1,721,057 research outputs found
CD166 modulates disease progression and osteolytic disease in multiple myeloma
Full text linkIndiana University-Purdue University Indianapolis (IUPUI)Multiple myeloma (MM) is an incurable malignancy characterized by the proliferation of neoplastic plasma cells in the bone marrow (BM) and by multiple osteolytic lesions throughout the skeleton. We previously reported that CD166 is a functional molecule on normal hematopoietic stem cells (HSC) that plays a critical role in HSC homing and engraftment, suggesting that CD166 is involved in HSC trafficking and lodgment. CD166, a member of the immunoglobulin superfamily capable of mediating homophilic interactions, has been shown to enhance metastasis and invasion in several tumors. However, whether CD166 is involved in MM and plays a role in MM progression has not been addressed. We demonstrated that a fraction of all human MM cell lines tested and MM patients’ BM CD138+ cells express CD166. Additionally, CD166+ cells preferentially home to the BM of NSG mice. Knocking-down (KD) CD166 expression on MM cells with shRNA reduced their homing to the BM. Furthermore, in a long-term xenograft model, NSG mice inoculated with CD166KD cells showed delayed disease progression and prolonged survival compared to mice receiving mock transduced cells. To examine the potential role of CD166 in osteolytic lesions, we first used a novel Ex Vivo Organ Culture Assay (EVOCA) which creates an in vitro 3D system for the interaction of MM cells with the bone microenvironment.
EVOCA data from MM cells lines as well as from primary MM patients’ CD138+ BM cells demonstrated that bone osteolytic resorption was significantly reduced when CD166 was absent on MM cells or calvarial cells. We then confirmed our ex vivo findings with intra-tibial inoculation of MM cells in vivo. Mice inoculated with CD166KD cells had significantly less osteolytic lesions. Further analysis demonstrated that CD166 expression on MM cells alters bone remodeling by inhibiting RUNX2 gene expression in osteoblast precursors and increasing RANKL to OPG ratio in osteoclast precursors. We also identified that CD166 is indispensable for osteoclastogenesis via the activation of TRAF6-dependent signaling pathways. These results suggest that CD166 directs MM cell homing to the BM and promotes MM disease progression and osteolytic disease. CD166 may serve as a therapeutic target in the treatment of MM
Synthesis of New Polymers Possessing Dense Triazole Backbone by Copper(I)-Catalyzed Azide–Alkyne Cycloaddition
No full textThis doctoral thesis is related to the following article: Xu L, Kamon Y, Hashidzume A. Synthesis of a New Polyanion Possessing Dense 1,2,3-Triazole Backbone. Polymers. 2021; 13(10):1614. https://doi.org/10.3390/polym13101614This doctoral thesis is related to the following article: Xu Linlin, Nakahata Masaki, Kamon Yuri, et al. Synthesis of an alternating copolymer of the dense 1,2,3‐triazole backbone carrying t‐butyl ester and nitrile side chains. Journal of Polymer Science 341, 628 (2023); https://doi.org/10.1002/pol.20230760
ドウイチショクバイアジド－アルキンカンカフカニヨルコウミツドトリアゾールコッカクヲユウスルシンキポリマーノゴウセイ
Full text linkThis doctoral thesis is related to the following article: Xu L, Kamon Y, Hashidzume A. Synthesis of a New Polyanion Possessing Dense 1,2,3-Triazole Backbone. Polymers. 2021; 13(10):1614. https://doi.org/10.3390/polym13101614This doctoral thesis is related to the following article: Xu Linlin, Nakahata Masaki, Kamon Yuri, et al. Synthesis of an alternating copolymer of the dense 1,2,3‐triazole backbone carrying t‐butyl ester and nitrile side chains. Journal of Polymer Science 341, 628 (2023); https://doi.org/10.1002/pol.20230760
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Transformer-based Point Cloud Processing and Analysis for LiDAR Remote Sensing
No full textThe processing and analysis of Light Detection and Ranging (LiDAR) point cloud data, a fundamental task in Three-Dimensional (3D) computer vision, is essential for a wide range of remote sensing applications. However, the disorder, sparsity, and uneven spatial distribution of LiDAR point clouds pose significant challenges to effective and efficient processing. In recent years, Transformers have demonstrated notable advantages over traditional deep learning methods in computer vision, yet designing Transformer-based frameworks tailored to point clouds remains an underexplored topic. This thesis investigates the potential of Transformer models for accurate and efficient LiDAR point cloud processing.
Firstly, a 3D Global-Local (GLocal) Transformer Network (3DGTN) is introduced to capture both local and global context, thereby enhancing model accuracy for LiDAR data. This design not only ensures a comprehensive understanding of point cloud characteristics but also establishes a foundation for subsequent efficient Transformer frameworks.
Secondly, a fast point Transformer network with Dynamic Token Aggregation (DTA-Former) is proposed to improve model speed. By optimizing point sampling, grouping, and reconstruction, DTA-Former substantially reduces the time complexity of 3DGTN while retaining its strong accuracy.
Finally, to further reduce time and space complexity, a 3D Learnable Supertoken Transformer (3DLST) is presented. Building on DTA-Former, 3DLST employs a novel supertoken clustering strategy that lowers computational overhead and memory consumption, achieving state-of-the-art performance across multi-source LiDAR point cloud tasks in terms of both accuracy and efficiency.
These Transformer-based frameworks contribute to more robust and scalable LiDAR point cloud processing solutions, supporting diverse remote sensing applications such as urban planning, environmental monitoring, and autonomous navigation. By enabling efficient yet high-accuracy analysis of large-scale 3D data, this work fosters further research and innovation in LiDAR remote sensing
Correction Methods for Non-Stationary Noise Floor in Sentinel-1 Images Using Convex Optimization
Full text linkSynthetic aperture radar (SAR) is a method of creating images of the surface of the Earth by emitting and receiving radar waves. Sentinel-1 is a SAR platform made by the European Space Agency (ESA) that provides a source of SAR images open to the public through the operation of two satellites. Due to the non-uniform radiation pattern projected from the satellite's antenna, there are significant non-stationary noise floor intensity patterns that distract from the desired measurements, which are particularly significant in certain types of image modes, namely Extra Wide and Interferometric Wide modes. While ESA provides a default noise floor estimate with each Sentinel-1 product, with the intention that it be subtracted from the original image so the result is homogeneous, there is clear evidence that it is miscalibrated. This Masters thesis presents two novel methods for estimating the noise floor patterns in the images that are demonstrated to be improvements over the default noise floor. The first method presents a way to dynamically construct and apply linear rescaling to the default noise floor estimate over different sections of the images, called subswaths, by use of least squares optimization. While the method is successful in improving image quality, it is not totally effective because the default noise floor is mis-fit in a non-linear manner. The second method constructs a new noise floor as a power function of the radiation pattern power by using linear programming and least squares optimization. This successfully compensates for the non-linear mis-fit, resulting in an overall increase in image quality, albeit with greater parametric complexity. These methods greatly improve the intrinsic value of Sentinel-1 images in scenarios where the noise floor dominates, such as in cross-polarized images and images where the physical materials result in lower backscatter intensity
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Deep Learning Based Building Extraction from High-Resolution Remote Sensing Images
Full text linkBuilding extraction from remote sensing images is a critical task to support various applications such as cartography, disaster response, and urban planning. The automation of this task is an active research area due to the time-consuming nature and high expense associated with the manual approach. However, traditional computer vision methods rely on handcrafted features and human knowledge, leading to the lack of the ability to leverage big remote sensing data. Although recently developed deep learning based methods brought significant advancements in the identification and coarse annotation of buildings, the accuracy and precision of extracted buildings are still insufficient for high-precision applications such as surveying and mapping.
This thesis presents two works aiming at enhanced building extraction from high-resolution remote sensing images by tackling key issues in building footprint extraction and building vectorization. For building footprint extraction, to address the heterogeneous noisy features around building boundaries, this thesis presents a deep learning strategy that incorporates a topography-aware loss (TAL) within a multi-resolution fusion architecture to increase the accuracy of boundaries in building segmentation. For building vectorization, to address the interference caused by noise and obstruction from shadows and trees around buildings and the limited receptive field in deep learning networks, this thesis presents a framework that combines a deep learning based building edge detection strategy and a geometry-guided building polygon reconstruction method for improved building outline vectorization in terms of vertex accuracy. Comparative experimental results on high-resolution remote sensing building datasets demonstrate significant improvements in building boundary accuracy and polygon vertex accuracy respectively over state-of-the-art methods. Hence, both works provide new means to address challenges posed by complex environmental conditions around buildings captured in remote sensing images and enable accurate building segmentation and vectorization towards automatic building extraction for high-precision applications
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