Correction: NGS read classification using AI.

[This corrects the article DOI: 10.1371/journal.pone.

Investigation of semi- and self-supervised learning methods in the histopathological domain.

Training models with semi- or self-supervised learning methods is one way to reduce annotation effort since they rely on unlabeled or sparsely labeled datasets. Such approaches are particularly promising for domains with a time-consuming annotation process requiring specialized expertise and where high-quality labeled machine learning datasets are scarce, like in computational pathology. Even though some of these methods have been used in the histopathological domain, there is, so far, no comprehensive study comparing different approaches.

NGS read classification using AI.

Clinical metagenomics is a powerful diagnostic tool, as it offers an open view into all DNA in a patient's sample. This allows the detection of pathogens that would slip through the cracks of classical specific assays. However, due to this unspecific nature of metagenomic sequencing, a huge amount of unspecific data is generated during the sequencing itself and the diagnosis only takes place at the data analysis stage where relevant sequences are filtered out.

Metastable isonitrosyl structure of the nitroprusside anion confirmed by nuclear inelastic scattering.

Nuclear inelastic scattering (NIS) measurements were performed on a guanidium nitroprusside ((CN(3)H(6))(2)[Fe(CN)(5)NO], GNP) monocrystal at 77 K after the sample was illuminated with blue light (450 nm) at 50 K to populate the two metastable states, MS(1) and MS(2), of the nitroprusside anion. A second measurement was performed at 77 K after warming up the illuminated crystal to 250 K where the metastable states decay to the groundstate. The measured spectra were compared with simulated NIS spectra that were calculated by using density functional methods.