Semantically Informed Multiview Surface Refinement

We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated images. Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D. In the semantic segmentation step, the labels on the mesh are updated with MRF inference, such that they are compatible with the semantic segmentations in the input images. Also, this step includes prior assumptions about the surface shape of different semantic classes. The priors induce a tight coupling, where semantic information influences the shape update and vice versa. Specifically, we introduce priors that favor (i) adaptive smoothing, depending on the class label; (ii) straightness of class boundaries; and (iii) semantic labels that are consistent with the surface orientation. The novel mesh-based reconstruction is evaluated in a series of experiments with real and synthetic data. We compare both to state-of-the-art, voxel-based semantic 3D reconstruction, and to purely geometric mesh refinement, and demonstrate that the proposed scheme yields improved 3D geometry as well as an improved semantic segmentation

Medium Cut-Off (MCO) Membranes Reduce Inflammation in Chronic Dialysis Patients—A Randomized Controlled Clinical Trial

Background To increase the removal of middle-sized uremic toxins a new membrane with enhanced permeability and selectivity, called Medium Cut-Off membrane (MCO-Ci) has been developed that at the same time ensures the retention of albumin. Because many middle-sized substances may contribute to micro-inflammation we hypothesized that the use of MCO-Ci influences the inflammatory state in hemodialysis patients. Methods The randomized crossover trial in 48 patients compared MCO-Ci dialysis to High-flux dialysis of 4 weeks duration each plus 8 weeks extension phase. Primary endpoint was the gene expression of TNF-α and IL-6 in peripheral blood mononuclear cells (PBMCs), secondary endpoints were plasma levels of specified inflammatory mediators and cytokines. Results After four weeks of MCO-Ci the expression of TNF-α mRNA (Relative quantification (RQ) from 0.92 ± 0.34 to 0.75 ± 0.31, -18.5%, p<0.001)-α and IL-6 mRNA (RQ from 0.78 ± 0.80 to 0.60 ± 0.43, -23.1%, p<0.01) was reduced to a significantly greater extent than with High-flux dialyzers (TNF mRNA-RQ: -14.3%; IL-6 mRNA-RQ: -3.5%). After retransformation of logarithmically transformed data, measurements after MCO were reduced to 82% of those after HF (95% CI 74%–91%). 4 weeks use of MCO-Ci resulted in long- lasting change in plasma levels of several cytokines and other substances with a significant decrease for sTNFR1, kappa and lambda free light chains, urea and an increase for Lp-PLA2 (PLA2G7) compared to High-flux. Albumin levels dropped significantly after 4 weeks of MCO dialysis but increased after additional 8 weeks of MCO dialysis. Twelve weeks treatment with MCO-Ci was well tolerated regarding the number of (S)AEs. In the extension period levels of CRP, TNF-α-mRNA and IL-6 mRNA remained stable in High-flux as well as in MCO-Ci. Conclusions MCO-Ci dialyzers modulate inflammation in chronic HD patients to a greater extent compared to High-flux dialyzers. Transcription of pro-inflammatory cytokines in peripheral leukocytes is markedly reduced and removal of soluble mediators is enhanced with MCO dialysis. Serum albumin concentrations stabilize after an initial drop. These results encourage further trials with longer treatment periods and clinical endpoints

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift\unicode{x2013}\unicode{x2013}a setting that poses a challenge to standard deep learning methods. In this paper, we present Q-SAVI, a probabilistic model able to address these challenges by encoding explicit prior knowledge of the data-generating process into a prior distribution over functions, presenting researchers with a transparent and probabilistically principled way to encode data-driven modeling preferences. Building on a novel, gold-standard bioactivity dataset that facilitates a meaningful comparison of models in an extrapolative regime, we explore different approaches to induce data shift and construct a challenging evaluation setup. We then demonstrate that using Q-SAVI to integrate contextualized prior knowledge of drug-like chemical space into the modeling process affords substantial gains in predictive accuracy and calibration, outperforming a broad range of state-of-the-art self-supervised pre-training and domain adaptation techniques.Comment: Published in the Proceedings of the 40th International Conference on Machine Learning (ICML 2023

Maternal Diabetes Leads to Unphysiological High Lipid Accumulation in Rabbit Preimplantation Embryos

According to the "developmental origin of health and disease" hypothesis, the metabolic set points of glucose and lipid metabolism are determined prenatally. In the case of a diabetic pregnancy, the embryo is exposed to higher glucose and lipid concentrations as early as during preimplantation development. We used the rabbit to study the effect of maternal diabetes type 1 on lipid accumulation and expression of lipogenic markers in preimplantation blastocysts. Accompanied by elevated triglyceride and glucose levels in the maternal blood, embryos from diabetic rabbits showed a massive intracellular lipid accumulation and increased expression of fatty acid transporter 4, fatty acid-binding protein 4, perilipin/adipophilin, and maturation of sterol-regulated element binding protein. However, expression of fatty acid synthase, a key enzyme for de novo synthesis of fatty acids, was not altered in vivo. During a short time in vitro culture of rabbit blastocysts, the accumulation of lipid droplets and expression of lipogenic markers were directly correlated with increasing glucose concentration, indicating that hyperglycemia leads to increased lipogenesis in the preimplantation embryo. Our study shows the decisive effect of glucose as the determining factor for fatty acid metabolism and intracellular lipid accumulation in preimplantation embryos

Functionality of Two Origins of Replication in Vibrio cholerae Strains With a Single Chromosome

Chromosomal inheritance in bacteria usually entails bidirectional replication of a single chromosome from a single origin into two copies and subsequent partitioning of one copy each into daughter cells upon cell division. However, the human pathogen Vibrio cholerae and other Vibrionaceae harbor two chromosomes, a large Chr1 and a small Chr2. Chr1 and Chr2 have different origins, an oriC-type origin and a P1 plasmid-type origin, respectively, driving the replication of respective chromosomes. Recently, we described naturally occurring exceptions to the two-chromosome rule of Vibrionaceae: i.e., Chr1 and Chr2 fused single chromosome V. cholerae strains, NSCV1 and NSCV2, in which both origins of replication are present. Using NSCV1 and NSCV2, here we tested whether two types of origins of replication can function simultaneously on the same chromosome or one or the other origin is silenced. We found that in NSCV1, both origins are active whereas in NSCV2 ori2 is silenced despite the fact that it is functional in an isolated context. The ori2 activity appears to be primarily determined by the copy number of the triggering site, crtS, which in turn is determined by its location with respect to ori1 and ori2 on the fused chromosome

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of measures is described in detail and then summarized according to the timescales required for their implementation. The measures will both contribute to sustainable research and accelerate scientific progress through increased awareness of resource usage. This work is based on a three-days workshop on sustainability in digital transformation held in May 2023.Comment: 20 pages, 2 figures, publication following workshop 'Sustainability in the Digital Transformation of Basic Research on Universe & Matter', 30 May to 2 June 2023, Meinerzhagen, Germany, https://indico.desy.de/event/3748