Interactive NeRF geometry editing with shape priors

Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it is not trivial to change their content afterwards. Previous methods to modify NeRFs provide some control but they do not support direct shape deformation which is common for geometry representations like triangle meshes. In this paper, we present a NeRF geometry editing method that first extracts a triangle mesh representation of the geometry inside a NeRF. This mesh can be modified by any 3D modeling tool (we use ARAP mesh deformation). The mesh deformation is then extended into a volume deformation around the shape which establishes a mapping between ray queries to the deformed NeRF and the corresponding queries to the original NeRF. The basic shape editing mechanism is extended towards more powerful and more meaningful editing handles by generating box abstractions of the NeRF shapes which provide an intuitive interface to the user. By additionally assigning semantic labels, we can even identify and combine parts from different objects. We demonstrate the performance and quality of our method in a number of experiments on synthetic data as well as real captured scenes

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

The Genome of Ganderma lucidum Provide Insights into Triterpense Biosynthesis and Wood Degradation

BACKGROUND: Ganoderma lucidum (Reishi or Ling Zhi) is one of the most famous Traditional Chinese Medicines and has been widely used in the treatment of various human diseases in Asia countries. It is also a fungus with strong wood degradation ability with potential in bioenergy production. However, genes, pathways and mechanisms of these functions are still unknown. METHODOLOGY/PRINCIPAL FINDINGS: The genome of G. lucidum was sequenced and assembled into a 39.9 megabases (Mb) draft genome, which encoded 12,080 protein-coding genes and ∼83% of them were similar to public sequences. We performed comprehensive annotation for G. lucidum genes and made comparisons with genes in other fungi genomes. Genes in the biosynthesis of the main G. lucidum active ingredients, ganoderic acids (GAs), were characterized. Among the GAs synthases, we identified a fusion gene, the N and C terminal of which are homologous to two different enzymes. Moreover, the fusion gene was only found in basidiomycetes. As a white rot fungus with wood degradation ability, abundant carbohydrate-active enzymes and ligninolytic enzymes were identified in the G. lucidum genome and were compared with other fungi. CONCLUSIONS/SIGNIFICANCE: The genome sequence and well annotation of G. lucidum will provide new insights in function analyses including its medicinal mechanism. The characterization of genes in the triterpene biosynthesis and wood degradation will facilitate bio-engineering research in the production of its active ingredients and bioenergy

Global trends in research on extracorporeal shock wave therapy (ESWT) from 2000 to 2021

Abstract Background This study intended to analyze the application of extracorporeal shock wave therapy in medicine and to evaluate the quality of related literature. Methods All publications were extracted from 2000 to 2021 from the Web of Science Core Collection (WoSCC). The literature characteristics were depicted by VOSviewer (version 1.6.15) and the online bibliometric website ( http://bibliometric.com/ ). The future trends and hotspots were conducted by Bibliographic Item Co-occurrence Matrix Builder (version 2.0) and gCLUTO software. Results We analyzed 1774 articles corresponding to the criteria for ESWT publications from 2000 to 2021. Most studies were conducted within the United States and China which besides have the most cooperation. The most published research institutions are Chang Gung University, Kaohsiung Chang Gung Memorial Hospital, and Kaohsiung Medical University. Six research hotspots were identified by keyword clustering analysis: Cluster0: The effects of ESWT on muscle spasticity; Cluster1: The application of ESWT in osteoarthritis (OA); Cluster2: Therapeutic effect of ESWT on tendon diseases; Cluster3: Early application of ESWT/ESWL in urolithiasis; Cluster4: The Role of angiogenesis in ESWT and the efficiency of ESWT for penile disease; Cluster5: The Special value of radial extracorporeal shock wave therapy (rESWT). Conclusions A comprehensive and systematic bibliometric analysis of ESWT was conducted in our study. We identified six ESWT-related research hotspots and predicted future research trends. With the gradual increase of research on ESWT, we find that ESWT is used more and more extensively, such in musculoskeletal disease, bone delay union, neurological injury, andrology disorders, lymphedema, and so on. In addition, the mechanism is not destructive damage, as initially thought, but a restorative treatment. Furthermore, delayed union, cellulite, burn, and diabetic foot ulcers may be the future direction of scientific study

Physically-based NURBS surface editing with curves

This paper presents a physically-based approach for NURBS surface modification using curve handles. The approach consists of three steps: curve handle creation, target curve generation, and surface modification. The handle curve is a composite curve that is a composition of a NURBS surface and a 2D parametric curve defined in the parameter domain of the NURBS surface. The handle curve is treated as a physically-based dynamic curve whose formulation is derived directly based on the representation of the NURBS surface where the composition curve is defined. Then the target curve is generated as the result of dynamic evolution of the physically-based composition curve with external point forces. Finally, the initial handle curve and the target curve are used to define a curve force for the dynamic NURBS to modify the NURBS surface. The use of curves as shape handles makes the shape editing more effective and intuitive, and the incorporation of physics into geometric modeling makes the modeling process meaningful and the resulting surface smooth. Examples are provided to demonstrate the effectiveness and capability of the proposed NURBS manipulation method

Comparison of the efficacy and comfort of high-flow nasal cannula with different initial flow settings in patients with acute hypoxemic respiratory failure: a systematic review and network meta-analysis

Abstract Background High-flow nasal cannula (HFNC) has been proven effective in improving patients with acute hypoxemic respiratory failure (AHRF), but a discussion of its use for initial flow settings still need to be provided. We aimed to compare the effectiveness and comfort evaluation of HFNC with different initial flow settings in patients with AHRF. Methods Studies published by October 10, 2022, were searched exhaustively in PubMed, Embase, Web of Science, Cochrane Library (CENTRAL), and the China National Knowledge Infrastructure (CNKI) database. Network meta-analysis (NMA) was performed with STATA 17.0 and R software (version 4.2.1). A Bayesian framework was applied for this NMA. Comparisons of competing models based on the deviance information criterion (DIC) were used to select the best model for NMA. The primary outcome is the intubation at day 28. Secondary outcomes included short-term and long-term mortality, comfort score, length of ICU or hospital stay, and 24-h PaO2/FiO2. Results This NMA included 23 randomized controlled trials (RCTs) with 5774 patients. With NIV as the control, the HFNC_high group was significantly associated with lower intubation rates (odds ratio [OR] 0.72 95% credible interval [CrI] 0.56 to 0.93; moderate quality evidence) and short-term mortality (OR 0.81 95% CrI 0.69 to 0.96; moderate quality evidence). Using HFNC_Moderate (Mod) group (mean difference [MD] − 1.98 95% CrI -3.98 to 0.01; very low quality evidence) as a comparator, the HFNC_Low group had a slight advantage in comfort scores but no statistically significant difference. Of all possible interventions, the HFNC_High group had the highest probability of being the best in reducing intubation rates (73.04%), short-term (82.74%) and long-term mortality (67.08%). While surface under the cumulative ranking curve value (SUCRA) indicated that the HFNC_Low group had the highest probability of being the best in terms of comfort scores. Conclusions The high initial flow settings (50–60 L/min) performed better in decreasing the occurrence of intubation and mortality, albeit with poor comfort scores. Treatment of HFNC for AHRF patients ought to be initiated from moderate flow rates (30–40 L/min), and individualized flow settings can make HFNC more sensible in clinical practice

Triangular mesh deformation via edge-based graph

Mesh deformation is an important mesh editing process which provides a convenient way to modify a given mesh to meet various design requirements. Most of the existing mesh deformation methods establish their formulations in the primal vertex-based domain and are expected to produce globally smooth and consistent deformation results while preserving geometry details as much as possible. In this paper, we present a new surface-based mesh deformation method that performs the computation via an edge-based graph to increase the sampling rate for more accurate shape computation and better deformation results. The user is given the flexibility of adjusting the deformation effect between local shape preservation and global smoothness. Moreover, to simulate the deformation behaviors of regions with different materials, we introduce a stiffness property into the deformation model and present an easy and intuitive way for the user to set the material property. Experimental results demonstrate that our algorithm can produce satisfactory results and make the deformation more flexible

Tetrahedral mesh deformation with positional constraints

Deforming a tetrahedral mesh to conform to geometry modifications is a useful process in applications. This paper presents a method for tetrahedral mesh deformation driven by displacement of partial vertices of the mesh. The basic techniques behind the method are radial basis function (RBF)-based interpolation and adaptive mesh refinement. The method is realized by a warping process that transforms the mesh via iterative RBF-based interpolation to avoid the inversion of tetrahedra. Adaptive refinement by locally bisecting potentially inverted tetrahedra is also introduced to assure sufficiently large warping stepsizes. The refinement is performed on both the input mesh and the warped meshes concurrently to maintain the consistency of the topology. As a result, the method can effectively produce an inversion-free and topology compatible deformation mesh that satisfies hard positional constraints. Experimental results show the effectiveness of the method.Ministry of Education (MOE)This work was conducted in collaboration with HP Inc. and partially supported by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant. It was also partially supported by the Ministry of Education, Singapore, under its MOE Tier-2 Grant (2017-T2-1-076)