Heat shock protein 90: translation from cancer to Alzheimer's disease treatment?

Both malignant transformation and neurodegeneration, as it occurs in Alzheimer's disease, are complex and lengthy multistep processes characterized by abnormal expression, post-translational modification, and processing of certain proteins. To maintain and allow the accumulation of these dysregulated processes, and to facilitate the step-wise evolution of the disease phenotype, cells must co-opt a compensatory regulatory mechanism. In cancer, this role has been attributed to heat shock protein 90 (Hsp90), a molecular chaperone that maintains the functional conformation of multiple proteins involved in cell-specific oncogenic processes. In this sense, at the phenotypic level, Hsp90 appears to serve as a biochemical buffer for the numerous cancer-specific lesions that are characteristic of diverse tumors. The current review proposes a similar role for Hsp90 in neurodegeneration. It will present experimentally demonstrated, but also hypothetical, roles that suggest Hsp90 can act as a regulator of pathogenic changes that lead to the neurodegenerative phenotype in Alzheimer's disease

Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization

We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics. By leveraging Bayesian optimization (BO) with a surrogate model, our approach enables efficient hyperparameter selection with multi-objective trade-offs and allows us to perform data-driven sensitivity analysis. By incorporating normalization and subsampling, the proposed framework demonstrates versatility and efficiency, as shown in applications to visualization techniques such as t-SNE and UMAP. We evaluate our results on various synthetic and real-world datasets using multiple quality metrics, providing a robust and efficient solution for hyperparameter selection in DR algorithms.Comment: 20 pages, 16 figure

Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation

Detecting out-of-context media, such as "mis-captioned" images on Twitter, is a relevant problem, especially in domains of high public significance. In this work we aim to develop defenses against such misinformation for the topics of Climate Change, COVID-19, and Military Vehicles. We first present a large-scale multimodal dataset with over 884k tweets relevant to these topics. Next, we propose a detection method, based on the state-of-the-art CLIP model, that leverages automatically generated hard image-text mismatches. While this approach works well on our automatically constructed out-of-context tweets, we aim to validate its usefulness on data representative of the real world. Thus, we test it on a set of human-generated fakes created by mimicking in-the-wild misinformation. We achieve an 11% detection improvement in a high precision regime over a strong baseline. Finally, we share insights about our best model design and analyze the challenges of this emerging threat.Comment: 11 pages, 6 figure

Visualizing the Determinants of Viral RNA Recognition by Innate Immune Sensor RIG-I

SummaryRetinoic acid inducible gene-I (RIG-I) is a key intracellular immune receptor for pathogenic RNAs, particularly from RNA viruses. Here, we report the crystal structure of human RIG-I bound to a 5′ triphosphorylated RNA hairpin and ADP nucleotide at 2.8 Å resolution. The RNA ligand contains all structural features that are essential for optimal recognition by RIG-I, as it mimics the panhandle-like signatures within the genome of negative-stranded RNA viruses. RIG-I adopts an intermediate, semiclosed conformation in this product state of ATP hydrolysis. The structure of this complex allows us to visualize the first steps in RIG-I recognition and activation upon viral infection