A Fast Geometric Multigrid Method for Curved Surfaces

We introduce a geometric multigrid method for solving linear systems arising from variational problems on surfaces in geometry processing, Gravo MG. Our scheme uses point clouds as a reduced representation of the levels of the multigrid hierarchy to achieve a fast hierarchy construction and to extend the applicability of the method from triangle meshes to other surface representations like point clouds, nonmanifold meshes, and polygonal meshes. To build the prolongation operators, we associate each point of the hierarchy to a triangle constructed from points in the next coarser level. We obtain well-shaped candidate triangles by computing graph Voronoi diagrams centered around the coarse points and determining neighboring Voronoi cells. Our selection of triangles ensures that the connections of each point to points at adjacent coarser and finer levels are balanced in the tangential directions. As a result, we obtain sparse prolongation matrices with three entries per row and fast convergence of the solver.Comment: Ruben Wiersma and Ahmad Nasikun contributed equally. To be published in SIGGRAPH 2023. 16 pages total (8 main, 5 supplement), 14 figure

TDP-43 oligomerization and RNA binding are codependent but their loss elicits distinct pathologies

Aggregation of the RNA-binding protein TDP-43 is the main common neuropathological feature of TDP-43 proteinopathies. In physiological conditions, TDP-43 is predominantly nuclear and contained in biomolecular condensates formed via liquid-liquid phase separation (LLPS). However, in disease, TDP-43 is depleted from these compartments and forms cytoplasmic or, sometimes, intranuclear inclusions. How TDP-43 transitions from physiological to pathological states remains poorly understood. Here, we show that self-oligomerization and RNA binding cooperatively govern TDP-43 stability, functionality, LLPS and cellular localization. Importantly, our data reveal that TDP-43 oligomerization is connected to, and conformationally modulated by, RNA binding. Mimicking the impaired proteasomal activity observed in patients, we found that TDP-43 forms nuclear aggregates via LLPS and cytoplasmic aggregates via aggresome formation. The favored aggregation pathway depended on the TDP-43 state –monomeric/oligomeric, RNA-bound/-unbound– and the subcellular environment –nucleus/cytoplasm. Our work unravels the origins of heterogeneous pathological species occurring in TDP-43 proteinopathies

Loss of TDP-43 oligomerization or RNA binding elicits distinct aggregation patterns

Aggregation of the RNA-binding protein TAR DNA-binding protein 43 (TDP-43) is the key neuropathological feature of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). In physiological conditions, TDP-43 is predominantly nuclear, forms oligomers, and is contained in biomolecular condensates assembled by liquid-liquid phase separation (LLPS). In disease, TDP-43 forms cytoplasmic or intranuclear inclusions. How TDP-43 transitions from physiological to pathological states remains poorly understood. Using a variety of cellular systems to express structure-based TDP-43 variants, including human neurons and cell lines with near-physiological expression levels, we show that oligomerization and RNA binding govern TDP-43 stability, splicing functionality, LLPS, and subcellular localization. Importantly, our data reveal that TDP-43 oligomerization is modulated by RNA binding. By mimicking the impaired proteasomal activity observed in ALS/FTLD patients, we found that monomeric TDP-43 forms inclusions in the cytoplasm, whereas its RNA binding-deficient counterpart aggregated in the nucleus. These differentially localized aggregates emerged via distinct pathways: LLPS-driven aggregation in the nucleus and aggresome-dependent inclusion formation in the cytoplasm. Therefore, our work unravels the origins of heterogeneous pathological species reminiscent of those occurring in TDP-43 proteinopathy patients

Urologists’ and GPs’ knowledge of hereditary prostate cancer is suboptimal for prostate cancer counseling: a nation-wide survey in The Netherlands

A family history of prostate cancer (PCa) is an established risk factor for PCa. In case of a positive family history, the balance between positive and adverse effects of prostate-specific antigen (PSA) testing might be different from the general population, for which the European Randomized Study of Screening for Prostate Cancer (ERSPC) showed a beneficial effect on mortality. This, however, went at the cost of considerable overtreatment. This study assessed Dutch physicians’ knowledge of heredity and PCa and their ‘post-ERSPC’ attitude towards PCa testing, including consideration of family history. In January 2010, all Dutch urologists and clinical geneticists (CGs) and 300 general practitioners (GPs) were invited by email to complete an anonymous online survey, which contained questions about hereditary PCa and their attitudes towards PCa case-finding and screening. 109 urologists (31%), 69 GPs (23%) and 46 CGs (31%) completed the survey. CGs had the most accurate knowledge of hereditary PCa. All but 1 CG mentioned at least one inherited trait with PCa, compared to only 25% of urologists and 9% of GPs. CGs hardly ever counseled men about PCa testing. Most urologists and GPs discuss possible risks and benefits before testing for PCa with PSA. Remarkably, 35–40% of them do not take family history into consideration. Knowledge of urologists and GPs about heredity and PCa is suboptimal. Hence, PCa counseling might not be optimal for men with a positive family history. Multidisciplinary guidelines on this topic should be developed to optimize personalized counseling

Community compensation in the context of Carbon Capture and Storage: Current debates and practices

Societal opposition has the potential to slow down the implementation of Carbon Capture and Storage (CCS). One of the difficulties is that the perceived benefits associated with a CCS facility for local communities tend to be low compared to its perceived burdens. As is the case for other low carbon technologies, community compensation (or community benefits) has been suggested as a way to restore this perceived imbalance. A diverse literature has looked into the role of community compensation across various land uses and research fields. Synthesis is limited, while at the same time, the provision of community compensation in practice is moving from an ad hoc to a more institutionalized approach. Therefore, it is important to take stock of the literature. This paper provides a review of the community compensation literature in the form of four debates, drawing together environmental social science research on different low carbon technologies (e.g. CCS, renewable energy). In addition, current practices in community compensation for four European countries are discussed. The two parts of this paper are brought together in a set of lessons for the provision of community compensation for future CCS projects; in turn, suggestions for further research are made to address remaining knowledge gaps

Harmonic Surface Networks

We present a new approach for deep learning on surfaces, combining geometric convolutional networks with rotationally equivariant networks. Existing work either learns rotationally invariant filters, or learns filters in the tangent plane without correctly relating orientations between different tangent planes (orientation ambiguity). We propose a solution to both problems by applying Harmonic Networks on surfaces in the tangent plane: Harmonic Surface Networks (HSN).Harmonic Networks constrain their filters to circular harmonics, which output complexvalued, rotatable feature maps. Considering these complex features as vectors inside the tangent plane, we can use parallel transport along shortest geodesics to transport them along the surface in a natural way. Additionally, Harmonic Networks can be configured so that the output is rotationally invariant, while containing rotationally equivariant filters in hidden layers. This property solves the orientation ambiguity problem, while learning directional filters. We evaluate HSN on three different problems: classification on Rotated MNIST in a plane and mapped to a sphere, correspondence on FAUST, and shape segmentation on FAUST. The results suggest that HSN could improve on state of the art approaches.Computer Scienc

Tau: a phase in the crowd

From the management of microtubules to the production of pathological species: liquid–liquid phase separation may tune the behavior of the protein tau in health and neurodegenerative disease. In this issue of The EMBO Journal, Hochmair et al (2022) demystify important aspects of tau condensate compilation

DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global coordinate system for tangential directions on surfaces. We introduce DeltaConv, a convolution layer that combines geometric operators from vector calculus to enable the construction of anisotropic filters on point clouds. Because these operators are defined on scalar- and vector-fields, we separate the network into a scalar- and a vector-stream, which are connected by the operators. The vector stream enables the network to explicitly represent, evaluate, and process directional information. Our convolutions are robust and simple to implement and match or improve on state-of-the-art approaches on several benchmarks, while also speeding up training and inference.Comment: 8 pages, 5 figures, 7 tables; ACM Transactions on Graphics 41, 4, Article 105 (SIGGRAPH 2022

Automating Valuations for Real-Estate

As GeoPhy is developing its business model and looking into the future of automated valu- ation models (AVM), this project delivers a proof of concept of a system that automates the training, maintaining, and delivery of machine learning models for automated valuations. In order to achieve this goal, the situation and problem were first analysed. This resulted in an outline of the desired product and requirements in the form of a MoSCoW analysis. An important goal for this project was to incorporate streams of data from a stream processing platform (Apache Kafka) into a service that would train and update models automatically. The second goal for this project was to keep track of the changes in the data in order to detect significant changes in distribution (concept drift) of the target prediction value.These subjects were studied in literature, reviewing existing and upcoming valuation prac- tices in real-estate, steps needed to perform machine learning tasks, architecture to support big data processing, and concept drift. This resulted in a design made up of four different components: An ETL and data processing component, a modelling component, a Kafka con- nector, and a client-facing API. An important part to ensure efficiency and scalability of the system is the implementation of concept drift: models are only retrained when the distribu- tion of the target training value has changed significantly.These components use storage in the form of a Postgres database, disk storage and Elastic Search logs. The logs (on model performance and concept drift usage) can be interpreted through a Grafana dashboard, which is editable through its own GUI.Finally, to test the success of the project, a testing plan was set up and the code was reviewed by an external group (SIG). The code achieved all the testing milestones and received a 4.5/5 in a mid-development review on maintainability. With this project, the concept of automated valuation models inside GeoPhy’s new architecture has been tested and proved and the project is ready to be further developed and used in practice