UNC93B1 recruits syntenin-1 to dampen TLR7 signalling and prevent autoimmunity.

At least two members of the Toll-like receptor (TLR) family, TLR7 and TLR9, can recognize self-RNA and self-DNA, respectively. Despite the structural and functional similarities between these receptors, their contributions to autoimmune diseases such as systemic lupus erythematosus can differ. For example, TLR7 and TLR9 have opposing effects in mouse models of systemic lupus erythematosus-disease is exacerbated in TLR9-deficient mice but attenuated in TLR7-deficient mice1. However, the mechanisms of negative regulation that differentiate between TLR7 and TLR9 are unknown. Here we report a function for the TLR trafficking chaperone UNC93B1 that specifically limits signalling of TLR7, but not TLR9, and prevents TLR7-dependent autoimmunity in mice. Mutations in UNC93B1 that lead to enhanced TLR7 signalling also disrupt binding of UNC93B1 to syntenin-1, which has been implicated in the biogenesis of exosomes2. Both UNC93B1 and TLR7 can be detected in exosomes, suggesting that recruitment of syntenin-1 by UNC93B1 facilitates the sorting of TLR7 into intralumenal vesicles of multivesicular bodies, which terminates signalling. Binding of syntenin-1 requires phosphorylation of UNC93B1 and provides a mechanism for dynamic regulation of TLR7 activation and signalling. Thus, UNC93B1 not only enables the proper trafficking of nucleic acid-sensing TLRs, but also sets the activation threshold of potentially self-reactive TLR7

Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation

We propose a self-supervised representation learning model for the task of unsupervised phoneme boundary detection. The model is a convolutional neural network that operates directly on the raw waveform. It is optimized to identify spectral changes in the signal using the Noise-Contrastive Estimation principle. At test time, a peak detection algorithm is applied over the model outputs to produce the final boundaries. As such, the proposed model is trained in a fully unsupervised manner with no manual annotations in the form of target boundaries nor phonetic transcriptions. We compare the proposed approach to several unsupervised baselines using both TIMIT and Buckeye corpora. Results suggest that our approach surpasses the baseline models and reaches state-of-the-art performance on both data sets. Furthermore, we experimented with expanding the training set with additional examples from the Librispeech corpus. We evaluated the resulting model on distributions and languages that were not seen during the training phase (English, Hebrew and German) and showed that utilizing additional untranscribed data is beneficial for model performance.Comment: Interspeech 2020 pape

Self-supervised Speaker Diarization

Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to the demonstrated effectiveness of their speaker representations. These, however, are heavily dependent on large amounts of annotated data and can be sensitive to new domains. This study proposes an entirely unsupervised deep-learning model for speaker diarization. Specifically, the study focuses on generating high-quality neural speaker representations without any annotated data, as well as on estimating secondary hyperparameters of the model without annotations. The speaker embeddings are represented by an encoder trained in a self-supervised fashion using pairs of adjacent segments assumed to be of the same speaker. The trained encoder model is then used to self-generate pseudo-labels to subsequently train a similarity score between different segments of the same call using probabilistic linear discriminant analysis (PLDA) and further to learn a clustering stopping threshold. We compared our model to state-of-the-art unsupervised as well as supervised baselines on the CallHome benchmarks. According to empirical results, our approach outperforms unsupervised methods when only two speakers are present in the call, and is only slightly worse than recent supervised models.Comment: Submitted to Interspeech 202

Opbrengstvergelijking lelie en hyacint Proef Bollenmeer

In het kader van het project ‘Teelt de grond uit’ is op de Oostwaardhoeve (Bollenmeer) een perceel aangelegd waarbij de ondergrond afgedekt is met folie. Het drainwater wordt niet geloosd op het oppervlakte water maar opgevangen in een waterbassin zodat er sprake is van een gesloten teeltsysteem. Op het proefveld wordt geteeld in een grondlaag met een ontwateringsdiepte van 45 en 80 cm. Het onderzoek moet antwoord geven op de vraag of het mogelijk is om lelies te te len op een dunne teeltlaag. Om te beoordelen of de ontwikkelde zandgrond voldoet aan de hoge eisen die de bloembollenteelt stelt, worden middels praktijkproeven de opbrengst en de kwaliteit van lelies op de Oostwaardhoeve (Bollenmeer) vergeleken met een bestaande locatie in het noordelijk zandgebied. In 2011 is bij het gewas lelie specifiek gekeken naar de mogelijkheid en effectiviteit van stomen in de 45 cm teeltlaag. De vraag is of het mogelijk is om in de winterperiode te stomen en of de benodigde temperatuur voor ziektedoding bereikt wordt. Proeftuin Zwaagdijk heeft in het teeltjaar 2011 voor het gewas lelie de opbrengst en de kwaliteit vergeleken van diverse percelen. Voor het gewas hyacint is alleen de vergelijking tussen de teeltlagen 0-45 cm en 0-8 0 cm gemaakt. In de zomer is ook de grond gestoomd voor de vervolgteelt van hyacint. In dit verslag zijn de resultaten van het onderzoek uitgewerkt