bluuebunny/crossref_metadata_2025 huggingface dataset:
ColorfulCurves: Palette-Aware Lightness Control and Color Editing via Sparse Optimization
Authors: Chao Cheng-Kang Ted , Klein Jason , Tan Jianchao , Echevarria Jose , Gingold Yotam
Published: July 2023

Color editing in images often consists of two main tasks: changing hue and saturation, and editing lightness or tone curves. State-of-the-art palette-based recoloring approaches entangle these two tasks. A user's only lightness control is changing the lightness of individual palette colors. This is inferior to state-of-the-art commercial software, where lightness editing is based on flexible tone curves that remap lightness. However, tone curves are only provided globally or per color channel (e.g., RGB). They are unrelated to the image content. Neither tone curves nor palette-based approaches support direct image-space edits---changing a
specific
pixel to a desired hue, saturation, and lightness.
ColorfulCurves
solves both of these problems by uniting palette-based and tone curve editing. In
ColorfulCurves
, users directly edit palette colors' hue and saturation, per-palette tone curves, or image pixels (hue, saturation, and lightness).
ColorfulCurves
solves an
L
2,1
optimization problem in real-time to find a sparse edit that satisfies all user constraints. Our expert study found overwhelming support for
ColorfulCurves
over experts' preferred tools.
Random-Access Neural Compression of Material Textures
Authors: Vaidyanathan Karthik , Salvi Marco , Wronski Bartlomiej , Akenine-Moller Tomas , Ebelin Pontus , Lefohn Aaron
Published: July 2023

The continuous advancement of photorealism in rendering is accompanied by a growth in texture data and, consequently, increasing storage and memory demands. To address this issue, we propose a novel neural compression technique specifically designed for material textures. We unlock two more levels of detail, i.e., 16× more texels, using low bitrate compression, with image quality that is better than advanced image compression techniques, such as AVIF and JPEG XL.

At the same time, our method allows on-demand, real-time decompression with random access similar to block texture compression on GPUs, enabling compression on disk and memory. The key idea behind our approach is compressing multiple material textures and their mipmap chains together, and using a small neural network, that is optimized for each material, to decompress them. Finally, we use a custom training implementation to achieve practical compression speeds, whose performance surpasses that of general frameworks, like PyTorch, by an order of magnitude.
HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
Authors: Işık Mustafa , Rünz Martin , Georgopoulos Markos , Khakhulin Taras , Starck Jonathan , Agapito Lourdes , Nießner Matthias
Published: July 2023

Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF
1
, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints. Our novel representation acts as a dynamic video encoding that captures fine details at high compression rates by factorizing space-time into a temporal matrix-vector decomposition. This allows us to obtain temporally coherent reconstructions of human actors for long sequences, while representing high-resolution details even in the context of challenging motion. While most research focuses on synthesizing at resolutions of 4MP or lower, we address the challenge of operating at 12MP. To this end, we introduce ActorsHQ, a novel multi-view dataset that provides 12MP footage from 160 cameras for 16 sequences with high-fidelity, per-frame mesh reconstructions
2
. We demonstrate challenges that emerge from using such high-resolution data and show that our newly introduced HumanRF effectively leverages this data, making a significant step towards production-level quality novel view synthesis.
Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
Authors: Shen Tianchang , Munkberg Jacob , Hasselgren Jon , Yin Kangxue , Wang Zian , Chen Wenzheng , Gojcic Zan , Fidler Sanja , Sharp Nicholas , Gao Jun
Published: July 2023

This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics. Existing implementations adapt classic isosurface extraction algorithms like Marching Cubes or Dual Contouring; these techniques were designed to extract meshes from fixed, known fields, and in the optimization setting they lack the degrees of freedom to represent high-quality feature-preserving meshes, or suffer from numerical instabilities. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives. Our main insight is to introduce additional carefully-chosen parameters into the representation, which allow local
flexible
adjustments to the extracted mesh geometry and connectivity. These parameters are updated along with the underlying scalar field via automatic differentiation when optimizing for a downstream task. We base our extraction scheme on Dual Marching Cubes for improved topological properties, and present extensions to optionally generate tetrahedral and hierarchically-adaptive meshes. Extensive experiments validate FlexiCubes on both synthetic benchmarks and real-world applications, showing that it offers significant improvements in mesh quality and geometric fidelity.
Effect-based Multi-viewer Caching for Cloud-native Rendering
Authors: Weinrauch Alexander , Tatzgern Wolfgang , Stadlbauer Pascal , Crickx Alexis , Hladky Jozef , Coomans Arno , Winter Martin , Mueller Joerg H. , Steinberger Markus
Published: July 2023

With cloud computing becoming ubiquitous, it appears as virtually everything can be offered
as-a-service.
However, real-time rendering in the cloud forms a notable exception, where the cloud adoption stops at running individual game instances in compute centers. In this paper, we explore whether a cloud-native rendering architecture is viable and scales to multi-client rendering scenarios. To this end, we propose world-space and on-surface caches to share rendering computations among viewers placed in the same virtual world. We discuss how caches can be utilized on an effect-basis and demonstrate that a large amount of computations can be saved as the number of viewers in a scene increases. Caches can easily be set up for various effects, including ambient occlusion, direct illumination, and diffuse global illumination. Our results underline that the image quality using cached rendering is on par with screen-space rendering and due to its simplicity and inherent coherence, cached rendering may even have advantages in single viewer setups. Analyzing the runtime and communication costs, we show that cached rendering is already viable in multi-GPU systems. Building on top of our research, cloud-native rendering may be just around the corner.
P2M: A Fast Solver for Querying Distance from Point to Mesh Surface
Authors: Zong Chen , Xu Jiacheng , Song Jiantao , Chen Shuangmin , Xin Shiqing , Wang Wenping , Tu Changhe
Published: July 2023

Most of the existing point-to-mesh distance query solvers, such as Proximity Query Package (PQP), Embree and Fast Closest Point Query (FCPW), are based on bounding volume hierarchy (BVH). The hierarchical organizational structure enables one to eliminate the vast majority of triangles that do not help find the closest point. In this paper, we develop a totally different algorithmic paradigm, named
P2M
, to speed up point-to-mesh distance queries. Our original intention is to precompute a KD tree (KDT) of mesh vertices to approximately encode the geometry of a mesh surface containing vertices, edges and faces. However, it is very likely that the closest primitive to the query point is an edge
e
(resp., a face
f
), but the KDT reports a mesh vertex υ instead. We call υ an
interceptor
of
e
(resp.,
f
). The main contribution of this paper is to invent a simple yet effective interception inspection rule and an efficient flooding interception inspection algorithm for quickly finding out all the interception pairs. Once the KDT and the interception table are precomputed, the query stage proceeds by first searching the KDT and then looking up the interception table to retrieve the closest geometric primitive. Statistics show that our query algorithm runs many times faster than the state-of-the-art solvers.
Micro-Mesh Construction
Authors: Maggiordomo Andrea , Moreton Henry , Tarini Marco
Published: July 2023

Micro-meshes (μ-meshes) are a new structured graphics primitive supporting a large increase in geometric fidelity, without commensurate memory and run-time processing costs, consisting of a base mesh enriched by a displacement map. A new generation of GPUs supports this structure with native hardware μ-mesh ray-tracing, that leverages a self-bounding, compressed displacement mapping scheme to achieve these efficiencies.

In this paper, we present anautomatic method to convert an existing multi-million triangle mesh into this compact format, unlocking the advantages of the data representation for a large number of scenarios. We identify the requirements for high-quality μ-meshes, and show how existing re-meshing and displacement-map baking tools are ill-suited for their generation. Our method is based on a simplification scheme tailored to the generation of high-quality
base meshes
, optimized for tessellation and displacement sampling, in conjunction with algorithms for determining
displacement vectors
to control the direction and range of displacements. We also explore the optimization of μ-meshes for texture maps and the representation of boundaries.

We demonstrate our method with extensive batch processing, converting an existing collection of high-resolution scanned models to the micro-mesh representation, providing an open-source reference implementation, and, as additional material, the data and an inspection tool.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Authors: Zhang Biao , Tang Jiapeng , Nießner Matthias , Wonka Peter
Published: July 2023

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation, and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation. Code: https://1zb.github.io/3DShape2VecSet/.
A Practical Wave Optics Reflection Model for Hair and Fur
Authors: Xia Mengqi , Walter Bruce , Hery Christophe , Maury Olivier , Michielssen Eric , Marschner Steve
Published: July 2023

Traditional fiber scattering models, based on ray optics, are missing some important visual aspects of fiber appearance. Previous work [Xia et al. 2020] on wave scattering from ideal extrusions demonstrated that diffraction produces strong forward scattering and colorful effects that are missing from ray-based models. However, that work was unable to include some important surface characteristics such as surface roughness and tilted cuticle scales, which are known to be important for fiber appearance. In this work, we take an important step to study wave effects from rough fibers with arbitrary 3D microgeometry. While the full-wave simulation of realistic 3D fibers remains intractable, we developed a 3D wave optics simulator based on a physical optics approximation, using a GPU-based hierarchical algorithm to greatly accelerate the calculation. It simulates surface reflection and diffractive scattering, which are present in all fibers and typically dominate for darkly pigmented fibers. The simulation provides a detailed picture of first order scattering, but it is not practical to use for production rendering as this would require tabulation per fiber geometry. To practically handle geometry variations in the scene, we propose a model based on wavelet noise, capturing the important statistical features in the simulation results that are relevant for rendering. Both our simulation and practical model show similar granular patterns to those observed in optical measurement. Our compact noise model can be easily combined with existing scattering models to render hair and fur of various colors, introducing visually important colorful glints that were missing from all previous models.
Composite Motion Learning with Task Control
Authors: Xu Pei , Shang Xiumin , Zordan Victor , Karamouzas Ioannis
Published: July 2023

We present a deep learning method for composite and task-driven motion control for physically simulated characters. In contrast to existing data-driven approaches using reinforcement learning that imitate full-body motions, we learn decoupled motions for specific body parts from multiple reference motions simultaneously and directly by leveraging the use of multiple discriminators in a GAN-like setup. In this process, there is no need of any manual work to produce composite reference motions for learning. Instead, the control policy explores by itself how the composite motions can be combined automatically. We further account for multiple task-specific rewards and train a single, multi-objective control policy. To this end, we propose a novel framework for multi-objective learning that adaptively balances the learning of disparate motions from multiple sources and multiple goal-directed control objectives. In addition, as composite motions are typically augmentations of simpler behaviors, we introduce a sample-efficient method for training composite control policies in an incremental manner, where we reuse a pre-trained policy as the meta policy and train a cooperative policy that adapts the meta one for new composite tasks. We show the applicability of our approach on a variety of challenging multi-objective tasks involving both composite motion imitation and multiple goal-directed control. Code is available at
https://motion-lab.github.io/CompositeMotion
.
Topology driven approximation to rational surface-surface intersection via interval algebraic topology analysis
Authors: Cheng Jin-San , Zhang Bingwei , Xiao Yikun , Li Ming
Published: July 2023

Computing the intersection between two parametric surfaces (SSI) is one of the most fundamental problems in geometric and solid modeling. Maintaining the SSI topology is critical to its computation robustness. We propose a topology-driven hybrid symbolic-numeric framework to approximate rational parametric surface-surface intersection (SSI) based on a concept of
interval algebraic topology analysis (IATA)
, which configures within a 4D interval box the SSI topology. We map the SSI topology to an algebraic system's solutions within the framework, classify and enumerate all topological cases as a mixture of four fundamental cases (or their specific sub-cases). Various complicated topological situations are covered, such as cusp points or curves, tangent points (isolated or not) or curves, tiny loops, self-intersections, or their mixtures. The theoretical formulation is also implemented numerically using advanced real solution isolation techniques, and computed within a topology-driven framework which maximally utilizes the advantages of the topology maintenance of algebraic analysis, the robustness of iterative subdivision, and the efficiency of forward marching. The approach demonstrates improved robustness under benchmark topological cases when compared with available open-source and commercial solutions, including IRIT, SISL, and Parasolid.
NeRSemble: Multi-view Radiance Field Reconstruction of Human Heads
Authors: Kirschstein Tobias , Qian Shenhan , Giebenhain Simon , Walter Tim , Nießner Matthias
Published: July 2023

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time steps. To this end, we propose a new multi-view capture setup composed of 16 calibrated machine vision cameras that record time-synchronized images at 7.1 MP resolution and 73 frames per second. With our setup, we collect a new dataset of over 4700 high-resolution, high-framerate sequences of more than 220 human heads, from which we introduce a new human head reconstruction benchmark
1
. The recorded sequences cover a wide range of facial dynamics, including head motions, natural expressions, emotions, and spoken language. In order to reconstruct high-fidelity human heads, we propose Dynamic Neural Radiance Fields using Hash Ensembles (NeRSemble). We represent scene dynamics by combining a deformation field and an ensemble of 3D multi-resolution hash encodings. The deformation field allows for precise modeling of simple scene movements, while the ensemble of hash encodings helps to represent complex dynamics. As a result, we obtain radiance field representations of human heads that capture motion over time and facilitate re-rendering of arbitrary novel viewpoints. In a series of experiments, we explore the design choices of our method and demonstrate that our approach outperforms state-of-the-art dynamic radiance field approaches by a significant margin.
GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and Examples
Authors: Charalambous Panayiotis , Pettre Julien , Vassiliades Vassilis , Chrysanthou Yiorgos , Pelechano Nuria
Published: July 2023

Simulating crowds with realistic behaviors is a difficult but very important task for a variety of applications. Quantifying how a person balances between different conflicting criteria such as goal seeking, collision avoidance and moving within a group is not intuitive, especially if we consider that behaviors differ largely between people. Inspired by recent advances in Deep Reinforcement Learning, we propose Guided REinforcement Learning (GREIL) Crowds, a method that learns a model for pedestrian behaviors which is guided by reference crowd data. The model successfully captures behaviors such as goal seeking, being part of consistent groups without the need to define explicit relationships and wandering around seemingly without a specific purpose. Two fundamental concepts are important in achieving these results: (a) the per agent state representation and (b) the reward function. The agent state is a temporal representation of the situation around each agent. The reward function is based on the idea that people try to move in situations/states in which they feel comfortable in. Therefore, in order for agents to stay in a comfortable state space, we first obtain a distribution of states extracted from real crowd data; then we evaluate states based on how much of an outlier they are compared to such a distribution. We demonstrate that our system can capture and simulate many complex and subtle crowd interactions in varied scenarios. Additionally, the proposed method generalizes to unseen situations, generates consistent behaviors and does not suffer from the limitations of other data-driven and reinforcement learning approaches.
Assessing Aircraft Security: A Comprehensive Survey and Methodology for Evaluation
Authors: Habler Edan , Bitton Ron , Shabtai Asaf
Published: July 2023

The sophistication and complexity of cyber attacks and the variety of targeted platforms have grown in recent years. Adversaries are targeting a wide range of platforms, e.g., enterprise networks, mobile phones, PCs, and industrial control systems. The past few years have also seen various cyber attacks on transportation systems, including attacks on ports, trains, airports, and aircraft. Due to the enormous potential damage inherent in attacking vehicles carrying many passengers and the lack of security measures applied in existing airborne systems, the vulnerability of aircraft systems is one of the most concerning topics in the vehicle security domain. This article provides a comprehensive review of aircraft systems and components and their various networks, emphasizing the cyber threats they are exposed to and the impact of a cyber attack on these components and networks and an aircraft’s essential capabilities. In addition, we present a comprehensive and in-depth taxonomy that standardizes the knowledge and understanding of cyber security in the avionics field. The taxonomy divides attack techniques into relevant categories (tactics) reflecting the various phases of the adversarial attack lifecycle and maps existing attacks according to the MITRE ATT&CK methodology. To contribute to increased understanding of the potential risks, we categorize the identified threats related to the various systems based on STRIDE threat model and demonstrate the practical application of this taxonomy in the analysis of real-world attack use cases. Finally, we review various mitigation techniques aimed at addressing security risks related to aircraft systems. Future work directions are presented as guidelines for industry and academia.
Mesenchymal Stem Cells Inhibit Epithelial-to-Mesenchymal Transition by Modulating the IRE1α Branch of the Endoplasmic Reticulum Stress Response
Authors: Luo Ruixi , Wei Yaqiong , Chen Peng , Zhang Jing , Wang La , Wang Wenjia , Wang Ping , Tian Weiyi
Published: July 2023

Background. Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease, and it carries a poor prognosis due to a lack of efficient diagnosis methods and treatments. Epithelial-mesenchymal transition (EMT) plays a key role in IPF pathogenesis. Endoplasmic reticulum (ER) stress contributes to fibrosis via EMT-mediated pathways. Mesenchymal stem cell (MSC) transplantation is a promising treatment strategy for pulmonary fibrosis and ameliorates lung fibrosis in animal models via paracrine effects. However, the specific mechanisms underlying the effect of transplanted MSCs are not known. We previously reported that MSCs attenuate endothelial injury by modulating ER stress and endothelial-to-mesenchymal transition. The present study investigated whether modulation of ER stress- and EMT-related pathways plays essential roles in MSC-mediated alleviation of IPF. Methods and Results. We constructed a A549 cell model of transforming growth factor-β1 (TGF-β1)-induced fibrosis. TGF-β1 was used to induce EMT in A549 cells, and MSC coculture decreased EMT, as indicated by increased E-cadherin levels and decreased vimentin levels. ER stress participated in TGF-β1-induced EMT in A549 cells, and MSCs inhibited the expression of XBP-1s, XBP-1u, and BiP, which was upregulated by TGF-β1. Inhibition of ER stress contributed to MSC-mediated amelioration of EMT in A549 cells, and modulation of the IRE1α-XBP1 branch of the ER stress pathway may have played an important role in this effect. MSC transplantation alleviated bleomycin (BLM)-induced pulmonary fibrosis in mice. MSC treatment decreased the expression of ER stress- and EMT-related genes and proteins, and the most obvious effect of MSC treatment was inhibition of the IRE1α/XBP1 pathway. Conclusions. The present study demonstrated that MSCs decrease EMT by modulating ER stress and that blockade of the IRE1α-XBP1 pathway may play a critical role in this effect. The current study provides novel insight for the application of MSCs for IPF treatment and elucidates the mechanism underlying the preventive effects of MSCs against pulmonary fibrosis.
Renal Pseudoaneurysm with Associated Arteriovenous Fistula as a Cause of Delayed Bleeding after Percutaneous Nephrolithotomy: A Case Report and Current Literature Review
Authors: Devos Brecht , Vandeursen Hendrik , d’Archambeau Olivier , Vergauwe Eric
Published: July 2023

Background. Pseudoaneurysm (PA) with associated arteriovenous fistula (AVF) is a rare delayed bleeding complication, occurring in less than 1% of patients after percutaneous nephrolithotomy (PNL). Case presentation. A 54-year-old man underwent PNL on February 28, 2023, for a large renal calculus in the right kidney lower pole, with postoperative delayed bleeding: macroscopic hematuria and bladder clot retention after 3 weeks. An iatrogenic PA and AVF were diagnosed after the failure of conservative measures. The patient was successfully treated with superselective angioembolization (SAE) under local anesthesia. Conclusion. Late hemorrhagic complications after PNL can be severe. Rapid identification of a renal PA and AVF with SAE has a high success rate and low complication rate, avoiding prolonged hospitalization time and major renal surgery for this patient.
An Unconditionally Stable Difference Scheme for the Two-Dimensional Modified Fisher–Kolmogorov–Petrovsky–Piscounov Equation
Authors: Kwak Soobin , Kang Seungyoon , Ham Seokjun , Hwang Youngjin , Lee Gyeonggyu , Kim Junseok
Published: July 2023

In this article, we develop an unconditionally stable numerical scheme for the modified Fisher–Kolmogorov–Petrovsky–Piscounov (Fisher–KPP) equation modeling population dynamics in two-dimensional space. The Fisher–KPP equation models the process of interaction between reaction and diffusion. The new solution algorithm is based on an alternating direction implicit (ADI) method and an interpolation method so that it is unconditionally stable. The proposed finite difference method is second-order accurate in time and space variables. Therefore, the main purpose of this study is to propose the novel Fisher–KPP equation with a nonlinear growth term and develop an unconditionally stable second-order numerical scheme. The novelty of our method is that it is a numerical method with second-order accuracy using interpolation and ADI methods in two dimensions. We demonstrate the performance of the proposed scheme through computational tests such as convergence and stability tests and the effects of model parameters and initial conditions.
Endoscopic Image 2 Hours after PuraStat® Application: A Case of Achieving Hemostasis Using PuraStat® for Postgastric Lesion Biopsy Bleeding after Hemostatic Clips Failed
Authors: Misumi Yoshitsugu , Nonaka Kouichi , Kishino Maiko
Published: July 2023

PuraStat® (3D Matrix, Tokyo, Japan) is a novel, self-assembling peptide hemostatic hydrogel that can be used endoscopically. Hemostasis can be physically obtained by covering bleeding points; however, there are no reports of how long PuraStat remains in the upper gastrointestinal tract. Herein, we report a case wherein esophagogastroduodenoscopy (EGD) was performed 2 hours after PuraStat application. A 73-year-old man underwent EGD for evaluation of lesions in the posterior wall of the stomach. A biopsy was then performed on the gastric lesions; however, massive bleeding occurred. A hemostatic clip was used to stop bleeding but failed; primary hemostasis was obtained by applying PuraStat. EGD performed 2 hours later to determine whether the patient could be discharged revealed that the white-turning PuraStat gel remained firmly in the applied area, confirming complete hemostasis. PuraStat is a hemostatic agent capable of physical hemostasis that reliably remains in the stomach even after a few hours of use and, thus, may replace some conventional hemostasis methods.
Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection
Authors: Javed Muhammad Ashar , Bin Liaqat Hannan , Meraj Talha , Alotaibi Aziz , Alshammari Majid
Published: July 2023

Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient’s survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.
Feasibility of Conducting Sit-to-Stand Tests Using Video Consultation
Authors: Ng Deng Peng , Thiviyan P. , Shrida Sailli , Ng Li Whye Cindy
Published: July 2023

Objective. This study is aimed at ascertaining the feasibility of conducting the 1-minute sit-to-stand (1MSTS) and 30-second sit-to-stand (30SSTS) tests for healthy participants via video consultation. A secondary aim was to compare the relationship between the 1MSTS and 30SSTS. Methods. A total of 63 participants were recruited via the Singapore Institute of Technology emails and social media in 2020 during the peak of COVID-19. Prior to the sit-to-stand testing, all participants completed the consent form and physical activity questionnaires. Anthropometric data such as height and weight were also collected prior to testing. An instructional video detailing the sit-to-stand (STS) movement and the requirements for the environment set-up were sent to the participants via email. All STS tests were conducted virtually via the Zoom application. Healthy participants aged 21 to 55 years old performed a 1MSTS and 30SSTS each in random order. Results. All recruited participants completed the STS tests with no reported adverse events. Majority of participants were from the 21- to 25-year-old age groups, and the average number of repetitions performed by this group was

21.9
±
5.6

for the 30SSTS and

44.7
±
12.6

for the 1MSTS. Conclusion. Conducting the STS tests via video consultation was demonstrated to be safe and feasible. The number of repetitions performed in the 1MSTS is correlated to that of the 30SSTS, but 1MSTS has the ability to elicit a greater HR response among younger adults.
Genetic and Biological Characterization of H3N2 Avian Influenza Viruses Isolated from Poultry Farms in China between 2019 and 2021
Authors: Li Jiqing , Deng Guohua , Shi Jianzhong , Zhang Yaping , Zeng Xianying , Tian Guobin , Jiang Yongping , Liu Liling , Kong Huihui , Chen Hualan
Published: July 2023

H3N2 influenza viruses not only cause seasonal epidemics in humans but also circulate widely in animals, posing a threat to both animal and human health. Our previous studies indicate that H3N2 avian influenza viruses (AIVs) are readily detected in live poultry markets (LPMs); however, the evolution and biological characteristics of the H3N2 viruses in poultry farms in China are unclear. In this study, we performed active surveillance and collected 49,135 samples from poultry farms. In total, 21 H3N2 AIVs were isolated and their genetics, receptor-binding properties, and replication in mice were evaluated. Phylogenetic analysis indicated that H3N2 AIVs harbor complicated gene constellations and have undergone extensive reassortment; the viruses derived their genes from 12 different hemagglutinin subtypes of duck viruses, including H1, H2, H4, H5, H6, H7, H8, H9, H10, H11, H12, and H14. The complicated gene constellations indicated that H3N2 viruses may have been introduced into poultry farms from different sources, but none have become dominant in poultry farms. Although the H3N2 AIVs possessed avian-type receptor-binding preference, most of the isolates could replicate without preadaptation and some of H3N2 viruses caused weight loss in mice. Notably, two H3N2 viruses acquired the PB2 627K mutation after a single round of replication in mice, suggesting similar mutations could occur if they replicated in humans. Overall, our study demonstrates that the H3N2 AIVs pose a potential threat to the public health and emphasizes the need for continued surveillance of H3N2 viruses in the both LPMs and poultry farms.
Table S5 from Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility
Authors: Gregga Isabelle , Pharoah Paul D. P. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: July 2023

Table S5. PhenomeXcan TWAS results for 8 genes that had cancer associations.
Table S2 from Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility
Authors: Gregga Isabelle , Pharoah Paul D. P. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: July 2023

Table S2. Replication of cancer PWAS results in independent cohort with meta-analysis FDR<0.05.
Table S1 from Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility
Authors: Gregga Isabelle , Pharoah Paul D. P. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: July 2023

Table S1. Summary statistics of 7 cancer types tested with ARIC plasma proteome models, FDR<0.05, with single- and multi-variant COLOC results.
Supplementary Table 3 from Hospitalizations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: A Longitudinal Matched Cohort Study
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: July 2023

Adjusted annual mean costs of privately funded hospitalisations per individual, by all-cause and cause-specific physical diseases.
Supplementary Table 1 from Hospitalizations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: A Longitudinal Matched Cohort Study
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: July 2023

Major diagnostic groups of physical-health diseases based on the International Classification of Diseases (ICD), 9th (Clinical modification) and 10th (Australian Modification) revisions.
Data from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility
Authors: Gregga Isabelle , Pharoah Paul DP. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: July 2023

Abstract
Background: Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight to the mechanisms underlying cancer susceptibility.

Methods: We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis.

Results: Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations (FDR < 0.05). We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (p<0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (Posterior Probability, PP=0.65) and SNUPN protein levels and breast cancer (PP=0.62).

Conclusions: We used proteome-wide association studies (PWAS) to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect.

Impact: PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.
Data from Variations in Genes Encoding Human Papillomavirus Binding Receptors and Susceptibility to Cervical Pre-Cancer
Authors: Mukherjee Amrita , Ye Yuanfan , Wiener Howard W. , Kuniholm Mark H. , Minkoff Howard , Michel Kate , Palefsky Joel , D'Souza Gypsyamber , Rahangdale Lisa , Butler Kenneth R. , Kempf Mirjam-Colette , Sudenga Staci L. , Aouizerat Bradley E. , Ojesina Akinyemi I. , Shrestha Sadeep
Published: July 2023

Abstract
Background: Cervical cancer oncogenesis starts with human papillomavirus (HPV) cell entry after binding to host cell surface receptors; however, the mechanism is not fully known. We examined polymorphisms in receptor genes hypothesized to be necessary for HPV cell entry and assessed their associations with clinical progression to pre-cancer.

Methods: African-American women (N=1728) from the MACS/WIHS Combined Cohort Study were included. Two case-control study designs were used - cases with histology-based pre-cancer (CIN3+) and controls without; and cases with cytology-based pre-cancer (HSIL) and controls without. Single nucleotide polymorphisms (SNPs) in candidate genes (SDC1, SDC2, SDC3, SDC4, GPC1, GPC2, GPC3, GPC4, GPC5, GPC6 and ITGA6) were genotyped using an Illumina Omni2.5-quad beadchip. Logistic regression was used to assess the associations in all participants and by HPV genotypes, after adjusting for age, HIV-serostatus, CD4 T-cells, and three principal components for ancestry.

Results: Minor alleles in SNPs rs77122854 (SDC3), rs73971695, rs79336862 (ITGA6), rs57528020, rs201337456, rs11987725 (SDC2), rs115880588, rs115738853 and rs9301825 (GPC5) were associated with increased odds of both CIN3+ and HSIL, whereas, rs35927186 (GPC5) was found to decrease the odds for both outcomes (p-value≤0.01). Among those infected with Alpha-9 HPV types, rs722377 (SDC3), rs16860468, rs2356798 (ITGA6), rs11987725 (SDC2), and rs3848051 (GPC5) were associated with increased odds of both pre-cancer outcomes.

Conclusions: Polymorphisms in genes that encode binding receptors for HPV cell entry may play a role in cervical pre-cancer progression.

Impact: Our findings are hypothesis generating and support further exploration of mechanisms of HPV entry genes that may help prevent progression to cervical pre-cancer.
Germline Mutations in 12 Genes and Risk of Ovarian Cancer in Three Population-Based Cohorts
Authors: Kotsopoulos Joanne , Hathaway Cassandra A. , Narod Steven A. , Teras Lauren R. , Patel Alpa V. , Hu Chunling , Yadav Siddhartha , Couch Fergus J. , Tworoger Shelley S.
Published: July 2023

Background:

With the widespread use of multigene panel genetic testing, population-based studies are necessary to accurately assess penetrance in unselected individuals. We evaluated the prevalence of germline pathogenic or likely pathogenic variants (mutations) in 12 cancer-predisposition genes and associations with ovarian cancer risk in three population-based prospective studies [Nurses’ Health Study (NHS), NHSII, Cancer Prevention Study II].

Methods:

We included women with epithelial ovarian or peritoneal cancer (n = 776) and controls who were alive and had at least one intact ovary at the time of the matched case diagnosis (n = 1,509). Germline DNA was sequenced for mutations in 12 genes. Conditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for ovarian cancer risk by mutation status.

Results:

The mutation frequency across all 12 genes was 11.2% in cases and 3.3% in controls (P < 0.0001). BRCA1 and BRCA2 were the most frequently mutated (3.5% and 3.8% of cases and 0.3% and 0.5% of controls, respectively) and were associated with increased ovarian cancer risk [OR, BRCA1 = 12.38; 95% confidence interval (CI) = 4.72–32.45; OR, BRCA2 = 9.18; 95% CI = 3.98–21.15]. Mutation frequencies for the other genes were ≤1.0% and only PALB2 was significantly associated with risk (OR = 5.79; 95% CI = 1.09–30.83). There was no difference in survival for women with a BRCA germline mutation versus no mutation.

Conclusions:

Further research is needed to better understand the role of other mutations in ovarian cancer among unselected populations.

Impact:

Our data support guidelines for germline genetic testing for BRCA1 and BRCA2 among women diagnosed with epithelial ovarian cancer; testing for PALB2 may be warranted.
Table S4 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Table of mice enrolled in drug study
Table S1 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Gene set enrichment analysis of RAS signatures
Figure S8 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Pharmacokinetic Evaluation of abemaciclib and LY3214996
Figure S6 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Caspase Glo 3/7 Assay
Figure S5 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

CSF1R Mouse IHC, CSF1R and STK10 Western Blot, CDK6 Human, phospho-Rb (S807/811) Human, and pRSK1 (S380) Human IHC
Figure S3 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Hallmark E2F Targets Enrichment Plot and Top DEGs
Figure S2 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Reactome G1 Phase Heatmap and Top DEGs
Figure S11 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

DEG Tables and Volcano Plots
Figure S10 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Western Blots
Supplementary Table S1 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Table S1. Results of genome-wide CRISPR/Cas9 screening of HCC-1428/LTED cells.
Supplementary Figure S9 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Characterization of PDX model CTG-3346