghme Adab, Najma Ahmed, Nye Rhys Potter, Pavithira Tharmapoopathy, Prithvi Dixit, Rajiv Mohanraj, Ravanth Baskaran, Richard Davenport, Robert Seah, Rohan Bhate, Rohan Gupta, Sahar Shams, Siddarth Kannan, Tahir Majeed, Timothy Counihan, Tomas Ferriera, Yihui Cheng, Zaib ShamshiPeer reviewedPostprin

    Ocean remote sensing techniques and applications: a review (Part II)

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    As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

    Hosting an Educational Careers Day Within the Virtual Paradigm: The Neurology and Neurosurgery Interest Group Experience.

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    INTRODUCTION:  To explore our experience of hosting the 10th Annual Neurology and Neurosurgery Interest Group-Society of British Neurological Surgeons (NANSIG-SBNS) Neurosurgery Careers Day, held virtually for the first time. METHODS:  Reflective feedback and review of an international, virtual neurosurgery careers day. The authors reflect on the logistics of organizing the event, and the pre- and post-event feedback provided by delegates. Recommendations have been made on how to successfully host a virtual event. The key themes that permeated the event have been outlined and discussed in the context of the feedback received. RESULTS:  The event was attended by 231 delegates from 20 countries worldwide. Knowledge of neurosurgery as a career and the application process increased after attending the careers day (4.27/5 to 4.51/5, p=0.003 and 3.12/5 to 4.31/5, p<0.001 respectively). The key themes identified from the event include attendance, networking, and education. Qualitative feedback was positive and indicated a positive perception of the careers day. CONCLUSIONS:  The future of educational events is unclear, and a hybrid approach is recommended to retain the benefits of the online space when in-person events eventually return

    LEARN: A multi-centre, cross-sectional evaluation of Urology teaching in UK medical schools

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    OBJECTIVE: To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. MATERIALS AND METHODS: LEARN was a national multicentre cross-sectional study. Year 2 to Year 5 medical students and FY1 doctors were invited to complete a survey between 3rd October and 20th December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS: 7,063/8,346 (84.6%) responses from all 39 UK medical schools were included; 1,127/7,063 (16.0%) were from Foundation Year (FY) 1 doctors, who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and clinical placements in the later years of medical school. 20.1% of FY1 doctors reported no undergraduate clinical attachment in urology. CONCLUSION: LEARN is the largest ever evaluation of undergraduate urology teaching. In the UK, teaching seemed satisfactory as evaluated by the BAUS undergraduate syllabus. However, many students report having no clinical attachments in Urology and some newly qualified doctors report never having inserted a catheter, which is a GMC mandated requirement. We recommend a greater emphasis on undergraduate clinical exposure to urology and stricter adherence to GMC mandated procedures

    Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

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    Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

    The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

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    Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

    Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings

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    Due to its advantages and the continual availability of solar energy, photovoltaic (PV) systems have become the most popular energy production equipment in various business and residential structures. This chapter proposes solar radiation forecasting to manage solar power generation in residential and commercial buildings using deep learning algorithms. Convolutional neural network (CNN) and long short-term memory (LSTM) are two proposed algorithms created to forecast solar radiation to control the energy produced at a PV plant on the roof of the University of Macau in China. Climate data collected at the university’s meteorological station are used as input variables in solar radiation forecasting. The performance of each network is assessed using a variety of performance evaluation measures. Based on the results and analysis, the LSTM technique, which forecasts solar radiation with an accuracy of R = 99.84%, outperforms the CNN technique that predicts solar radiation with an accuracy of R = 99.71%. Furthermore, the LSTM technique’s predictions exhibit a lower forecasting error than the CNN process.</p
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