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- Vogel, G. F., et al.
(författare)
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Genotypic and phenotypic spectrum of infantile liver failure due to pathogenic TRMU variants
- 2023
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Ingår i: Genetics in Medicine. - : Elsevier BV. - 1098-3600. ; 25:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: This study aimed to define the genotypic and phenotypic spectrum of reversible acute liver failure (ALF) of infancy resulting from biallelic pathogenic TRMU variants and determine the role of cysteine supplementation in its treatment. Methods: Individuals with biallelic (likely) pathogenic variants in TRMU were studied within an international retrospective collection of de-identified patient data. Results: In 62 individuals, including 30 previously unreported cases, we described 47 (likely) pathogenic TRMU variants, of which 17 were novel, and 1 intragenic deletion. Of these 62 individuals, 42 were alive at a median age of 6.8 (0.6-22) years after a median follow-up of 3.6 (0.1-22) years. The most frequent finding, occurring in all but 2 individuals, was liver involvement. ALF occurred only in the first year of life and was reported in 43 of 62 individuals; 11 of whom received liver transplantation. Loss-of-function TRMU variants were associated with poor survival. Supplementation with at least 1 cysteine source, typically N-acetylcysteine, improved survival significantly. Neurodevelopmental delay was observed in 11 individuals and persisted in 4 of the survivors, but we were unable to determine whether this was a primary or a secondary consequence of TRMU deficiency. Conclusion: In most patients, TRMU-associated ALF was a transient, reversible disease and cysteine supplementation improved survival.
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| 2. |
- Hlozek, R., et al.
(författare)
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Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
- 2023
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Ingår i: Astrophysical Journal Supplement Series. - 0067-0049 .- 1538-4365. ; 267:2
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Tidskriftsartikel (refereegranskat)abstract
- Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
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