High efficacy and tolerability of hepatitis C genotype 2 treatment with direct-acting antivirals in real world: analysis of the Hepa-C Registry

Poster presentatio

Construction, assembly and tests of the ATLAS electromagnetic end-cap calorimeters

The construction and the assembly of the two end-caps of the ATLAS liquid argon electromagnetic calorimeter as well as their test and qualification programs are described. The work described here started at the beginning of 2001 and lasted for approximately three years. The results of the qualification tests performed before installation in the LHC ATLAS pit are given. The detectors are now installed in the ATLAS cavern, full of liquid argon and being commissioned. The complete detectors coverage is powered with high voltage and readout

Towards the biogeography of prokaryotic genes

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral

Correction to: SERENA: Particle Instrument Suite for Determining the Sun-Mercury Interaction from BepiColombo (Space Science Reviews, (2021), 217, 1, (11), 10.1007/s11214-020-00787-3)

The original online version of this article was revised because a number of authors had the wrong affiliation number next to their names. © 2021, Springer Nature B.V

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods