DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. One recent approach proposes self-supervision based on non-rigid reconstruction. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a novel semi-supervised strategy to obtain dense inter-frame correspondences from a sparse set of annotations. This way, we obtain a large dataset of 400 scenes, over 390,000 RGB-D frames, and 2,537 densely aligned frame pairs; in addition, we provide a test set along with several metrics for evaluation. Based on this corpus, we introduce a data-driven non-rigid feature matching approach, which we integrate into an optimization-based reconstruction pipeline. Here, we propose a new neural network that operates on RGB-D frames, while maintaining robustness under large non-rigid deformations and producing accurate predictions. Our approach significantly outperforms both existing non-rigid reconstruction methods that do not use learned data terms, as well as learning-based approaches that only use self-supervision

How simple can a model of an empty viral capsid be? Charge distributions in viral capsids

We investigate and quantify salient features of the charge distributions on viral capsids. Our analysis combines the experimentally determined capsid geometry with simple models for ionization of amino acids, thus yielding the detailed description of spatial distribution for positive and negative charge across the capsid wall. The obtained data is processed in order to extract the mean radii of distributions, surface charge densities and dipole moment densities. The results are evaluated and examined in light of previously proposed models of capsid charge distributions, which are shown to have to some extent limited value when applied to real viruses.Comment: 10 pages, 10 figures; accepted for publication in Journal of Biological Physic

The Analysis of Genetic Variability of Norway Spruce (Picea abies (L.) Karst.) Subpopulation at the Igman Mountain

Uporabom 12 izoenzimskih sustava, te analizom 20 gen lokusa, uspoređivana je genetička struktura dviju subpopulacija s planine Igman. Prema ekološkim pokazateljima, a misli se na klimu i njeno djelovanje, postoje razlike između subpopulacija, jer jedna pripada mrazištu a druga tipičnoj planinskoj klimi. Rezultati analize izoenzimskih biljega upućuju na postojanje razlika između analiziranih subpopulacija.In this study we analyzed the genetic structure of two autochthonous subpopulations of Norway spruce in the Mountain of Igman by usage of isoenzyme markers. We collected the material for the analysis in two separate plant communities. The subpopulation Igman – A is represented by fir-tree and spruce forest with randomly distributed white pine trees (Abieti-Piceetum illyricum Stef. 1960) while the Igman – B subpopulation is represented through the spruce tree of frosty type in the mountain area (Piceetum montanum s.lat. (Fuk. et Stef., 1958, emend. Horv. et al., 1974)). Between the subpopulations there is a 150 m difference in altitude. We analyzed the following systems of Acotinase (Aco-A), Glutamate dehydrogenase (Gdh-A), Glutamate oxaloacetate transaminase (Got-A, Got-B, Got-C), Izocitrate dehidrogenase (Idh-A, Idh-B), Leucine aminopeptidase (Lap-B), Malate dehydrogenase (Mdh-A, Mdh-B, Mdh-C), Menadione reductase (Mnr-A, Mnr-C), Phosphoglucose isomerase (Pgi-B), Phosphoglucomutase (Pgm-A), Shikimate dehydrogenase (Skdh-A), 6-Phosphogluconate dehydrogenase (6-Pgdh-A, 6-Pgdh-B, 6-Pgdh-C) and Fluorescentesterase (Fest-B). The frequency of the allele and the frequency of genotypes show diversity between subpopulations. The Allele differentiation was most evident at loci Got-C, 6-Pgdh-A. In the sample of the Igman – A subpopulation the frequency of the allele Aco-A2 was 7 % lower, and the frequency of 6-Pgdh-A2 7 % higher than in the sample from Igman – B subpopulation. The genotype subpopulations are most explicitly differentiated at loci Fest-B, Got-C, Lap-B, Mdh-C, Mnr-A, Mnr-C, Pgi-B, 6-Pgdh-A, 6-Pgdh-B, 6-Pgdh-C. If the Igman – A subpopulation is compared with Igman – B subpopulation, we have 8–14% higher frequency of homozygote: Got-C44 (52 % vs. 44 %), Fest22 (90 % vs. 80 %), Mnr-A22 (12 % vs. 4 %), Mnr-C22 (94 % vs. 82 %), 6-Pgdh-A22 (94 % vs. 80 %), 6-Pgdh-C22 (42 % vs. 30 %) and from 10–14 % higher heterozygote frequency for gene loci: Lap-B46 (12 % vs. 0 %), Pgi-B23 (52 % vs. 42 %), 6-Pgdh-B25 (54 % vs. 40 %). In Igman – B subpopulation versus Igman – A subpopulation has 10 % higher homozygote frequency, as follows: Pgi-B33 (46 % vs. 36 %), 6-Pgdh-B22 (50 % vs. 40 %) and between 8–14 % heterozygote frequency Fest-B12 (14 % vs. 2 %), Mnr-A24 (70 % vs. 56 %), Mnr-C23 (16 % vs. 4 %), 6-Pgdh-A23 (12 % vs. 4 %), 6-Pgdh-C25 (60 % vs. 46 %). By statistical calculation we obtained an average number of allele per locus, thus in the subpopulation A the number of allele per locus was 2,71, and the effective was 1,307, and in the subpopulation B it was 2,59, while the effective number was 1,332. The actual heterozygosis in subpopulation A was 24,4 %, and expected was 84,1 %, and in the subpopulation B the actual was 26,2 %, and expected 81,9 %. The number of polymorphous loci in both populations was 17, and the percentage of polymorphous loci was 85,00%. Through the analysis of the allele genetic closeness and genetic distance (d0), we can conclude that the closeness is very high, and differences are relatively small. Thus we determined that the allele closeness has the value of 0,959, and the distance is 0,041 according to Gregorius (1974), which in our case is an extremely high value taking into account the distance between subpopulations of approximately 2 km. Applied statistical parameters for comparison of populations did not show major differences, but the analysis of the direct comparison of the allele presence and their frequency points at the existence of differences, that is, the influence of diverse selection pressures at populations

Fluid-membrane tethers: minimal surfaces and elastic boundary layers

Thin cylindrical tethers are common lipid bilayer membrane structures, arising in situations ranging from micromanipulation experiments on artificial vesicles to the dynamic structure of the Golgi apparatus. We study the shape and formation of a tether in terms of the classical soap-film problem, which is applied to the case of a membrane disk under tension subject to a point force. A tether forms from the elastic boundary layer near the point of application of the force, for sufficiently large displacement. Analytic results for various aspects of the membrane shape are given.Comment: 12 page

Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition

Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses (CCMV, MS2, STNV, STMV) for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available