bluuebunny/crossref_metadata_2025 huggingface dataset:
Going with the Flow
Authors: Soliman Yousuf , Padilla Marcel , Gross Oliver , Knöppel Felix , Pinkall Ulrich , Schröder Peter
Published: July 2024

Given a sequence of poses of a body we study the motion resulting when the body is immersed in a (possibly) moving, incompressible medium. With the poses given, say, by an animator, the governing second-order ordinary differential equations are those of a rigid body with time-dependent inertia acted upon by various forces. Some of these forces, like lift and drag, depend on the motion of the body in the surrounding medium. Additionally, the inertia must encode the effect of the medium through its
added mass.
We derive the corresponding dynamics equations which generalize the standard rigid body dynamics equations. All forces are based on local computations using only physical parameters such as mass density. Notably, we approximate the effect of the medium on the body through local computations avoiding any global simulation of the medium. Consequently, the system of equations we must integrate in time is only 6 dimensional (rotation and translation). Our proposed algorithm displays linear complexity and captures intricate natural phenomena that depend on body-fluid interactions.
A Vortex Particle-on-Mesh Method for Soap Film Simulation
Authors: Tao Ningxiao , Ruan Liangwang , Deng Yitong , Zhu Bo , Wang Bin , Chen Baoquan
Published: July 2024

This paper introduces a novel physically-based vortex fluid model for films, aimed at accurately simulating cascading vortical structures on deforming thin films. Central to our approach is a novel mechanism decomposing the film's tangential velocity into circulation and dilatation components. These components are then evolved using a hybrid particle-mesh method, enabling the effective reconstruction of three-dimensional tangential velocities and seamlessly integrating surfactant and thickness dynamics into a unified framework. By coupling with its normal component and surface-tension model, our method is particularly adept at depicting complex interactions between in-plane vortices and out-of-plane physical phenomena, such as gravity, surfactant dynamics, and solid boundary, leading to highly realistic simulations of complex thin-film dynamics, achieving an unprecedented level of vortical details and physical realism.
A Free-Space Diffraction BSDF
Authors: Steinberg Shlomi , Ramamoorthi Ravi , Bitterli Benedikt , Mollazainali Arshiya , D'Eon Eugene , Pharr Matt
Published: July 2024

Free-space diffractions are an optical phenomenon where light appears to "bend" around the geometric edges and corners of scene objects. In this paper we present an efficient method to simulate such effects. We derive an edge-based formulation of Fraunhofer diffraction, which is well suited to the common (triangular) geometric meshes used in computer graphics. Our method dynamically constructs a free-space diffraction BSDF by considering the geometry around the intersection point of a ray of light with an object, and we present an importance sampling strategy for these BSDFs. Our method is unique in requiring only ray tracing to produce free-space diffractions, works with general meshes, requires no geometry preprocessing, and is designed to work with path tracers with a linear rendering equation. We show that we are able to reproduce accurate diffraction lobes, and, in contrast to any existing method, are able to handle complex, real-world geometry. This work serves to connect free-space diffractions to the efficient path tracing tools from computer graphics.
Spatial and Surface Correspondence Field for Interaction Transfer
Authors: Huang Zeyu , Xu Honghao , Huang Haibin , Ma Chongyang , Huang Hui , Hu Ruizhen
Published: July 2024

In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.
Eulerian-Lagrangian Fluid Simulation on Particle Flow Maps
Authors: Zhou Junwei , Chen Duowen , Deng Molin , Deng Yitong , Sun Yuchen , Wang Sinan , Xiong Shiying , Zhu Bo
Published: July 2024

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range advection for incompressible fluid simulation. The foundation of our method is the observation that a particle trajectory generated in a forward simulation naturally embodies a perfect flow map. Centered on this concept, we have developed an Eulerian-Lagrangian framework comprising four essential components: Lagrangian particles for a natural and precise representation of bidirectional flow maps; a dual-scale map representation to accommodate the mapping of various flow quantities; a particle-to-grid interpolation scheme for accurate quantity transfer from particles to grid nodes; and a hybrid impulse-based solver to enforce incompressibility on the grid. The efficacy of PFM has been demonstrated through various simulation scenarios, highlighting the evolution of complex vortical structures and the details of turbulent flows. Notably, compared to NFM, PFM reduces computing time by up to 49 times and memory consumption by up to 41%, while enhancing vorticity preservation as evidenced in various tests like leapfrog, vortex tube, and turbulent flow.
Implicit Swept Volume SDF: Enabling Continuous Collision-Free Trajectory Generation for Arbitrary Shapes
Authors: Wang Jingping , Zhang Tingrui , Zhang Qixuan , Zeng Chuxiao , Yu Jingyi , Xu Chao , Xu Lan , Gao Fei
Published: July 2024

In the field of trajectory generation for objects, ensuring continuous collision-free motion remains a huge challenge, especially for non-convex geometries and complex environments. Previous methods either oversimplify object shapes, which results in a sacrifice of feasible space or rely on discrete sampling, which suffers from the "tunnel effect". To address these limitations, we propose a novel hierarchical trajectory generation pipeline, which utilizes the Swept Volume Signed Distance Field (SVSDF) to guide trajectory optimization for Continuous Collision Avoidance (CCA). Our interdisciplinary approach, blending techniques from graphics and robotics, exhibits outstanding effectiveness in solving this problem. We formulate the computation of the SVSDF as a Generalized Semi-Infinite Programming model, and we solve for the numerical solutions at query points implicitly, thereby eliminating the need for explicit reconstruction of the surface. Our algorithm has been validated in a variety of complex scenarios and applies to robots of various dynamics, including both rigid and deformable shapes. It demonstrates exceptional universality and superior CCA performance compared to typical algorithms. The code will be released at https://github.com/ZJU-FAST-Lab/Implicit-SVSDF-Planner for the benefit of the community.
NeuralTO: Neural Reconstruction and View Synthesis of Translucent Objects
Authors: Cai Yuxiang , Qiu Jiaxiong , Li Zhong , Ren Bo
Published: July 2024

Learning from multi-view images using neural implicit signed distance functions shows impressive performance on 3D Reconstruction of opaque objects. However, existing methods struggle to reconstruct accurate geometry when applied to translucent objects due to the non-negligible bias in their rendering function. To address the inaccuracies in the existing model, we have reparameterized the density function of the neural radiance field by incorporating an estimated constant extinction coefficient. This modification forms the basis of our innovative framework, which is geared towards highfidelity surface reconstruction and the novel-view synthesis of translucent objects. Our framework contains two stages. In the reconstruction stage, we introduce a novel weight function to achieve accurate surface geometry reconstruction. Following the recovery of geometry, the second phase involves learning the distinct scattering properties of the participating media to enhance rendering. A comprehensive dataset, comprising both synthetic and real translucent objects, has been built for conducting extensive experiments. Experiments reveal that our method outperforms existing approaches in terms of reconstruction and novel-view synthesis.
Contact detection between curved fibres: high order makes a difference
Authors: Crespel Octave , Hohnadel Emile , Metivet Thibaut , Bertails-Descoubes Florence
Published: July 2024

Computer Graphics has a long history in the design of effective algorithms for handling contact and friction between solid objects. For the sake of simplicity and versatility, most methods rely on low-order primitives such as line segments or triangles, both for the detection and the response stages. In this paper we carefully analyse, in the case of fibre systems, the impact of such choices on the retrieved contact forces. We highlight the presence of artifacts in the force response that are tightly related to the low-order geometry used for contact detection. Our analysis draws upon thorough comparisons between the high-order super-helix model and the low-order discrete elastic rod model. These reveal that when coupled to a low-order, segment-based detection scheme, both models yield spurious jumps in the contact force profile. Moreover, these artifacts are shown to be all the more visible as the geometry of fibres at contact is curved. In order to remove such artifacts we develop an accurate high-order detection scheme between two smooth curves, which relies on an efficient adaptive pruning strategy. We use this algorithm to detect contact between super-helices at high precision, allowing us to recover, in the range of wavy to highly curly fibres, much smoother force profiles during sliding motion than with a classical segment-based strategy. Furthermore, we show that our approach offers better scaling properties in terms of efficiency vs. precision compared to segment-based approaches, making it attractive for applications where accurate and reliable forces are desired. Finally, we demonstrate the robustness and accuracy of our fully high-order approach on a challenging hair combing scenario.
Spin-It Faster: Quadrics Solve All Topology Optimization Problems That Depend Only On Mass Moments
Authors: Hafner Christian , Ly Mickaël , Wojtan Chris
Published: July 2024

The behavior of a rigid body primarily depends on its mass moments, which consist of the mass, center of mass, and moments of inertia. It is possible to manipulate these quantities without altering the geometric appearance of an object by introducing cavities in its interior. Algorithms that find cavities of suitable shapes and sizes have enabled the computational design of spinning tops, yo-yos, wheels, buoys, and statically balanced objects. Previous work is based, for example, on topology optimization on voxel grids, which introduces a large number of optimization variables and box constraints, or offset surface computation, which cannot guarantee that solutions to a feasible problem will always be found.

In this work, we provide a mathematical analysis of constrained topology optimization problems that depend only on mass moments. This class of problems covers, among others, all applications mentioned above. Our main result is to show that no matter the outer shape of the rigid body to be optimized or the optimization objective and constraints considered, the optimal solution always features a quadric-shaped interface between material and cavities. This proves that optimal interfaces are always ellipsoids, hyperboloids, paraboloids, or one of a few degenerate cases, such as planes.

This insight lets us replace a difficult topology optimization problem with a provably equivalent non-linear equation system in a small number (<10) of variables, which represent the coefficients of the quadric. This system can be solved in a few seconds for most examples, provides insights into the geometric structure of many specific applications, and lets us describe their solution properties. Finally, our method integrates seamlessly into modern fabrication workflows because our solutions are analytical surfaces that are native to the CAD domain.
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Authors: Maesumi Arman , Hu Dylan , Saripalli Krishi , Kim Vladimir , Fisher Matthew , Pirk Soeren , Ritchie Daniel
Published: July 2024

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters plus a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance. Open-sourced materials can be found at https://armanmaesumi.github.io/onenoise/
Terrain Amplification using Multi Scale Erosion
Authors: Schott Hugo , Galin Eric , Guérin Eric , Paris Axel , Peytavie Adrien
Published: July 2024

Modeling high-resolution terrains is a perennial challenge in the creation of virtual worlds. In this paper, we focus on the amplification of a low-resolution input terrain into a high-resolution, hydrologically consistent terrain featuring complex patterns by a multi-scale approach. Our framework combines the best of both worlds, relying on physics-inspired erosion models producing consistent erosion landmarks and introducing control at different scales, thus bridging the gap between physics-based erosion simulations and multi-scale procedural modeling. The method uses a fast and accurate approximation of different simulations, including thermal, stream power erosion and deposition performed at different scales to obtain a range of effects. Our approach provides landscape designers with tools for amplifying mountain ranges and valleys with consistent details.
Ray Tracing Harmonic Functions
Authors: Gillespie Mark , Yang Denise , Botsch Mario , Crane Keenan
Published: July 2024

Sphere tracing
is a fast and high-quality method for visualizing surfaces encoded by signed distance functions (SDFs). We introduce a similar method for a completely different class of surfaces encoded by
harmonic functions
, opening up rich new possibilities for visual computing. Our starting point is similar in spirit to sphere tracing: using conservative
Harnack bounds
on the growth of harmonic functions, we develop a
Harnack tracing
algorithm for visualizing level sets of harmonic functions, including those that are angle-valued and exhibit singularities. The method takes much larger steps than naïve ray marching, avoids numerical issues common to generic root finding methods and, like sphere tracing, needs only perform pointwise evaluation of the function at each step. For many use cases, the method is fast enough to run real time in a shader program. We use it to visualize smooth surfaces directly from point clouds (via Poisson surface reconstruction) or polygon soup (via generalized winding numbers) without linear solves or mesh extraction. We also use it to visualize nonplanar polygons (possibly with holes), surfaces from architectural geometry, mesh "exoskeletons", and key mathematical objects including knots, links, spherical harmonics, and Riemann surfaces. Finally we show that, at least in theory, Harnack tracing provides an alternative mechanism for visualizing arbitrary implicit surfaces.
Biharmonic Coordinates and their Derivatives for Triangular 3D Cages
Authors: Thiery Jean-Marc , Michel Élie , Chen Jiong
Published: July 2024

As a natural extension to the harmonic coordinates, the biharmonic coordinates have been found superior for planar shape and image manipulation with an enriched deformation space. However, the 3D biharmonic coordinates and their derivatives have remained unexplored. In this work, we derive closed-form expressions for biharmonic coordinates and their derivatives for 3D triangular cages. The core of our derivation lies in computing the closed-form expressions for the integral of the Euclidean distance over a triangle
and
its derivatives. The derived 3D biharmonic coordinates not only fill a missing component in methods of generalized barycentric coordinates but also pave the way for various interesting applications in practice, including producing a family of biharmonic deformations, solving variational shape deformations, and even unlocking the closed-form expressions for recently-introduced Somigliana coordinates for both fast and accurate evaluations.
DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models
Authors: Sun Zhiyao , Lv Tian , Ye Sheng , Lin Matthieu , Sheng Jenny , Wen Yu-Hui , Yu Minjing , Liu Yong-Jin
Published: July 2024

The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion. However, existing methods either employ a deterministic model for speech-to-motion mapping or encode the style using a one-hot encoding scheme. Notably, the one-hot encoding approach fails to capture the complexity of the style and thus limits generalization ability. In this paper, we propose DiffPoseTalk, a generative framework based on the diffusion model combined with a style encoder that extracts style embeddings from short reference videos. During inference, we employ classifier-free guidance to guide the generation process based on the speech and style. In particular, our style includes the generation of head poses, thereby enhancing user perception. Additionally, we address the shortage of scanned 3D talking face data by training our model on reconstructed 3DMM parameters from a high-quality, in-the-wild audio-visual dataset. Extensive experiments and user study demonstrate that our approach outperforms state-of-the-art methods. The code and dataset are at https://diffposetalk.github.io.
Split-Aperture 2-in-1 Computational Cameras
Authors: Shi Zheng , Chugunov Ilya , Bijelic Mario , Côté Geoffroi , Yeom Jiwoon , Fu Qiang , Amata Hadi , Heidrich Wolfgang , Heide Felix
Published: July 2024

While conventional cameras offer versatility for applications ranging from amateur photography to autonomous driving, computational cameras allow for domain-specific adaption. Cameras with co-designed optics and image processing algorithms enable high-dynamic-range image recovery, depth estimation, and hyperspectral imaging through optically encoding scene information that is otherwise undetected by conventional cameras. However, this optical encoding creates a challenging inverse reconstruction problem for conventional image recovery, and often lowers the overall photographic quality. Thus computational cameras with domain-specific optics have only been adopted in a few specialized applications where the captured information cannot be acquired in other ways. In this work, we investigate a method that combines two optical systems into one to tackle this challenge. We split the aperture of a conventional camera into two halves: one which applies an application-specific modulation to the incident light via a diffractive optical element to produce a coded image capture, and one which applies no modulation to produce a conventional image capture. Co-designing the phase modulation of the split aperture with a dual-pixel sensor allows us to simultaneously capture these coded and uncoded images without increasing physical or computational footprint. With an uncoded conventional image alongside the optically coded image in hand, we investigate image reconstruction methods that are conditioned on the conventional image, making it possible to eliminate artifacts and compute costs that existing methods struggle with. We assess the proposed method with 2-in-1 cameras for optical high-dynamic-range reconstruction, monocular depth estimation, and hyperspectral imaging, comparing favorably to all tested methods in all applications.
fVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial Intelligence
Authors: Williams Francis , Huang Jiahui , Swartz Jonathan , Klar Gergely , Thakkar Vijay , Cong Matthew , Ren Xuanchi , Li Ruilong , Fuji-Tsang Clement , Fidler Sanja , Sifakis Eftychios , Museth Ken
Published: July 2024

We present
f
VDB, a novel GPU-optimized framework for deep learning on large-scale 3D data.
f
VDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc.
f
VDB simultaneously provides a
much larger
feature set (primitives and operators) than established frameworks with no loss in efficiency: our operators match or exceed the performance of other frameworks with narrower scope. Furthermore,
f
VDB can process datasets with much larger footprint and spatial resolution than prior works, while providing a competitive memory footprint on small inputs. To achieve this combination of versatility and performance,
f
VDB relies on a single novel VDB index grid acceleration structure paired with several key innovations including GPU accelerated sparse grid construction, convolution using tensorcores, fast ray tracing kernels using a Hierarchical Digital Differential Analyzer algorithm (HDDA), and jagged tensors. Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines, and we demonstrate its effectiveness on a number of representative tasks such as large-scale point-cloud segmentation, high resolution 3D generative modeling, unbounded scale Neural Radiance Fields, and large-scale point cloud reconstruction.
Interactive Design of Stylized Walking Gaits for Robotic Characters
Authors: Hopkins Michael A. , Wiedebach Georg , Cesare Kyle , Bishop Jared , Knoop Espen , Bächer Moritz
Published: July 2024

Procedural animation has seen widespread use in the design of expressive walking gaits for virtual characters. While similar tools could breathe life into robotic characters, existing techniques are largely unaware of the kinematic and dynamic constraints imposed by physical robots. In this paper, we propose a system for the artist-directed authoring of stylized bipedal walking gaits, tailored for execution on robotic characters. The artist interfaces with an interactive editing tool that generates the desired character motion in realtime, either on the physical or simulated robot, using a model-based control stack. Each walking style is encoded as a set of sample parameters which are translated into whole-body reference trajectories using the proposed procedural animation technique. In order to generalize the stylized gait over a continuous range of input velocities, we employ a phase-space blending strategy that interpolates a set of example walk cycles authored by the animator while preserving contact constraints. To demonstrate the utility of our approach, we animate gaits for a custom, free-walking robotic character, and show, with two additional in-simulation examples, how our procedural animation technique generalizes to bipeds with different degrees of freedom, proportions, and mass distributions.
Theory of Human Tetrachromatic Color Experience and Printing
Authors: Lee Jessica , Jennings Nicholas , Srivastava Varun , Ng Ren
Published: July 2024

Genetic studies indicate that more than 50% of women are genetically tetrachromatic, expressing four distinct types of color photoreceptors (cone cells) in the retina. At least one functional tetrachromat has been identified in laboratory tests. We hypothesize that there is a large latent group in the population capable of fundamentally richer color experience, but we are not yet aware of this group because of a lack of tetrachromatic colors in the visual environment. This paper develops theory and engineering practice for fabricating tetrachromatic colors and potentially identifying tetrachromatic color vision in the wild. First, we apply general
d
-dimensional color theory to derive and compute all the key color structures of human tetrachromacy for the first time, including its 4D space of possible object colors, 3D space of chromaticities, and yielding a predicted 2D sphere of tetrachromatic hues. We compare this predicted hue sphere to the familiar hue circle of trichromatic color, extending the theory to predict how the higher dimensional topology produces an expanded color experience for tetrachromats. Second, we derive the four reflectance functions for the ideal tetrachromatic inkset, analogous to the well-known CMY printing basis for trichromacy. Third, we develop a method for prototyping tetrachromatic printers using a library of fountain pen inks and a multi-pass inkjet printing platform. Fourth, we generalize existing color tests - sensitive hue ordering tests and rapid isochromatic plate screening tests - to higher-dimensional vision, and prototype variants of these tests for identifying and characterizing tetrachromacy in the wild.
Probabilistic Craft- Materialization of generated images using digital and traditional craft
Authors: Elran Sharan R. , Zoran Amit R.
Published: July 2024

This paper explores innovative integration of generative AI with digital and traditional craft practice. It presents a theoretical analysis of the nature of these new tools and interactions with digital agents as well as a practical workflow utilizing image generators for artistic creation, emphasizing the transformation of digital images into tangible objects through digital and traditional craft techniques. The paper presents three projects in ceramic, woodcut prints, and relief - each illustrating an optional material interpretation of generated images. The paper shows how materialization re-asserts the human role in this new collaborative environment and how the tangible object changes the status of the generated image from a random sample into a critical part of a work of art.
Floating on the Boundary: Perceptions of Reality in a Half-Digital Half-Physical Bunny
Authors: Yokoyama Takumi , Izumi Kazuya , Fushimi Tatsuki , Ochiai Yoichi
Published: July 2024

In a realm where virtual and real worlds converge, we present "Float" as a digital-physical mixed entity and the findings through the preliminary study. This research has uncovered the "Physical-Digital Continuous Illusion"---a phenomenon in which users perceive impressions of objects existing across different spaces, building on reports of digital presence that seem to be "naturally pulled" towards physical presence. This exploration within our work critically engages with culture and technology, probing into how individuals discern reality. The results offer a transformative milestone in the digital-physical interaction narrative.
A Concert in a Vanished Church: Contextualizing Peace Island's Auditory History with Modern Technology
Authors: Hui Tak-Cheung , Kuo Yu-Chia
Published: July 2024

This paper presents an interdisciplinary project that merges archaeoacoustics with live music performance, aiming to offer a sound interpretation of San Salvador City's history in Taiwan through the fusion of historical research, archaeological discoveries, and the use of contemporary music technology. Central to the project is Ashes to Ashes, a musical performance that integrates live instruments, electronic music, and sound elements derived from 16th and 17th-century artifacts, including Dominican chants and indigenous storytelling. Employing techniques like 3D printing of instruments, physical modeling synthesis, and data sonification, the project not only resurrects forgotten sounds but also connects them with modern auditory experiences. This approach transcends traditional boundaries between historical conservation and artistic innovation, emphasizing the role of interdisciplinary collaboration in enriching our understanding of cultural heritage, particularly in East Asia.
Oyster Restoration to Recover Ecosystem Services
Authors: Smith Rachel S. , Pruett Jessica L.
Published: July 2024

Oyster reef loss represents one of the most dramatic declines of a foundation species worldwide. Oysters provide valuable ecosystem services (ES), including habitat provisioning, water filtration, and shoreline protection. Since the 1990s, a global community of science and practice has organized around oyster restoration with the goal of restoring these valuable services. We highlight ES-based approaches throughout the restoration process, consider applications of emerging technologies, and review knowledge gaps about the life histories and ES provisioning of underrepresented species. Climate change will increasingly affect oyster populations, and we assess how restoration practices can adapt to these changes. Considering ES throughout the restoration process supports adaptive management. For a rapidly growing restoration practice, we highlight the importance of early community engagement, long-term monitoring, and adapting actions to local conditions to achieve desired outcomes.
Sex differences and role of lysyl oxidase-like 2 in angiotensin II-induced hypertension in mice
Authors: Wang Huilei , Martinez Yus Marta , Brady Travis , Choi Rira , Nandakumar Kavitha , Smith Logan , Jang Rosie , Wodu Bulouere Princess , Almodiel Jose Diego , Stoddart Laila , Kim Deok-Ho , Steppan Jochen , Santhanam Lakshmi
Published: July 2024

We examined the effect of sex on the evolution of angiotensin II (ANG II)-induced hypertension and the role of lysyl oxidase-like 2 (LOXL2), an enzyme that catalyzes matrix cross linking. While ANG II led to hypertension and worsening vascular reactivity in both sexes, aortic remodeling and stiffening occurred only in males. LOXL2 depletion improved outcomes in males but not females. Thus males and females exhibit a distinct etiology of hypertension and LOXL2 is an effective target in males.
Sigmoidal heart rate response pattern during exercise in patients with chronic heart failure
Authors: Izumida Toshihide , Imamura Teruhiko , Kataoka Naoya , Kinugawa Koichiro
Published: July 2024

Heart rate response patterns can be classified into two groups among patients with chronic heart failure reaching maximal exertion: sigmoidal and exponential.
A General Maximum Principle for Discrete Fractional Stochastic Control System of Mean‐Field Type
Authors: Li Zheng , Chen Fei , Li Ning , Wu Di , Yu Xiangyue
Published: July 2024

In this paper, we investigate a general maximum principle for discrete fractional stochastic difference system of mean‐field type. The admissible control domain is nonconvex. We give Malliavin calculus for discrete‐time case to deal with the fractional terms. The maximum principle of general type is derived by classical variation and linear operator methods. In addition, a linear‐quadratic problem is solved to illustrate the main result and we also figure out a numerical result in this case.
Evaluation of Optical Coherence Tomography Angiography in Degenerative and Tractional Lamellar Macular Hole
Authors: Polat Gültekin Burcu , Kalaycı Defne
Published: July 2024

Background. This study aims to evaluate the optical coherence tomography angiography (OCTA) findings in cases with degenerative and tractional lamellar macular holes (LMH). Methods. Two subtypes of LMH cases were included. Seventeen patients had the degenerative subtype, whereas 18 patients had the tractional subtype of LMH. Twenty healthy individuals were enrolled as the control group. The foveal avascular zone (FAZ) and retinal vascular densities in the superficial, deep capillary, and choriocapillary plexuses were analyzed and compared with fellow eyes and healthy controls using OCTA. Results. The mean FAZ area was wider in the degenerative subtype (0.33 ± 0.14 mm2) compared to the tractional subtype (0.24 ± 0.10 mm2) (p = 0.04) and control eyes (0.26 ± 0.10 mm2) (p = 0.03). Foveal vessel densities in the superficial and deep capillary plexuses were lower in the degenerative group than in the tractional group, (21.7 ± 9.8% vs. 26.8 ± 6.9%, p = 0.01 and 28.5 ± 5.1% vs. 36.9 ± 6.2%, p = 0.01). Choriocapillary vascular density in the parafoveal area was also lower in degenerative lamellar macular holes compared to the tractional group (60.4 ± 4.7% vs. 63.7 ± 3.9%, p = 0.03). Compared to control eyes, eyes with degenerative and tractional LMH showed lower vessel densities in the parafoveal and perifoveal areas of the SCP, DCP, and all layers of CC (p < 0.05). In the foveal area, the LMH groups showed higher foveal vascular density (FVD) in the SCP than control eyes, while in the DCP, FVD was lower in the degenerative LMH eyes relative to the other groups. Conclusion. The finding of microvascular changes between degenerative and tractional LMH subtypes highlights their distinct pathologies and supports recent changes in the classification and terminology of this macular condition.
Insights into the Correlation between Toll‐Like Receptor 2 Polymorphism and HBV‐Related Disease Progression and Occurrence of Hepatocellular Carcinoma: A Case‐Control Study in Egyptian Patients
Authors: Elabd Naglaa S. , Helal Marwa L. , Elkhayat Mohsen , Abd-ElKhalek Heba Kamal , Ahmed Doaa M. , El-Shemy Asmaa M. , Elsaadawy Yara S. , Abdelmoneum Rasha A. , AboShabaan Hind S. , Seddik Randa M.
Published: July 2024

Background and Aim. Hepatitis B virus (HBV) infection causes a variety of clinical manifestations, including liver cirrhosis and hepatocellular carcinoma (HCC). Toll‐like receptors (TLRs) have crucial functions in immune and inflammatory control. Therefore, this study highlights the impact of TLR2 gene polymorphism on the progression of HBV‐linked liver diseases (liver cirrhosis and HCC). Methods. In total, 170 chronic HBV patients and 50 healthy controls of comparable age and gender were included in this case‐control study. Clinical, laboratory, and imaging evaluations were conducted. ELISA was used to determine serum IL‐6 levels, and TLR2 (rs3804099) genotyping allelic discrimination assay was performed using real‐time PCR. Results. IL‐6 values were significantly higher in the HCC group, followed by the cirrhotic group, than those in chronic hepatitis and control groups (p < 0.001), with a significant correlation with disease activity and progression parameters. TRL2 homozygous TT was the most frequent in the control group, but the CC genotype was significantly more prevalent in the HCC group than that in the other groups. Furthermore, the CC genetic variant was associated with higher levels of IL‐6 and viral load in all HBV patients, whereas the TT genotype was associated with larger tumor size. Multivariate regression analysis demonstrated that in chronic HBV patients, viral load and TRL2 polymorphism are independent risk factors associated with the progression from chronic hepatitis to liver cirrhosis and to HCC. Similarly, the HBV viral load (p = 0.03, OR = 2.45, and 95% CI: 1.69–3.65), IL‐6 levels (p = 0.04, OR = 3.45, and 95% CI: 2.01–6.9), and TRL2 variants (p = 0.01, OR = 4.25, and 95% CI: 2.14–13.5) are independent risk factors associated with disease progression from cirrhosis to HCC. Conclusion. In chronic HBV patients, TRL2 polymorphism and higher IL‐6 levels were positively correlated with a higher likelihood of HCC and chronic hepatitis B disease activity and progression.
Home Staff Perspectives on Implementation of the Community Homes for Opportunity (CHO) Program in Southwestern Ontario
Authors: Forchuk Cheryl , Gyamfi Sebastian , Hassan Heba , Lucyk Bryanna , Booth Richard
Published: July 2024

Introduction: Literature has established a bidirectional relationship between housing stability and mental health. Globally, there is a lack of affordable, safe, and appropriate housing for people with mental illness, and Canada is no exception. The current study explored the views of home staff on their experiences regarding the process of modernizing group homes (Community Homes for Opportunity [CHO]). It provided recommendations for further improvement of the implementation process.

Method: We used ethnographic techniques to purposefully recruit 51 home staff from 28 group homes in Southwest Ontario, Canada. Focus group discussions were conducted at two‐time points (baseline: spring 2018 and postimplementation: winter 2019).

Results: Data analysis produced four major themes. These include a general impression of the modernization process, facilitators, challenges to the implementation, and suggestions for improving the modernization program.

Conclusion: Group homes such as CHO positively impact the well‐being and quality of life of persons with mental health and addiction problems while enhancing their independence and social integration for improved recovery outcomes.
Neem Essential Oil as an Antifungal Agent against Phyllosticta citricarpa
Authors: Schuch Joyce Maria , Mendes Carolina Rosai , Cardoso Guilherme Lopes , André da Veiga Lima Rosa Costamilan Carlos , Matos Lopes Paulo Renato , Montagnolli Renato Nallin , Dilarri Guilherme , Bidoia Ederio Dino
Published: July 2024

The fungus Phyllosticta citricarpa is a quarantine phytopathogen responsible for causing citrus black spot (CBS) disease. To export fruits to CBS‐free countries, they must undergo a sanitation process to ensure disease control. In this study, neem essential oil (NEO) was tested against P. citricarpa for the first time as an alternative sanitizer. In vitro experiments were conducted to determine the inhibition concentration of NEO for P. citricarpa, and the mode of action of the essential oil was evaluated. In vivo assays were performed to simulate the sanitization process used in packinghouses. NEO was characterized by GC‐MS/MS. The results revealed that NEO at 100 μL·mL−1 exhibited a similar inhibitory effect as copper oxychloride, suppressing 89.68 ± 1.14% of fungal mycelium growth. Fluorescence microscopy experiments demonstrated that NEO functions by disrupting the cytoplasmic membrane of fungal hyphae, leading to their death within 30 minutes of contact with NEO. GC‐MS/MS characterization revealed a high presence of phenolic compounds, which serve as the primary antifungal agents responsible for the action against fungal hyphae. In vivo assays showed that NEO at 100 μL·mL−1 also reduced microorganisms (CFU mL−1) by 93.00 ± 3.88% compared to the negative control. Overall, the results demonstrate that NEO can effectively serve as an alternative sanitizer against P. citricarpa in citrus packinghouses. Our findings allow future studies to explore the use of NEO for sanitizing other fruits and combating different phytopathogens to broaden its potential application in fruit sanitation for export.
A Targetable Secreted Neural Protein Drives Pancreatic Cancer Metastatic Colonization and HIF1α Nuclear Retention
Authors: Yamaguchi Norihiro , Wu Y. Gloria , Ravetch Ethan , Takahashi Mai , Khan Abdul G. , Hayashi Akimasa , Mei Wenbin , Hsu Dennis , Umeda Shigeaki , de Stanchina Elisa , Lorenz Ivo C. , Iacobuzio-Donahue Christine A. , Tavazoie Sohail F.
Published: July 2024

Pancreatic ductal adenocarcinoma (PDAC) is an increasingly diagnosed cancer that kills 90% of afflicted patients, with most patients receiving palliative chemotherapy. We identified neuronal pentraxin 1 (NPTX1) as a cancer-secreted protein that becomes overexpressed in human and murine PDAC cells during metastatic progression and identified adhesion molecule with Ig-like domain 2 (AMIGO2) as its receptor. Molecular, genetic, biochemical, and pharmacologic experiments revealed that secreted NPTX1 acts cell-autonomously on the AMIGO2 receptor to drive PDAC metastatic colonization of the liver—the primary site of PDAC metastasis. NPTX1–AMIGO2 signaling enhanced hypoxic growth and was critically required for hypoxia-inducible factor-1α (HIF1α) nuclear retention and function. NPTX1 is overexpressed in human PDAC tumors and upregulated in liver metastases. Therapeutic targeting of NPTX1 with a high-affinity monoclonal antibody substantially reduced PDAC liver metastatic colonization. We thus identify NPTX1–AMIGO2 as druggable critical upstream regulators of the HIF1α hypoxic response in PDAC.

Significance: We identified the NPTX1–AMIGO2 axis as a regulatory mechanism upstream of HIF1α-driven hypoxia response that promotes PDAC liver metastasis. Therapeutic NPTX1 targeting outperformed a common chemotherapy regimen in inhibiting liver metastasis and suppressed primary tumor growth in preclinical models, revealing a novel therapeutic strategy targeting hypoxic response in PDAC.
Ensemble neural networks with input optimization for flood forecasting
Authors: Mohd Khairudin Nazli , Mustapha Norwati , Mohd Aris Teh Noranis , Zolkepli Maslina
Published: July 2024

Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. The aim is to highlight the improvement on accuracy of the forecast. Prior to the development of such ensemble model, data are optimized in two steps which are decomposed it using discrete wavelet transform (DWT) to reduce variations in the rainfall series and selecting dominant features using entropy called mutual information (MI) for the model. The result of the experiments indicates that ensemble neural networks model based on the data decomposition and entropy feature selection has outperformed individually executed forecast model in term of RMSE, MSE and NSE. This study proved that the proposed method has reduce the data variance and provide better forecasting with minimal error. With minimal forecast error the generalization of the model is improved.
Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models
Authors: Sayed Ismail Sharifah Noor Masidayu , Abdul Razak Siti Fatimah , Ab. Aziz Nor Azlina
Published: July 2024

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field. Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain. This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals. Forty-three articles were chosen from Scopus and Google Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning. The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm. alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles. Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals. It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases.
The potential of light fidelity in smart home automation
Authors: Aydin Hakan , Aydın Gülsüm Zeynep Gürkaş , Aydın Muhammed Ali
Published: July 2024

Light fidelity (Li-Fi) is a pioneering optical wireless communication (OWC) technology that utilizes visible light for wireless data transmission. Since its inception in a TED global talk by Professor Harald Haas in 2011, Li-Fi has captured significant attention in the research community. Smart home automation systems (SHAs) leverage internet of things (IoT) technology to remotely manage and automate various home devices and systems. Li-Fi technology has the potential to enable remote control of devices such as lighting, air conditioning, music systems, security cameras, and door locks within SHAs. This study presents Li-Fi-IoT, a Li-Fi-based system designed for efficient and secure IoT device management in SHAs. A series of experiments demonstrates the system's potential in IoT device control using Li-Fi technology. The research findings highlight the substantial improvement in data transfer speed, energy efficiency, and data security that Li-Fi technology can bring to SHAs.
Enhanced building footprint extraction from satellite imagery using Mask R-CNN and PointRend
Authors: NourEldeen Ahmed , E. Wahed Mohamed
Published: July 2024

The extraction of building footprints from aerial photos and satellite imagery plays a crucial role in change detection, urban development, and detecting encroachments on agricultural land. Deep neural networks offer the capability of extracting features and provide accurate methods for detecting and extracting building footprints from satellite imagery. Image segmentation, the process of dividing an image into coherent parts, can be accomplished using two types: semantic segmentation and instance segmentation. Convolutional neural networks (CNN) are commonly used for both instance and semantic segmentation tasks. In this paper, we propose a hybrid approach to extracting building footprints from low-resolution satellite imagery using instance segmentation techniques. Our analysis demonstrates that the mask region-based CNN (R-CNN) architecture with a ResNet-34 backbone and PointRend head to improve the bounding-boxes and mask prediction achieves the highest performance, as evidenced by various metrics, including an average precision (AP) score of 83.39% and an F-1 score of 85.71%. This approach holds promise for developing automated tools to process satellite imagery, benefiting fields such as agriculture, land use monitoring, and disaster response.