| 1. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
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| 4. |
- Adrianto, Indra, et al.
(författare)
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Association of a functional variant downstream of TNFAIP3 with systemic lupus erythematosus
- 2011
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 43:3, s. 253-258
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Tidskriftsartikel (refereegranskat)abstract
- Systemic lupus erythematosus (SLE, MIM152700) is an autoimmune disease characterized by self-reactive antibodies resulting in systemic inflammation and organ failure. TNFAIP3, encoding the ubiquitin-modifying enzyme A20, is an established susceptibility locus for SLE. By fine mapping and genomic re-sequencing in ethnically diverse populations, we fully characterized the TNFAIP3 risk haplotype and identified a TT>A polymorphic dinucleotide (deletion T followed by a T to A transversion) associated with SLE in subjects of European (P = 1.58 x 10(-8), odds ratio = 1.70) and Korean (P = 8.33 x 10(-10), odds ratio = 2.54) ancestry. This variant, located in a region of high conservation and regulatory potential, bound a nuclear protein complex composed of NF-kappa B subunits with reduced avidity. Further, compared with the non-risk haplotype, the haplotype carrying this variant resulted in reduced TNFAIP3 mRNA and A20 protein expression. These results establish this TT>A variant as the most likely functional polymorphism responsible for the association between TNFAIP3 and SLE.
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| 5. |
- Dance, Sarah L., et al.
(författare)
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Improvements in Forecasting Intense Rainfall : Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
- 2019
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Ingår i: Atmosphere. - : MDPI. - 2073-4433. ; 10:3
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Forskningsöversikt (refereegranskat)abstract
- The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project's achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.
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| 6. |
- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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| 9. |
- Fine, Samson W., et al.
(författare)
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A Contemporary Update on Pathology Reporting for Prostate Cancer: Biopsy and Radical Prostatectomy Specimens
- 2012
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Ingår i: European Urology. - : Elsevier BV. - 1873-7560 .- 0302-2838. ; 62:1, s. 20-39
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Forskningsöversikt (refereegranskat)abstract
- Context: The diagnosis of and reporting parameters for prostate cancer (PCa) have evolved over time, yet they remain key components in predicting clinical outcomes. Objective: Update pathology reporting standards for PCa. Evidence acquisition: A thorough literature review was performed for articles discussing PCa handling, grading, staging, and reporting published as of September 15, 2011. Electronic articles published ahead of print were also considered. Proceedings of recent international conferences addressing these areas were extensively reviewed. Evidence synthesis: Two main areas of reporting were examined: (1) prostatic needle biopsy, including handling, contemporary Gleason grading, extent of involvement, and high-risk lesions/precursors and (2) radical prostatectomy (RP), including sectioning, multifocality, Gleason grading, staging of organ-confined and extraprostatic disease, lymph node involvement, tumor volume, and lymphovascular invasion. For each category, consensus views, controversial areas, and clinical import were reviewed. Conclusions: Modern prostate needle biopsy and RP reports are extremely detailed so as to maximize clinical utility. Accurate diagnosis of cancer-specific features requires up-to-date knowledge of grading, quantitation, and staging criteria. While some areas remain controversial, efforts to codify existing knowledge have had a significant impact on pathology practice. (C) 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.
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| 10. |
- Walsh, John A., et al.
(författare)
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Digital humanities in the iSchool
- 2022
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Ingår i: Journal of the Association for Information Science and Technology. - : John Wiley & Sons. - 2330-1635 .- 2330-1643. ; 73:25, s. 188-203
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Tidskriftsartikel (refereegranskat)abstract
- The interdisciplinary field known as digital humanities (DH) is represented in various forms in the teaching and research practiced in iSchools. Building on the work of an iSchools organization committee charged with exploring digital humanities curricula, we present findings from a series of related studies exploring aspects of DH teaching, education, and research in iSchools, often in collaboration with other units and disciplines. Through a survey of iSchool programs and an online DH course registry, we investigate the various education models for DH training found in iSchools, followed by a detailed look at DH courses and curricula, explored through analysis of course syllabi and course descriptions. We take a brief look at collaborative disciplines with which iSchools cooperate on DH research projects or in offering DH education. Next, we explore DH careers through an analysis of relevant job advertisements. Finally, we offer some observations about the management and administrative challenges and opportunities related to offering a new iSchool DH program. Our results provide a snapshot of the current state of digital humanities in iSchools which may usefully inform the design and evolution of new DH programs, degrees, and related initiatives.
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