bluuebunny/crossref_metadata_2025 huggingface dataset:
Second-order Stencil Descent for Interior-point Hyperelasticity
Authors: Lan Lei , Li Minchen , Jiang Chenfanfu , Wang Huamin , Yang Yin
Published: July 2023

In this paper, we present a GPU algorithm for finite element hyperelastic simulation. We show that the interior-point method, known to be effective for robust collision resolution, can be coupled with non-Newton procedures and be massively sped up on the GPU. Newton's method has been widely chosen for the interior-point family, which fully solves a linear system at each step. After that, the active set associated with collision/contact constraints is updated. Mimicking this routine using a non-Newton optimization (like gradient descent or ADMM) unfortunately does not deliver expected accelerations. This is because the barrier functions employed in an interior-point method need to be updated at every iteration to strictly confine the search to the feasible region. The associated cost (e.g., per-iteration CCD) quickly overweights the benefit brought by the GPU, and a new parallelism modality is needed. Our algorithm is inspired by the domain decomposition method and designed to move interior-point-related computations to local domains as much as possible. We minimize the size of each domain (i.e., a stencil) by restricting it to a single element, so as to fully exploit the capacity of modern GPUs. The stencil-level results are integrated into a global update using a novel hybrid sweep scheme. Our algorithm is locally second-order offering better convergence. It enables simulation acceleration of up to two orders over its CPU counterpart. We demonstrate the scalability, robustness, efficiency, and quality of our algorithm in a variety of simulation scenarios with complex and detailed collision geometries.
Pyramid Texture Filtering
Authors: Zhang Qing , Jiang Hao , Nie Yongwei , Zheng Wei-Shi
Published: July 2023

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github.io/pyramid_texture_filtering/.
ArrangementNet: Learning Scene Arrangements for Vectorized Indoor Scene Modeling
Authors: Huang Jingwei , Zhang Shanshan , Duan Bo , Zhang Yanfeng , Guo Xiaoyang , Sun Mingwei , Yi Li
Published: July 2023

We present a novel vectorized indoor modeling approach that converts point clouds into building information models (BIM) with concise and semantically segmented polygonal meshes. Existing methods detect planar shapes and connect them to complete the scene. Some focus on floor plan reconstruction as a simplified problem to better analyze connectivity between planes of floors and walls. However, the connectivity analysis is still challenging and ill-posed with incomplete point clouds as input. We propose ArrangementNet to estimate scene arrangements from an incomplete point cloud, which we can easily convert into a BIM model. ArrangementNet is a novel graph neural network that consumes noisy over-partitioned initial arrangements extracted through non-learning techniques and outputs high-quality scene arrangement. The core of ArrangementNet is an extended graph convolution that leverages co-linear and co-face relationships in the arrangement and improves the quality of prediction in complex scenes. We apply ArrangementNet to improve floor plan and ceiling arrangements and enrich them with semantic objects as scene arrangements for scene generation. Our approach faithfully models challenging scenes obtained from laser scans or multiview stereo and shows significant improvement in BIM model reconstruction compared to the state-of-the-art. Our code is available at https://github.com/zssjh/ArrangementNet.
StripMaker: Perception-driven Learned Vector Sketch Consolidation
Authors: Liu Chenxi , Aoki Toshiki , Bessmeltsev Mikhail , Sheffer Alla
Published: July 2023

Artist sketches often use multiple
overdrawn
strokes to depict a single intended curve. Humans effortlessly mentally
consolidate
such sketches by detecting groups of overdrawn strokes and replacing them with the corresponding intended curves. While this mental process is near instantaneous, manually annotating or retracing sketches to communicate this intended mental image is highly time consuming; yet most sketch applications are not designed to handle overdrawing and can only operate on overdrawing-free, consolidated sketches. We propose
StripMaker
, a new and robust learning based method for automatic consolidation of raw vector sketches. We avoid the need for an unsustainably large manually annotated learning corpus by leveraging observations about artist workflow and perceptual cues viewers employ when mentally consolidating sketches. We train two perception-aware classifiers that assess the likelihood that a pair of stroke groups jointly depicts the same intended curve: our first classifier is purely local and only accounts for the properties of the evaluated strokes; our second classifier incorporates global context and is designed to operate on approximately consolidated sketches. We embed these classifiers within a consolidation framework that leverages artist workflow: we first process strokes in the order they were drawn and use our local classifier to arrive at an approximate consolidation output, then use the contextual classifier to refine this output and finalize the consolidated result. We validate StripMaker by comparing its results to manual consolidation outputs and algorithmic alternatives. StripMaker achieves comparable performance to manual consolidation. In a comparative study participants preferred our results by a 53% margin over those of the closest algorithmic alternative (67% versus 14%, other/neither 19%).
Semi-supervised reference-based sketch extraction using a contrastive learning framework
Authors: Seo Chang Wook , Ashtari Amirsaman , Noh Junyong
Published: July 2023

Sketches reflect the drawing style of individual artists; therefore, it is important to consider their unique styles when extracting sketches from color images for various applications. Unfortunately, most existing sketch extraction methods are designed to extract sketches of a single style. Although there have been some attempts to generate various style sketches, the methods generally suffer from two limitations: low quality results and difficulty in training the model due to the requirement of a paired dataset. In this paper, we propose a novel multi-modal sketch extraction method that can imitate the style of a given reference sketch with unpaired data training in a semi-supervised manner. Our method outperforms state-of-the-art sketch extraction methods and unpaired image translation methods in both quantitative and qualitative evaluations.
Example-based Motion Synthesis via Generative Motion Matching
Authors: Li Weiyu , Chen Xuelin , Li Peizhuo , Sorkine-Hornung Olga , Chen Baoquan
Published: July 2023

We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known
Motion Matching
method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.
Towards Attention–aware Foveated Rendering
Authors: Krajancich Brooke , Kellnhofer Petr , Wetzstein Gordon
Published: July 2023

Foveated graphics is a promising approach to solving the bandwidth challenges of immersive virtual and augmented reality displays by exploiting the falloff in spatial acuity in the periphery of the visual field. However, the perceptual models used in these applications neglect the effects of higherlevel cognitive processing, namely the allocation of visual attention, and are thus overestimating sensitivity in the periphery in many scenarios. Here, we introduce the first attention-aware model of contrast sensitivity. We conduct user studies to measure contrast sensitivity under different attention distributions and show that sensitivity in the periphery drops significantly when the user is required to allocate attention to the fovea. We motivate the development of future foveation models with another user study and demonstrate that tolerance for foveation in the periphery is significantly higher when the user is concentrating on a task in the fovea. Analysis of our model predicts significant bandwidth savings over those afforded by current models. As such, our work forms the foundation for attention-aware foveated graphics techniques.
Patternshop: Editing Point Patterns by Image Manipulation
Authors: Huang Xingchang , Ritschel Tobias , Seidel Hans-Peter , Memari Pooran , Singh Gurprit
Published: July 2023

Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embedding for point correlations. This embedding can map point patterns to common three-channel raster images, enabling manipulation with off-the-shelf image editing software. To synthesize back point patterns, we propose a novel edge-aware objective that carefully handles sharp variations in density and correlation. The resulting framework allows intuitive and backward-compatible manipulation of point patterns, such as recoloring, relighting to even texture synthesis that have not been available to 2D point pattern design before. Effectiveness of our approach is tested in several user experiments. Code is available at https://github.com/xchhuang/patternshop.
ETER: Elastic Tessellation for Real-Time Pixel-Accurate Rendering of Large-Scale NURBS Models
Authors: Xiong Ruicheng , Lu Yang , Chen Cong , Zhu Jiaming , Zeng Yajun , Liu Ligang
Published: July 2023

We present ETER, an elastic tessellation framework for rendering large-scale NURBS models with pixel-accurate and crack-free quality at real-time frame rates. We propose a highly parallel adaptive tessellation algorithm to achieve pixel accuracy, measured by the screen space error between the exact surface and its triangulation. To resolve a bottleneck in NURBS rendering, we present a novel evaluation method based on uniform sampling grids and accelerated by GPU Tensor Cores. Compared to evaluation based on hardware tessellation, our method has achieved a significant speedup of 2.9 to 16.2 times depending on the degrees of the patches. We develop an efficient crack-filling algorithm based on conservative rasterization and visibility buffer to fill the tessellation-induced cracks while greatly reducing the jagged effect introduced by conservative rasterization. We integrate all our novel algorithms, implemented in CUDA, into a GPU NURBS rendering pipeline based on Mesh Shaders and hybrid software/hardware rasterization. Our performance data on a commodity GPU show that the rendering pipeline based on ETER is capable of rendering up to 3.7 million patches (0.25 billion tessellated triangles) in real-time (30FPS). With its advantages in performance, scalability, and visual quality in rendering large-scale NURBS models, a real-time tessellation solution based on ETER can be a powerful alternative or even a potential replacement for the existing pre-tessellation solution in CAD systems.
Expansion Cones: A Progressive Volumetric Mapping Framework
Authors: Nigolian Valentin Zénon , Campen Marcel , Bommes David
Published: July 2023

Volumetric mapping is a ubiquitous and difficult problem in Geometry Processing and has been the subject of research in numerous and various directions. While several methods show encouraging results, the field still lacks a general approach with guarantees regarding map bijectivity. Through this work, we aim at opening the door to a new family of methods by providing a novel framework based on the concept of
progressive expansion.
Starting from an initial map of a tetrahedral mesh whose image may contain degeneracies but no inversions, we incrementally adjust vertex images to expand degenerate elements. By restricting movement to so-called
expansion cones
, it is done in such a way that the number of degenerate elements decreases in a strictly monotonic manner, without ever introducing any inversion. Adaptive local refinement of the mesh is performed to facilitate this process. We describe a prototype algorithm in the realm of this framework for the computation of maps from ball-topology tetrahedral meshes to convex or star-shaped domains. This algorithm is evaluated and compared to state-of-the-art methods, demonstrating its benefits in terms of bijectivity. We also discuss the associated cost in terms of sometimes significant mesh refinement to obtain the necessary degrees of freedom required for establishing a valid mapping. Our conclusions include that while this algorithm is only of limited immediate practical utility due to efficiency concerns, the general framework has the potential to inspire a range of novel methods improving on the efficiency aspect.
Trim Regions for Online Computation of From-Region Potentially Visible Sets
Authors: Voglreiter Philip , Kerbl Bernhard , Weinrauch Alexander , Mueller Joerg Hermann , Neff Thomas , Steinberger Markus , Schmalstieg Dieter
Published: July 2023

Visibility computation is a key element in computer graphics applications. More specifically, a from-region potentially visible set (PVS) is an established tool in rendering acceleration, but its high computational cost means a from-region PVS is almost always precomputed. Precomputation restricts the use of PVS to static scenes and leads to high storage cost, in particular, if we need fine-grained regions. For dynamic applications, such as streaming content over a variable-bandwidth network, online PVS computation with configurable region size is required. We address this need with trim regions, a new method for generating from-region PVS for arbitrary scenes in real time. Trim regions perform controlled erosion of object silhouettes in image space, implicitly applying the shrinking theorem known from previous work. Our algorithm is the first that applies automatic shrinking to unconstrained 3D scenes, including non-manifold meshes, and does so in real time using an efficient GPU execution model. We demonstrate that our algorithm generates a tight PVS for complex scenes and outperforms previous online methods for from-viewpoint and from-region PVS. It runs at 60 Hz for realistic game scenes consisting of millions of triangles and computes PVS with a tightness matching or surpassing existing approaches.
Neural Prefiltering for Correlation-Aware Levels of Detail
Authors: Weier Philippe , Zirr Tobias , Kaplanyan Anton , Yan Ling-Qi , Slusallek Philipp
Published: July 2023

We introduce a practical general-purpose neural appearance filtering pipeline for physically-based rendering. We tackle the previously difficult challenge of aggregating visibility across many levels of detail from local information only, without relying on learning visibility for the entire scene. The high adaptivity of neural representations allows us to retain geometric correlations along rays and thus avoid light leaks. Common approaches to prefiltering decompose the appearance of a scene into volumetric representations with physically-motivated parameters, where the inflexibility of the fitted models limits rendering accuracy. We avoid assumptions on particular types of geometry or materials, bypassing any special-case decompositions. Instead, we directly learn a compressed representation of the intra-voxel light transport. For such high-dimensional functions, neural networks have proven to be useful representations. To satisfy the opposing constraints of prefiltered appearance and correlation-preserving point-to-point visibility, we use two small independent networks on a sparse multi-level voxel grid. Each network requires 10--20 minutes of training to learn the appearance of an asset across levels of detail. Our method achieves 70--95% compression ratios and around 25% of quality improvements over previous work. We reach interactive to real-time framerates, depending on the level of detail.
Efficient Embeddings in Exact Arithmetic
Authors: Finnendahl Ugo , Bogiokas Dimitrios , Robles Cervantes Pablo , Alexa Marc
Published: July 2023

We provide a set of tools for generating planar embeddings of triangulated topological spheres. The algorithms make use of Schnyder labelings and realizers. A new representation of the realizer based on dual trees leads to a simple linear time algorithm mapping from weights per triangle to barycentric coordinates and, more importantly, also in the reverse direction. The algorithms can be implemented so that all coefficients involved are 1 or -1. This enables integer computation, making all computations exact. Being a Schnyder realizer, mapping from positive triangle weights guarantees that the barycentric coordinates form an embedding. The reverse direction enables an algorithm for fixing flipped triangles in planar realizations, by mapping from coordinates to weights and adjusting the weights (without forcing them to be positive). In a range of experiments, we demonstrate that all algorithms are orders of magnitude faster than existing robust approaches.
Blended Latent Diffusion
Authors: Avrahami Omri , Fried Ohad , Lischinski Dani
Published: July 2023

The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling
generic
images requires a diverse underlying generative model, hence the latest works utilize diffusion models, which were shown to surpass GANs in terms of diversity. One major drawback of diffusion models, however, is their relatively slow inference time. In this paper, we present an accelerated solution to the task of
local
text-driven editing of generic images, where the desired edits are confined to a user-provided mask. Our solution leverages a text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space and eliminating the need for resource-intensive CLIP gradient calculations at each diffusion step. We first enable LDM to perform local image edits by blending the latents at each step, similarly to Blended Diffusion. Next we propose an optimization-based solution for the inherent inability of LDM to accurately reconstruct images. Finally, we address the scenario of performing local edits using thin masks. We evaluate our method against the available baselines both qualitatively and quantitatively and demonstrate that in addition to being faster, it produces more precise results.
Embracing the Power of eLearning: Advancing the future of education
Authors: Samuel Anita
Published: July 2023

In an era of rapid technological advancements and explosion in eLearning, it is with great enthusiasm that I start my term as the new Editor-in-Chief of
eLearn Magazine
. I am honored to lead the magazine through these exciting times.
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation
Authors: Song Haiyue , Dabre Raj , Chu Chenhui , Kurohashi Sadao , Sumita Eiichiro
Published: July 2023

Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair Encoding (BPE), however, they are inefficient, as they require parallel corpora, days to train, and hours to decode. This article introduces SelfSeg, a self-supervised neural sub-word segmentation method that is much faster to train/decode and requires only monolingual dictionaries instead of parallel corpora. SelfSeg takes as input a word in the form of a partially masked character sequence, optimizes the word generation probability, and generates the segmentation with the maximum posterior probability, which is calculated using a dynamic programming algorithm. The training time of SelfSeg depends on word frequencies, and we explore several word frequency normalization strategies to accelerate the training phase. Additionally, we propose a regularization mechanism that allows the segmenter to generate various segmentations for one word. To show the effectiveness of our approach, we conduct MT experiments in low-, middle-, and high-resource scenarios, where we compare the performance of using different segmentation methods. The experimental results demonstrate that, on the low-resource ALT dataset, our method achieves more than 1.2 BLEU score improvement compared with BPE and SentencePiece, and a 1.1 score improvement over Dynamic Programming Encoding (DPE) and Vocabulary Learning via Optimal Transport (VOLT), on average. The regularization method achieves approximately a 4.3 BLEU score improvement over BPE and a 1.2 BLEU score improvement over BPE-dropout, the regularized version of BPE. We also observed significant improvements on IWSLT15 Vi→En, WMT16 Ro→En, and WMT15 Fi→En datasets and competitive results on the WMT14 De→En and WMT14 Fr→En datasets. Furthermore, our method is 17.8× faster during training and up to 36.8× faster during decoding in a high-resource scenario compared to DPE. We provide extensive analysis, including why monolingual word-level data is enough to train SelfSeg.
Language and Race: Settler Colonial Consequences and Epistemic Disruptions
Authors: Shankar Shalini
Published: July 2023

This article reviews anthropological paradigms that link language and race with a focus on the United States and other settler colonial nations that continue to use language as a tool of racialization to bolster White supremacy. Enduring colonial ideologies, along with Boas's “salvage anthropology,” which separated race and language, have enshrined White racism in anthropological studies of language as well as in the field of linguistic anthropology. Contemporary studies frame linguistic racialization through markedness theory and use paradigms of language ideology, language materiality, and semiotics to forward discursive and ontological analyses that span communities and institutional spaces. I offer “disruption” as a way to consider the impact of epistemologies that inform academic research agendas as well as institutional power dynamics between BIPOC (Black, Indigenous, and people of color) scholars and White practitioners in linguistic anthropology and discuss how these disruptions could form the basis from which to decolonize aspects of linguistic anthropology.
Structural, Morphological, and Magnetocaloric Properties of Nanocrystalline Pr0.7La0.3MnO3
Authors: Singh Gurmeet , Gaur Arpit , Bisht Priyanka , Mahato Rabindra Nath
Published: July 2023

The structural, morphological, and magnetocaloric properties of nanocrystalline Pr0.7La0.3MnO3 (PLMO), synthesized by using a wet chemical Sol-gel method, have been investigated. The single-phase nature of nanocrystalline PLMO with orthorhombic crystal structure along with the Pbnm space group was confirmed by Rietveld analysis of the X-ray diffraction pattern. The average crystallite size and lattice microstrain were determined using the Williamson-Hall technique, and they were found to be ∼28 nm and ∼0.00137, respectively. Transmission electron microscopy shows the particle size and structural characteristics of the sample. Temperature-dependent magnetization data show paramagnetic to ferromagnetic transition. According to Banerjee criterion, the positive slope of Arrott’s plot for nanocrystalline PLMO indicates the second-order magnetic phase transition. In a 5 T external magnetic field, the maximum magnetic entropy change was found to be 2.47 J kg K−1. The refrigerant capacity and relative cooling power were calculated and the values are 193.3 J kg−1 and 277.57 J kg−1, respectively, under a 5 T applied external magnetic field.
The Impact of Annealing Treatment on Ethanol Gas Sensing Performance of SnO2 Nanoflakes
Authors: Zamiri Golnoush , Ghodsi F. E.
Published: July 2023

High-performance ethanol gas sensors are in high demand for various clinical and environmental monitoring applications. In this study, we developed a high-performance ethanol gas sensor using pure SnO2 nanoflakes. These nanoflakes were synthesized through a hydrothermal method and subsequently annealed at different temperatures to investigate the impact of annealing temperature on sensor performance. Among the samples tested, the ethanol gas sensor based on SnO2 annealed at 300 °C exhibited a higher response to ethanol than other samples toward 20 and 1000 ppm ethanol gas at 200 °C. The fast response and good selectivity confirm that the SnO2 nanoflakes annealed at 300 °C can be a promising sensing material for the fabrication of ethanol gas sensors.
Depression and Its Determinants among Postpartum Mothers Attending at Universal College of Medical Sciences and Teaching Hospital, Bhairahawa, Rupandehi, Nepal
Authors: Bhusal Chet Kant , Bhattarai Sigma , Shrestha Alisha , Sharma Hem Raj
Published: July 2023

Background. Postpartum depression is a serious mental health issue linked to maternal morbidity and negative effects for infant’s normal growth, development, and well-being. This study is aimed at assessing the prevalence and factors associated with postpartum depression among mothers attending a tertiary hospital in Nepal. Methods. A hospital-based cross-sectional study was conducted among 173 postpartum mothers (<6 weeks postdelivery) who were receiving care either at the postnatal ward or immunization clinic of Universal College of Medical Sciences and Teaching Hospital in Bhairahawa, Rupandehi district, Nepal. The study was carried out from October 2020 to February 2021 by using purposive sampling technique for selecting respondents. The variables that showed significant association with the dependent variable havingpvalue < 0.05 in bivariate analysis were entered into multivariate logistic regression model to find the final associated factors. Results. The prevalence of postpartum depression was 20.2% among mothers attending a tertiary hospital in Nepal. The mean age of the respondents was24.77±3.47. Factors such as mothers having female child (AOR=6.39,CI=1.54−26.46), unplanned pregnancy (AOR=10.08,CI=2.91−34.94), pregnancy-induced health problems (AOR=9.68, CI: 3.51-26.64) were associated with an increased risk of postpartum depression. Similarly, mothers having formal education (AOR=0.28, CI: 0.08-0.91), whose spouses have secondary and above education (AOR=0.16, CI: 0.03-0.85), and who have ≥4 ANC visits (AOR=0.15,CI=0.05−0.40) were significantly associated but have a protective effect with postpartum depression. Conclusions. Sex of newborn, mother’s and spouse’s education, intention of pregnancy, ANC visits, and pregnancy-induced health problems were found to be significantly associated with postpartum depression. Hence, specific health education program regarding maternal and child health integrating mental health should be provided to pregnant women, mothers, and their husbands, focusing on gender discrimination. Similarly specific orientation program should be provided to local health worker about the importance of planned pregnancy, in order to reduce pregnancy related health problems during ANC visits and to mothers after their delivery to reduce further chances of postpartum depression.
Grid-Connected Solar PV System with Maximum Power Point Tracking and Battery Energy Storage Integrated with Sophisticated Three-Level NPC Inverter
Authors: Kishore D. Ravi , Muni T. Vijay , Raja B. Srinivas , Pushkarna Mukesh , Goud B. Srikanth , AboRas Kareem M. , Alphonse Sadam
Published: July 2023

In this research, a solar photovoltaic system with maximum power point tracking (MPPT) and battery storage is integrated into a grid-connected system using an improved three-level neutral-point-clamped (NPC) inverter. An NPC inverter with adjustable neutral-point clamping may achieve this result. To achieve this result, a modernized NPC inverter is used. Using the three-level vector modulation approach, the correct AC voltage may be generated when DC voltage conditions are present in an unbalanced situation involving an NPC inverter. A higher degree of precision is made possible by this. In-depth information on the many possible NPC inverter designs and modulation strategies was provided. By incorporating the necessary sophisticated algorithms into the NPC inverter, the necessary solar PV-MPPT functionality is made available, allowing for the regulation of power transfer among the solar PV system, the battery, and the grid. Modified INC method is proposed which has a tracking efficiency of 99.5% for varying irradiance. The use of sufficiently sophisticated algorithms makes this a reality. Using simulation with MATLAB/Simulink, we were able to examine an NPC inverter’s performance in several setups. Several amounts of solar irradiation were used to test the battery’s charging and discharging capabilities. The probe turned out to be fruitful. Laboratory testing ensures that the proposed method works by simulating real-world conditions.
S100A9 Regulated M1/M2 Macrophage Polarization in Interleukin-10-Induced Promotion of Malignant Pleural Effusion
Authors: Pei Xue-Bin , Yi Feng-Shuang , Dong Shu-Feng , Chen Qing-Yu , Shi Xin-Yu
Published: July 2023

Interleukin-10 (IL-10) promotes the formation and development of malignant pleural effusion (MPE). Previous studies have elucidated the pathogenesis from the view of the immune-regulation function of CD4+ T-cells. However, the underlying mechanism is still not fully understood. In this study, our results showed that IL-10 deficiency reduced the percentage of macrophages in mouse MPE and regulated M1/M2 polarization in vivo and in vitro. The migration capacity of tumor cells was suppressed, and apoptosis was promoted when tumor cells were cocultured with MPE macrophages in the absence of IL-10. Messenger RNA sequencing of MPE macrophages showed that S100A9 was downregulated in IL-10−/− mice. Bone marrow-derived macrophages obtained from wild-type mice transfected with S100A9-specific small interfering RNAs (siRNAs) also showed less M2 and more M1 polarization than those from the siRNA control group. Furthermore, downregulation of S100A9 using S100A9-specific siRNA suppressed MPE development, decreased macrophages, and modulated macrophage polarization in MPE in vivo. In conclusion, S100A9 plays a vital role in the process of IL-10 deficiency-mediated MPE suppression by regulating M1/M2 polarization, thus influencing the tumor-migration capacity and apoptosis. This could result in clinically applicable strategies to inhibit the formation of MPE by regulating the polarization of MPE macrophages.
Identification of DUSP7 as an RNA Marker for Prognostic Stratification in Acute Myeloid Leukemia: Evidence from Large Population Cohorts
Authors: Gao Xin
Published: July 2023

Background. The problem of prognostic stratification in acute myeloid leukemia (AML) patients still has limitations. Methods. The expression profile data and clinical features of AML patients were obtained from multiple publicly available sources, including GSE71014, TCGA-LAML, and TARGET-AML. Single-cell analysis was performed using the TISCH project. All the analysis was conducted in the R software. Results. In our study, three public AML cohorts, GSE71014, TARGET-AML, and TCGA-AML, were selected. Then, we identified the prognosis-related molecules through bioinformatic analysis. Finally, the DUSP7 was noticed as a risk factor for AML patients, which has not been reported previously. Biological enrichment analysis and immune-related analysis were performed to illustrate the role of DUSP7 in AML. Single-cell analysis indicated that the DUSP7 was widely distributed in various cells, especially in monocyte/macrophages and malignant. Following this, a prognosis model based on DUSP7-derived genes was constructed, which showed a good prognosis prediction ability in all cohorts. Conclusions. Our results preliminarily reveal the role and potential mechanism of DUSP7 in AML, providing direction for future research.
A Rare Odontogenic Cyst: Gingival Cyst of the Adult. A Case Report and Review of the Literature in Elderly Patients
Authors: Frigerio Martina , Al Eid Rami , Lombardi Tommaso
Published: July 2023

Gingival cysts of adults (GCAs) are a relatively rare type of odontogenic cysts developing from the remnants of the dental lamina. Although GCAs generally affect individuals below the age of 65, their findings in older patients may exceptionally occur. Usually asymptomatic, they present as small, generally solitary nodules, located on the attached gingiva, primarily in the mandibular canine and premolar regions. This report highlights a rare case of a solitary GCA in an 82-year-old patient, who presented with a discrete nodule located between the right mandibular canine and first premolar. An excisional biopsy was performed, and the diagnosis of GCA was confirmed by the histological examination. No recurrence was observed during the 18-month follow-up period. Additionally, a literature review was conducted to analyse the clinical and pathological characteristics of GCAs in patients aged 65 and older. By providing details of this rare entity, our case study highlights the importance of considering GCAs when diagnosing gingival lesions in geriatric patients. By increasing our understanding of GCAs in the elderly population, our findings can help improve diagnosis as well as management strategies for these lesions.
Supplementary Figure 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

Supplementary Figure S1: Workflow illustrating the methods.
Supplementary Table S1 from Variations in Genes Encoding Human Papillomavirus Binding Receptors and Susceptibility to Cervical Precancer
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

Supplementary Table S1 lists all genetic variants (SNPs) in the candidate genese associated with pre-cancer.
Supplementary Figure 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

The mean cumulative count of hospitalisations for any physical-health disease by time since index cancer diagnosis, in childhood cancer survivors.
Data from Variations in Genes Encoding Human Papillomavirus Binding Receptors and Susceptibility to Cervical Precancer
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

AbstractBackground:
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 precancer.
Methods:
African American women (N = 1,728) from the MACS/WIHS Combined Cohort Study were included. Two case–control study designs were used—cases with histology-based precancer (CIN3+) and controls without; and cases with cytology-based precancer [high-grade squamous intraepithelial lesions (HSIL)] and controls without. 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, human immunodeficiency virus 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 precancer outcomes.
Conclusions:
Polymorphisms in genes that encode binding receptors for HPV cell entry may play a role in cervical precancer progression.
Impact:
Our findings are hypothesis generating and support further exploration of mechanisms of HPV entry genes that may help prevent progression to cervical precancer.
Table 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

Raw log2LFQ values for kinases profiled by MIB/MS
Figure 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

SPI1 Mouse and ETV4 Human IHC Cytonuclear Analysis
Figure 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

Hallmark KRAS Pathway Heatmap and Top DEGs
Supplementary Table 2 from A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer
Authors: Miglietta Federica , Ragazzi Moira , Fernandes Bethania , Griguolo Gaia , Massa Davide , Girardi Fabio , Bottosso Michele , Bisagni Alessandra , Zarrilli Giovanni , Porra Francesca , Iannaccone Daniela , Dore Leocadia , Gaudio Mariangela , Santandrea Giacomo , Fassan Matteo , Lo Mele Marcello , De Sanctis Rita , Zambelli Alberto , Bisagni Giancarlo , Guarneri Valentina , Dieci Maria Vittoria
Published: July 2023

Supplementary Table 2 - Representativeness of Study Participants
Supplementary Table 1 from A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer
Authors: Miglietta Federica , Ragazzi Moira , Fernandes Bethania , Griguolo Gaia , Massa Davide , Girardi Fabio , Bottosso Michele , Bisagni Alessandra , Zarrilli Giovanni , Porra Francesca , Iannaccone Daniela , Dore Leocadia , Gaudio Mariangela , Santandrea Giacomo , Fassan Matteo , Lo Mele Marcello , De Sanctis Rita , Zambelli Alberto , Bisagni Giancarlo , Guarneri Valentina , Dieci Maria Vittoria
Published: July 2023

Supplementary Table 1 - Clinico-pathological characteristics of the overall population.
Supplementary Figure 3 from A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer
Authors: Miglietta Federica , Ragazzi Moira , Fernandes Bethania , Griguolo Gaia , Massa Davide , Girardi Fabio , Bottosso Michele , Bisagni Alessandra , Zarrilli Giovanni , Porra Francesca , Iannaccone Daniela , Dore Leocadia , Gaudio Mariangela , Santandrea Giacomo , Fassan Matteo , Lo Mele Marcello , De Sanctis Rita , Zambelli Alberto , Bisagni Giancarlo , Guarneri Valentina , Dieci Maria Vittoria
Published: July 2023

Supplementary Figure 3 – Matched evaluation of baseline TIL (b-TILs) and residual (RD-TILs) in the overall RD cohort (Fig. 3a), the high RD-TILs cohort (Fig. 3b) and in the low RD-TILs cohort (Fig. 3c).
Data 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

AbstractPurpose:
Clinical evidence indicates that treatment with estrogens elicits anticancer effects in ∼30% of patients with advanced endocrine-resistant estrogen receptor α (ER)-positive breast cancer. Despite the proven efficacy of estrogen therapy, its mechanism of action is unclear and this treatment remains underused. Mechanistic understanding may offer strategies to enhance therapeutic efficacy.
Experimental Design:
We performed genome-wide CRISPR/Cas9 screening and transcriptomic profiling in long-term estrogen-deprived ER+ breast cancer cells to identify pathways required for therapeutic response to the estrogen 17β-estradiol (E2). We validated findings in cell lines, patient-derived xenografts (PDX), and patient samples, and developed a novel combination treatment through testing in cell lines and PDX models.
Results:
Cells treated with E2 exhibited replication-dependent markers of DNA damage and the DNA damage response prior to apoptosis. Such DNA damage was partially driven by the formation of DNA:RNA hybrids (R-loops). Pharmacologic suppression of the DNA damage response via PARP inhibition with olaparib enhanced E2-induced DNA damage. PARP inhibition synergized with E2 to suppress growth and prevent tumor recurrence in BRCA1/2-mutant and BRCA1/2-wild-type cell line and PDX models.
Conclusions:
E2-induced ER activity drives DNA damage and growth inhibition in endocrine-resistant breast cancer cells. Inhibition of the DNA damage response using drugs such as PARP inhibitors can enhance therapeutic response to E2. These findings warrant clinical exploration of the combination of E2 with DNA damage response inhibitors in advanced ER+ breast cancer, and suggest that PARP inhibitors may synergize with therapeutics that exacerbate transcriptional stress.
Data from A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer
Authors: Miglietta Federica , Ragazzi Moira , Fernandes Bethania , Griguolo Gaia , Massa Davide , Girardi Fabio , Bottosso Michele , Bisagni Alessandra , Zarrilli Giovanni , Porra Francesca , Iannaccone Daniela , Dore Leocadia , Gaudio Mariangela , Santandrea Giacomo , Fassan Matteo , Lo Mele Marcello , De Sanctis Rita , Zambelli Alberto , Bisagni Giancarlo , Guarneri Valentina , Dieci Maria Vittoria
Published: July 2023

AbstractPurpose:
We aim to evaluate the prognostic significance of tumor-infiltrating lymphocyte on residual disease (RD-TIL) in HER2+ patients with breast cancer who failed to achieve pathologic complete response (pCR) after anti-HER2+ chemotherapy (CT)-based neoadjuvant treatment (NAT). We assessed the feasibility of combining the prognostic information provided by residual cancer burden (RCB) and RD-TILs into a composite score (RCB+TIL).
Experimental Design:
HER2+ patients with breast cancer treated with CT+anti-HER2-based NAT at three institutions were retrospectively included. RCB and TIL levels were evaluated on hematoxylin and eosin–stained slides from surgical samples according to available recommendations. Overall survival (OS) was used as an outcome measure.
Results:
A total of 295 patients were included, of whom 195 had RD. RCB was significantly associated with OS. Higher RD-TILs were significantly associated with poorer OS as compared with lower RD-TILs (15% cutoff). In multivariate analysis, both RCB and RD-TIL maintained their independent prognostic value. A combined score, RCB+TIL, was calculated from the estimated coefficient of RD-TILs and the RCB index in a bivariate logistic model for OS. The RCB+TIL score was significantly associated with OS. The C-index for OS of the RCB+TIL score was numerically higher than that of RCB and significantly higher than that of RD-TILs.
Conclusions:
We have reported an independent prognostic impact of RD-TILs after anti-HER2+CT NAT, which might underlie an imbalance of the RD microenvironment towards immunosuppressive features. We provided a new composite prognostic score based on RCB+TIL, which was significantly associated with OS and proved to be more informative than the isolated evaluation of RCB and RD-TILs.
Figure S5 from Proteogenomic Approaches for the Identification of NF1/Neurofibromin-depleted Estrogen Receptor–positive Breast Cancers for Targeted Treatment
Authors: Kim Beom-Jun , Zheng Ze-Yi , Lei Jonathan T. , Holt Matthew V. , Chen Anran , Peng Jianheng , Fandino Diana , Singh Purba , Kennedy Hilda , Dou Yongchao , Chica-Parrado María del Rosario , Bikorimana Emmanuel , Ye Dan , Wang Yunguan , Hanker Ariella B. , Mohamed Nada , Hilsenbeck Susan G. , Lim Bora , Asirvatham Jaya Ruth , Sreekumar Arun , Zhang Bing , Miles George , Anurag Meenakshi , Ellis Matthew J. , Chang Eric C.
Published: July 2023

Figure S5. IHC analysis on NF1 in baseline biopsies from the POL cohort.