Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space

Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(V)AE (H-(V)AE), a fully end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space, suitable for unsupervised learning and generation of data distributed around a specified origin. H-(V)AE is trained to reconstruct the spherical Fourier encoding of data, learning in the process a latent space with a maximally informative invariant embedding alongside an equivariant frame describing the orientation of the data. We extensively test the performance of H-(V)AE on diverse datasets and show that its latent space efficiently encodes the categorical features of spherical images and structural features of protein atomic environments. Our work can further be seen as a case study for equivariant modeling of a data distribution by reconstructing its Fourier encoding

H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing

Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting the conformation of side-chains (rotamers) given the protein's backbone structure and amino-acid sequence. Conventional approaches for this task rely on expensive sampling procedures over hand-crafted energy functions and rotamer libraries. Recently, several deep learning methods have been developed to tackle the problem in a data-driven way, albeit with vastly different formulations (from image-to-image translation to directly predicting atomic coordinates). Here, we frame the problem as a joint regression over the side-chains' true degrees of freedom: the dihedral $\chi$ angles. We carefully study possible objective functions for this task, while accounting for the underlying symmetries of the task. We propose Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain packing built on top of two light-weight rotationally equivariant neural networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is computationally efficient and shows favorable performance against conventional physics-based algorithms and is competitive against alternative deep learning solutions.Comment: Accepted as a conference paper at MLCB 2023. 8 pages main body, 20 pages with appendix. 10 figure

RET mutation and increased angiogenesis in medullary thyroid carcinomas

Advanced medullary thyroid cancers (MTCs) are now being treated with drugs that inhibit receptor tyrosine kinases, many of which involved in angiogenesis. Response rates vary widely, and toxic effects are common, so treatment should be reserved for MTCs likely to be responsive to these drugs. RET mutations are common in MTCs, but it is unclear how they influence the microvascularization of these tumors. We examined 45 MTCs with germ-line or somatic RET mutations (RETmut group) and 34 with wild-type RET (RETwt). Taqman Low-Density Arrays were used to assess proangiogenic gene expression. Immunohistochemistry was used to assess intratumoral, peritumoral and nontumoral expression levels of VEGFR1, R2, R3, PDGFRa, PDGFB and NOTCH3. We also assessed microvessel density (MVD) and lymphatic vessel density (LVD) based on CD31-positive and podoplanin-positive vessel counts, respectively, and vascular pericyte density based on staining for a-smooth muscle actin (a-SMA), a pericyte marker. Compared with RETwt tumors, RETmut tumors exhibited upregulated expression of proangiogenic genes (mRNA and protein), especially VEGFR1, PDGFB and NOTCH3. MVDs and LVDs were similar in the two groups. However, microvessels in RETmut tumors were more likely to be a-SMA positive, indicating enhanced coverage by pericytes, which play key roles in vessel sprouting, maturation and stabilization. These data suggest that angiogenesis in RETmut MTCs may be more intense and complete than that found in RETwt tumors, a feature that might increase their susceptibility to antiangiogenic therapy. Given their increased vascular pericyte density, RETmut MTCs might also benefit from combined or preliminary treatment with PDGF inhibitors

Azacitidine and Lenalidomide (Combined vs Sequential Treatment) in Higher-Risk Myelodysplastic Syndromes. Long-Term Results of a Randomized Phase II Multicenter Study

Azacitidine and Lenalidomide (Combined vs Sequential Treatment) in Higher-Risk Myelodysplastic Syndromes. Long-Term Results of a Randomized Phase II Multicenter Stud

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