Order-to-Disorder Transition and Hydrogen Bonding in the Jahn-Teller Active NH4CrF3 Fluoroperovskite.

Large quantities of high-purity NH4CrF3 have been synthesized using a wet-chemical method, and its structural chemistry and magnetic properties are investigated in detail for the first time. NH4CrF3 is a tetragonal fluoroperovskite that displays an ordering of the ammonium (NH4+) groups at room temperature and C-type orbital ordering. The ammonium groups order and display distinct signs of hydrogen bonds to nearby fluoride anions by buckling the Cr-F-Cr angle away from 180°. The ammonium ordering remains up to 405 K, much higher than in other ammonium fluoroperovskites, indicating a correlation between the flexibility of the Jahn-Teller ion, the hydrogen bond formation, and the ammonium ordering. At 405 K, an order-to-disorder transition occurs, where the ammonium groups disorder, corresponding to a transition to higher symmetry. This is accompanied by a contraction of the unit cell from breaking hydrogen bonds, similar to the phenomenon observed in water ice melting. The compound orders antiferromagnetically with a Neél temperature of 60 K, an effective paramagnetic moment of 4.3 µB, and a Weiss temperature of -33 K. An A-type antiferromagnetic structure is identified by neutron diffraction, with an ordered moment of 3.72(2) µB.

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR.

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR).

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel deep learning method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.