MYB immunohistochemistry as a predictor of MYB::NFIB fusion in the diagnosis of adenoid cystic carcinoma of the head and neck.

Objectives: Diagnosing adenoid cystic carcinoma (AdCC) is challenging due to histopathological variability and similarities with other tumors. In AdCC pathogenesis, the cellular myeloblastosis gene (c-MYB) often exhibits a MYB::NFIB fusion from a reciprocal translocation. This study aimed to assess the predictive accuracy of MYB immunohistochemistry for detecting this translocation compared to fluorescence in situ hybridization (FISH).

Research in methodologies for modelling the oral cavity.

The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.

Benchmarking framework for machine learning classification from fNIRS data.

Background: While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.

COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition.

Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a machine learning framework. This paper introduces an uncertainty-aware multimodal fusion approach that quantifies modality-wise aleatoric or data uncertainty towards emotion prediction. We propose a novel fusion framework, in which latent distributions over unimodal temporal context are learned by constraining their variance.