Search Results (26)

Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors is important for diagnosis and treatment planning of liver cancer. Methods: A hybrid model with a U-Net based structure and the Whale Optimization Algorithm (WOA) was proposed. WOA was used to optimize the hyperparameters of the conventional LiTS-Res-UNet to obtain the best segmentation performance of the deep learning model. Results: The LiTS-Res-Unet + WOA hybrid model achieved a performance of 99.54% for accuracy, with a Dice coefficient of 92.38% and a Jaccard index of 86.73% on the benchmark dataset, outperforming state-of-the-art methods. Conclusions: The WOA-based adaptive search space was able to obtain an optimal set of hyperparameters for deep learning model convergence while increasing the accuracy of the model in the proposed hybrid model. The robust performance and clinical applicability of the model in liver tumor segmentation were demonstrated. Full article

The “Movahed sign” refers to the presence of a D-shaped left ventricle on cross-sectional cardiac imaging due to interventricular septal flattening seen during cardiac gated single-photon emission computed tomography (SPECT) This phenomenon arises from significant right ventricular (RV) pressure or volume overload, which pushes the septum toward the left ventricle (LV) and distorts the LV’s normally circular profile into a “D” shape. However, the prevalence and incidence of similar findings during cardiac Magnetic Resonance Imaging (MRI) or computed tomography (CT) are not known. The goal of this study was to perform a literature search focusing on the “Movahed sign” or D-shaped left ventricle in the context of cardiac MRI and CT. Databases searched included PubMed and Google Scholar, and reference lists of relevant articles were reviewed. The echocardiography literature was also consulted for foundational concepts of septal flattening. Key data on pathophysiology, imaging features, clinical correlations, and prognostic significance were extracted. Full article

In this paper, a new approach to topology optimization using the parameterized level set function and genetic algorithm optimization methods is presented. The impact of a number of parameters describing the level set function in the representation of the model was examined. Using the B-spline interpolation function, the number of variables describing the level set function was decreased, enabling the application of evolutionary methods (genetic algorithms) in the topology optimization process. The traditional level set method is performed by using the Hamilton–Jacobi transport equation, which implies the use of gradient optimization methods that are prone to becoming stuck in local minima. Furthermore, the resulting optimal shapes are strongly dependent on the initial solution. The proposed topology optimization procedure, written in MATLAB R2013b, utilizes a genetic algorithm for global optimization, enabling it to locate the global optimum efficiently. To assess the acceleration and convergence capabilities of the proposed topology optimization method, a new genetic algorithm penalty operator was tested. This operator addresses the slow convergence issue typically encountered when the genetic algorithm optimization procedure nears a solution. By penalizing similar individuals within a population, the method aims to enhance convergence speed and overall performance. In complex examples (3D), the method can also function as a generator of good initial solutions for faster topology optimization methods (e.g., level set) that rely on such initial solutions. Both the proposed method and the traditional methods have their own advantages and limitations. The main advantage is that the proposed method is a global search method. This makes it robust against entrapment in local minima and independent of the initial solution. It is important to note that this evolutionary approach does not necessarily perform better in terms of convergence speed compared to gradient-based or other local optimization methods. However, once the global optimum has been found using the genetic algorithm, convergence can be accelerated using a faster local method such as gradient-based optimization. The application and usefulness of the method were tested on typical 2D cantilever beams and Michell beams. Full article

The high biocompatibility and the key role of collagen in bone extracellular matrix make it useful for tissue engineering. However, the high demand, costs, and challenges of extracting good-quality collagen have led to the use of collagen derivatives and search for non-human alternatives. This study investigates fish and bovine collagen peptides (Coll_f and Coll_b, respectively) as sustainable sources for 3D-printed bone scaffolds by developing and characterizing peptide-incorporated alginate/hydroxyapatite-based bioinks. The chemical analysis revealed structural similarities between the peptides, while rheological tests showed a slightly higher viscosity of Coll_f-based inks, which improved shape fidelity during the printing process. Upon oscillating rheological tests, both the Coll_f and Coll_b-based ink formulations demonstrated a solid-like behavior at frequencies higher than 0.4 Hz, which is crucial for maintaining the printed structure integrity during extrusion. Although Coll_b-based inks exhibited better pore printability, Coll_f-based inks achieved superior resolution and geometry retention. Macro-porous structures printed from both inks showed good accuracy, with minimal shrinkage attributed to hydroxyapatite. Both the produced inks had a high gel fraction and swelling behavior, with Coll_b-based outperforming Coll_f-based inks. Finally, both ink formulations resulted to be cytocompatibile with human dermal fibroblasts. These findings position Coll_f- and Coll_b-based inks as promising alternatives for bone tissue scaffolds, offering a sustainable balance between performance and structural stability in 3D printing applications. Full article

This study presents six degrees of freedom (6-DoF) pose estimation of an object from a single RGB image and retrieval of the matching CAD model by measuring the similarity between RGB and CAD rendering images. The 6-DoF pose estimation of an RGB object is one of the important techniques in 3D computer vision. However, in addition to 6-DoF pose estimation, retrieval and alignment of the matching CAD model with the RGB object should be performed for various industrial applications such as eXtended Reality (XR), Augmented Reality (AR), robot’s pick and place, and so on. This paper addresses 6-DoF pose estimation and CAD model retrieval problems simultaneously and quantitatively analyzes how much the 6-DoF pose estimation affects the CAD model retrieval performance. This study consists of two main steps. The first step is 6-DoF pose estimation based on the PoseContrast network. We enhance the structure of PoseConstrast by adding variance uncertainty weight and feature attention modules. The second step is the retrieval of the matching CAD model by an image similarity measurement between the CAD rendering and the RGB object. In our experiments, we used 2000 RGB images collected from Google and Bing search engines and 100 CAD models from ShapeNetCore. The Pascal3D+ dataset is used to train the pose estimation network and DELF features are used for the similarity measurement. Comprehensive ablation studies about the proposed network show the quantitative performance analysis with respect to the baseline model. Experimental results show that the pose estimation performance has a positive correlation with the CAD retrieval performance. Full article

Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based approaches can be divided into two-stage and single-stage methods, each with limitations. Two-stage methods often rely on generating intermediate proposals by searching for similar structures across the entire dataset, a process that is computationally expensive due to the large search space and high-dimensional feature-matching requirements, further limiting flexibility to predefined object categories. In contrast, single-stage methods directly reconstruct 3D shapes from images without intermediate steps, but they struggle to capture complex object geometries due to high feature loss between image features and 3D shapes and limit their ability to represent intricate details. To address these challenges, this paper introduces SS3DNet-AF, a single-stage, single-view 3D reconstruction network with an attention-based fusion (AF) mechanism to enhance focus on relevant image features, effectively capturing geometric details and generalizing across diverse object categories. The proposed method is quantitatively evaluated using the ShapeNet dataset, demonstrating its effectiveness in achieving accurate 3D reconstructions while overcoming the computational challenges associated with traditional approaches. Full article

Background: Photodynamic therapy (PDT) is a treatment modality that uses light to activate a photosensitizing agent, destroying target cells. The growing awareness of the necessity to reduce or eliminate the use of mammals in research has prompted the search for safer toxicity testing models aligned with the new global guidelines and compliant with the relevant regulations. Objective: The objective of this study was to assess the impact of PDT on alternative models to mammals, including in vitro three-dimensional (3D) cultures and in vivo, in invertebrate animals, utilizing a potent photosensitizer, 2-hydroxychalcone. Methods: Cytotoxicity was assessed in two cellular models: monolayer (2D) and 3D. For this purpose, spheroids of two cell lines, primary dermal fibroblasts (HDFa) and adult human epidermal cell keratinocytes (HaCat), were developed and characterized following criteria on cell viability, shape, diameter, and number of cells. The survival percentages of Caenorhabditis elegans and Galleria mellonella were evaluated at 1 and 7 days, respectively. Results: The findings indicated that all the assessed platforms are appropriate for investigating PDT toxicity. Furthermore, 2-hydroxychalcone demonstrated low toxicity in the absence of light and when mediated by PDT across a range of in vitro (2D and 3D cultures) and in vivo (invertebrate animal models, including G. mellonella and C. elegans) models. Conclusion: There was a strong correlation between the in vitro and in vivo tests, with similar toxicity results, particularly in the 3D models and C. elegans, where the concentration for 50% viability was approximately 100 µg/mL. Full article

The aerospace industry is imposing increasingly strict dimensional tolerances, which is forcing continuous development in component manufacturing. Ensuring tight dimensional tolerances is difficult for thin-walled structures due to their reduced stiffness, which are increasingly used in the aerospace industry, where titanium alloys and nickel alloys, among others, dominate. Developments in this area are causing a search for machining conditions that provide sufficient quality characteristics including dimensional and shape accuracy. We discuss, herewith, thin wall deformations in the horizontal orientation of Inconel 625 nickel alloy samples in cross-sections perpendicular and parallel to the direction of tool feed motion. We measured dimensional and shape accuracy using a 3D optical scanner and also using a coordinate measuring machine to correlate these results. We compared the results obtained by the two methods and obtained the maximum discrepancy of the results equal to around 8%. Samples made with adaptive cylindrical milling had similar values of thin wall deviations, with the smallest deviations observed for the sample made with the tool for high-performance machining using adaptive cylindrical milling. Full article

In an attempt to isolate new probiotic bacteria, two Gram-variable, spore-forming, rod-shaped aerobic bacteria designated as strain A4 and A15 were isolated from the feces of Canada geese (Branta canadensis). Strain A4 was able to grow in high salt levels and exhibited lipase activity, while A15 did not propagate under these conditions. Both were positive for starch hydrolysis, and they inhibited the growth of Staphylococcus aureus. The strains of the 16S rRNA sequence shared only 94% similarity to previously identified Sporosarcina spp. The ANI (78.08%) and AAI (82.35%) between the two strains were less than the species threshold. Searches for the most similar genomes using the Mash/Minhash algorithm showed the nearest genome to strain A4 and A15 as Sporosarcina sp. P13 (distance of 21%) and S. newyorkensis (distance of 17%), respectively. Sporosarcina spp. strains A4 and A15 contain urease genes, and a fibronectin-binding protein gene indicates that these bacteria may bind to eukaryotic cells in host gastrointestinal tracts. Phenotypic and phylogenetic data, along with low dDDH, ANI, and AAI values for strains A4 and A15, indicate these bacteria are two novel isolates of the Sporosarcina genus: Sporosarcina sp. A4 sp. nov., type strain as Sporosarcina cascadiensis and Sporosarcina sp. A15 sp. nov., type strain Sporosarcina obsidiansis. Full article

Object retrieval systems measure the degree of similarity of the shape of 3D models. They search for the elements of the 3D model databases that resemble the query model. In structural bioinformatics, the query model is a protein tertiary/quaternary structure and the objective is to find similarly shaped molecules in the Protein Data Bank. With the ever-growing size of the PDB, a direct atomic coordinate comparison with all its members is impractical. To overcome this problem, the shape of the molecules can be encoded by fixed-length feature vectors. The distance of a protein to the entire PDB can be measured in this low-dimensional domain in linear time. The state-of-the-art approaches utilize Zernike–Canterakis moments for the shape encoding and supply the retrieval process with geometric data of the input structures. The BioZernike descriptors are a standard utility of the PDB since 2020. However, when trying to calculate the ZC moments locally, the issue of the deficiency of libraries readily available for use in custom programs (i.e., without relying on external binaries) is encountered, in particular programs written in Python. Here, a fast and well-documented Python implementation of the Pozo–Koehl algorithm is presented. In contrast to the more popular algorithm by Novotni and Klein, which is based on the voxelized volume, the PK algorithm produces ZC moments directly from the triangular surface meshes of 3D models. In particular, it can accept the molecular surfaces of proteins as its input. In the presented PK-Zernike library, owing to Numba’s just-in-time compilation, a mesh with 50,000 facets is processed by a single thread in a second at the moment order 20. Since this is the first time the PK algorithm is used in structural bioinformatics, it is employed in a novel, simple, but efficient protein structure retrieval pipeline. The elimination of the outlying chain fragments via a fast PCA-based subroutine improves the discrimination ability, allowing for this pipeline to achieve an 0.961 area under the ROC curve in the BioZernike validation suite (0.997 for the assemblies). The correlation between the results of the proposed approach and of the 3D Surfer program attains values up to 0.99. Full article

Aldehyde dehydrogenase-2 (ALDH2) is a crucial enzyme participating in intracellular aldehyde metabolism and is acknowledged as a potential therapeutic target for the treatment of alcohol use disorder and other addictive behaviors. Using previously reported ALDH2 inhibitors of Daidzin, CVT-10216, and CHEMBL114083 as reference molecules, here we perform a ligand-based virtual screening of world-approved drugs via 2D/3D similarity search methods, followed by the assessments of molecular docking, toxicity prediction, molecular simulation, and the molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) analysis. The 2D molecular fingerprinting of ECFP4 and FCFP4 and 3D molecule-shape-based USRCAT methods show good performances in selecting compounds with a strong binding behavior with ALDH2. Three compounds of Zeaxanthin (q = 0), Troglitazone (q = 0), and Sequinavir (q = +1 e) are singled out as potential inhibitors; Zeaxanthin can only be hit via USRCAT. These drugs displayed a stronger binding strength compared to the reported potent inhibitor CVT-10216. Sarizotan (q = +1 e) and Netarsudil (q = 0/+1 e) displayed a strong binding strength with ALDH2 as well, whereas they displayed a shallow penetration into the substrate-binding tunnel of ALDH2 and could not fully occupy it. This likely left a space for substrate binding, and thus they were not ideal inhibitors. The MM–PBSA results indicate that the selected negatively charged compounds from the similarity search and Vina scoring are thermodynamically unfavorable, mainly due to electrostatic repulsion with the receptor (q = −6 e for ALDH2). The electrostatic attraction with positively charged compounds, however, yielded very strong binding results with ALDH2. These findings reveal a deficiency in the modeling of electrostatic interactions (in particular, between charged moieties) in the virtual screening via the 2D/3D similarity search and molecular docking with the Vina scoring system. Full article

As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite. Full article

Risk perception, literacy, communication, narrative, governance, and education are important aspects of emergency and disaster management, preparedness, and planning (EDMPP) as they for example influence and direct EDMPP policies and actions. A thorough understanding of the ‘social aspects of risk is important for EDMPP, especially in relation to marginalized populations who are often overlooked. Technologies are increasingly employed for EDMPP. How these technology applications identify and engage with the ‘social’ of risk in general and the ‘social’ of risk experienced by marginalized populations is important for EDMPP. Equity, diversity, and inclusion (EDI) and similar phrases are employed as policy concepts to improve research, education, and participation in the workplace for marginalized groups such as women, Indigenous peoples, visible/racialized minorities, disabled people, and LGBTQ2S including in workplaces engaging with EDMPP which includes universities. The aim of this scoping review was to generate data that allows for a detailed understanding of the risk related discussions within the EDMPP academic literature as these discussions shape EDMPP policies and actions. The objective of this scoping review study was to map out the engagement with risk, specifically the social aspects of risk, in the EDMPP-focused academic literature with a focus on (a) EDMPP in general, (b) COVID-19, (c) EDMPP and marginalized groups, (d) EDMPP and patients, and (e) EDMPP and technologies (artificial intelligence, machine learning, machine reasoning, algorithm design approaches such as Bayesian belief networks, e-coaching, decision support systems, virtual coaching, automated decision support, e-mentoring, automated dialogue and conversational agents). Using the academic databases SCOPUS, Web of Sciences, and databases accessible under Compendex and EBSCO-HOST and performing hit count frequency searches of online and downloaded abstracts and thematic analysis of downloaded abstracts the study reveals a lack of coverage on the social aspects of risk and engagement with risk concepts such as risk perception, risk governance, risk literacy, risk communication, risk education and risk narrative especially in conjunction with marginalized groups and technologies employed in EDMPP decision support. Our findings suggest many opportunities to further the EDMPP academic inquiry by filling the gaps. Full article

Type 2 diabetes mellitus is one of the most common diseases of the 21st century, caused by a sedentary lifestyle, poor diet, high blood pressure, family history, and obesity. To date, there are no known complete cures for type 2 diabetes. To identify bioactive natural products (NPs) to manage type 2 diabetes, the NPs from the ZINC15 database (ZINC-NPs DB) were screened using a 3D shape similarity search, molecular docking approaches, and ADMETox approaches. Frequently, in silico studies result in asymmetric structures as “hit” molecules. Therefore, the asymmetrical FDA-approved diabetes drugs linagliptin (8-[(3R)-3-aminopiperidin-1-yl]-7-but-2-ynyl-3-methyl-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione), sitagliptin ((3R)-3-amino-1-[3-(trifluoromethyl)-6,8-dihydro-5H-[1,2,4]triazolo[4,3-a]pyrazin-7-yl]-4-(2,4,5-trifluorophenyl)butan-1-one), and alogliptin (2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile) were used as queries to virtually screen the ZINC-NPs DB and detect novel potential dipeptidyl peptidase-4 (DPP-4) inhibitors. The most promising NPs, characterized by the best sets of similarity and ADMETox features, were used during the molecular docking stage. The results highlight that 11 asymmetrical NPs out of 224,205 NPs are potential DPP-4 candidates from natural sources and deserve consideration for further in vitro/in vivo tests. Full article

Several calibration algorithms use spheres as calibration tokens because of the simplicity and uniform shape that a sphere presents across multiple views, along with the simplicity of its construction. Among the alternatives are the use of complex 3D tokens with reference marks, usually complex to build and analyze with the required accuracy; or the search of common features in scene images, with this task being of high complexity too due to perspective changes. Some of the algorithms using spheres rely on the estimation of the sphere center projection obtained from the camera images to proceed. The computation of these projection points from the sphere silhouette on the images is not straightforward because it does not match exactly the silhouette centroid. Thus, several methods have been developed to cope with this calculation. In this work, a simple and fast numerical method adapted to precisely compute the sphere center projection for these algorithms is presented. The benefits over other similar existing methods are its ease of implementation and that it presents less sensibility to segmentation issues. Other possible applications of the proposed method are presented too. Full article