Three-dimensional joint models are important tools for investigating mechanisms related to normal and pathological joints. Often these models necessitate accurate three-dimensional joint surface geometric data so that reliable model results can be obtained; however, in models based on small joints, this is often problematic due to limitations of the present techniques. These limitations include insufficient measurement precision, the requirement of contact for the measurement process, and lack of entire joint description. This study presents a new non-contact method for precise determination of entire joint surfaces using multistation digital photogrammetry (MDPG) and is demonstrated by determining the cartilage and subchondral bone surfaces of the cat patellofemoral (PF) joint. The digital camera–lens setup was precisely calibrated using 16 photographs arranged to achieve highly convergent geometry to estimate interior and distortion parameters of the camera–lens setup. Subsequently, six photographs of each joint surface were then acquired for surface measurement. The digital images were directly imported to a computer and newly introduced semi-automatic computer algorithms were used to precisely determine the image coordinates. Finally, a rigorous mathematical procedure named the bundle adjustment was used to determine the three-dimensional coordinates of the joint surfaces and to estimate the precision of the coordinates. These estimations were validated by comparing the MDPG measurements of a cylinder and plane to an analytical model. The joint surfaces were successfully measured using the MDPG method with mean precision estimates in the least favorable coordinate direction being 10.3 μm for subchondral bone and 17.9 μm for cartilage. The difference in measurement precision for bone and cartilage primarily reflects differences in the translucent properties of the surfaces.
Research Article| January 14, 2014 Frequency‐Dependent Seismic Attenuation in the Eastern United States as Observed from the 2011 Central Virginia Earthquake and Aftershock Sequence Daniel E. McNamara; Daniel E. McNamara aU.S. Geological Survey, MS966, Box 25046, Denver, Colorado 80225 Search for other works by this author on: GSW Google Scholar Lind Gee; Lind Gee bU.S. Geological Survey, Albuquerque Seismological Laboratory, P.O. Box 82010, Albuquerque,New Mexico 87198‐2010 Search for other works by this author on: GSW Google Scholar Harley M. Benz; Harley M. Benz aU.S. Geological Survey, MS966, Box 25046, Denver, Colorado 80225 Search for other works by this author on: GSW Google Scholar Martin Chapman Martin Chapman cDepartment of Geosciences, 4044 Derring Hall, Virginia Tech, Blacksburg, Virginia 24061 Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2014) 104 (1): 55–72. https://doi.org/10.1785/0120130045 Article history first online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Daniel E. McNamara, Lind Gee, Harley M. Benz, Martin Chapman; Frequency‐Dependent Seismic Attenuation in the Eastern United States as Observed from the 2011 Central Virginia Earthquake and Aftershock Sequence. Bulletin of the Seismological Society of America 2014;; 104 (1): 55–72. doi: https://doi.org/10.1785/0120130045 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract Ground shaking due to earthquakes in the eastern United States (EUS) is felt at significantly greater distances than in the western United States (WUS) and for some earthquakes it has been shown to display a strong preferential direction. Shaking intensity variation can be due to propagation path effects, source directivity, and/or site amplification. In this paper, we use S and Lg waves recorded from the 2011 central Virginia earthquake and aftershock sequence, in the Central Virginia Seismic Zone, to quantify attenuation as frequency‐dependent Q(f). In support of observations based on shaking intensity, we observe high Q values in the EUS relative to previous studies in the WUS with especially efficient propagation along the structural trend of the Appalachian mountains. Our analysis of Q(f) quantifies the path effects of the northeast‐trending felt distribution previously inferred from the U.S. Geological Survey (USGS) “Did You Feel It” data, historic intensity data, and the asymmetrical distribution of rockfalls and landslides. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
The spatial resolution of Earth Observation (EO) images plays a key role in building footprint extraction. For the spatial resolution enhancement, deep learning-based image super-resolution methods have been widely used due to their remarkable performance. Transformer-based networks are effective and has drawn much attention in computer vision but underutilized in remote sensing, especially for super-resolving building datasets. Therefore, in this paper, we developed a novel transformer-based Single-Image Super-Resolution (SISR) method, named Pyramid Vision Transformer-Residual Feature Aggregation Network (PVT_RFANet), to improve the spatial resolution of building datasets. Specifically, the PVT v2 network was embedded into our Momentum Spatial-Channel Attention Residual Feature Aggregation Network (MSCA-RFANet). Moreover we conducted a comparative study to compare our method with Bicubic interpolation (BI), Super-resolution Convolutional Neural Network (SRCNN), Deep Recursive Residual Network (DRRN), SRResNet, and MSCA-RFANet. Using Peak Signal-Noise Ratio (PSNR) and Similarity Structure Index Measurement (SSIM) as the evaluation metrics, our method showed highest performance with the PSNR of 22.01 dB and the SSIM of 0.50 on the WHU Building Dataset, which demonstrated the superior performance of the proposed method.
Abstract Genomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma.
Abstract. The need for reliable systems for capturing precise detail in tunnels has increased as the number of tunnels (e.g., for cars and trucks, trains, subways, mining and other infrastructure) has increased and the age of these structures and, subsequent, deterioration has introduced structural degradations and eventual failures. Due to the hostile environments encountered in tunnels, mobile mapping systems are plagued with various problems such as loss of GNSS signals, drift of inertial measurements systems, low lighting conditions, dust and poor surface textures for feature identification and extraction. A tunnel mapping system using alternate sensors and algorithms that can deliver precise coordinates and feature attributes from surfaces along the entire tunnel path is presented. This system employs image bridging or visual odometry to estimate precise sensor positions and orientations. The fundamental concept is the use of image sequences to geometrically extend the control information in the absence of absolute positioning data sources. This is a non-trivial problem due to changes in scale, perceived resolution, image contrast and lack of salient features. The sensors employed include forward-looking high resolution digital frame cameras coupled with auxiliary light sources. In addition, a high frequency lidar system and a thermal imager are included to offer three dimensional point clouds of the tunnel walls along with thermal images for moisture detection. The mobile mapping system is equipped with an array of 16 cameras and light sources to capture the tunnel walls. Continuous images are produced using a semi-automated mosaicking process. Results of preliminary experimentation are presented to demonstrate the effectiveness of the system for the generation of seamless precise tunnel maps.
8000 Background: Upfront ASCT for NDTE MM remains under evaluation with high MRD rates following novel induction and consolidation (cons) strategies. Current phase 3 trials support ASCT, however these employ lenalidomide maintenance which predominantly benefits standard risk (SR) patients (pts). The CARDAMON trial investigated the role of ASCT using K based induction and maintenance. Methods: NDTE pts received 4 x KCd induction (K 20/56 mg/m 2 biweekly, C 500 mg D 1,8,15, d 40mg weekly) before 1:1 randomisation to ASCT or 4 x KCd cons. All received 18 months K maintenance (56mg/m 2 D1,8,15). Flow cytometric MRD (10 -5 ) was assessed post induction, pre-maintenance and at 6 months maintenance. Primary endpoints were ≥VGPR post induction and 2-year PFS from randomisation. 210 randomised pts were needed to exclude a 10% non-inferiority margin with 15% 1-sided alpha, 80% power. Results: 281 pts were registered, median age 59y (33–74), 24% high risk [t(4;14), t(14;16), t(14;20) or del(17p)]. Post induction, ≥VGPR rate was 58.5%, ORR was 87% with similar responses for high risk vs SR. 52 pts did not proceed to PBSCH (6 MR, 16 PD, 19 toxicity, 4 deaths: 3 infection, 1 cardiac event, 7 other). 109 were randomised to ASCT, 109 to KCd cons. ≥VGPR rate was 78.5% after cons and 80.0% after ASCT (p = 0.8). Median KCd cons dose was 55.5 mg/m 2 , 99 (90.8%) pts completed 4 cycles, 104 (95.4%) pts received ASCT. After 2.6 years follow-up, median PFS was not reached for ASCT vs 3.8 years for cons (HR: 0.82 (70% CI 0.65, 1.05, p = 0.4). Observed 2-year PFS for ASCT was 75.5% vs 70.7% for cons; calculated difference in 2-year PFS rate (cons vs ASCT) was -4.5% (70% CI -9.2%, +1.1%, non-inferior). High risk pts had inferior outcomes to SR overall regardless of randomisation (2-year PFS ASCT: 52% vs 82% (HR 4.09); cons 48% vs 77% (HR 2.83)). 2 year PFS did not differ according to randomisation: SR 82% (ASCT) vs 77% (cons) HR: 1.29 (0.71-2.35); high risk: 52% (ASCT) vs 48% (cons) HR: 1.06 (0.50-2.23). MRD negativity post induction was 24.3% and similar by genetic risk. MRD negative rates were higher post ASCT (53.1%) than cons (35.8%) (p = 0.02) independent of genetics: SR 49% (ASCT) vs 36% (cons); high risk: 57% (ASCT) vs 32% (cons). G3+ adverse events to induction were infections (18.7%), hypertension (11.2%), anaemia (10.4%), cardiac disorders (3.6%), vomiting (2.2%), fatigue (2.2%), diarrhoea (1.8%). Conclusions: In NDMM receiving KCd induction and K maintenance, KCd cons was non-inferior to ASCT. High risk pts had inferior outcomes, that were not influenced by ASCT or cons randomisation. Clinical trial information: NCT02315716. [Table: see text]
Abstract Precision medicine holds great promise to improve outcomes in cancer, including haematological malignancies. However, there are few biomarkers that influence choice of chemotherapy in clinical practice. In particular, multiple myeloma requires an individualized approach as there exist several active therapies, but little agreement on how and when they should be used and combined. We have previously shown that a transcriptomic signature can identify specific bortezomib- and lenalidomide-sensitivity. However, gene expression signatures are challenging to implement clinically. We reasoned that signatures based on the presence or absence of gene mutations would be more tractable in the clinical setting, though examples of such signatures are rare. We performed whole exome sequencing as part of the CARDAMON trial, which employed carfilzomib-based therapy. We applied advanced machine learning approaches to discover mutational patterns predictive of treatment outcome. The resulting model accurately predicted progression-free survival (PFS) both in CARDAMON patients and in an external validation set of patients from the CoMMpass study who had received carfilzomib. The signature was specific for carfilzomib therapy and was strongly driven by genes on chromosome 1p36. Importantly, patients predicted to be carfilzomib-sensitive had a longer PFS when treated with carfilzomib/lenalidomide/dexamethasone than with bortezomib/carfilzomib/dexamethasone. However, in those predicted to be carfilzomib-insensitive, the latter therapy may have been capable of eradicating carfilzomib-resistant clones. We propose that the signature can be used to make rational therapeutic decisions and could be incorporated into future clinical trials.
This article presents a semi-automated method to extract the lane features along the curved roads from mobile laser scanning (MLS) point clouds. The proposed method consists of four steps. After data pre-processing, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces. Then, textual and directional road markings such as arrows, symbols, and words, to inform drivers in necessary cases, are detected by intensity thresholding and conditional Euclidean clustering algorithms. Furthermore, lane markings are extracted by local intensity analysis and distance thresholding methods according to road design standards, because they are more regular along the road. Finally, centerline points on lanes are estimated based on the coordinates of extracted lane markings. Our method shows strong feasibility and robustness when creating high-definition (HD) maps from MLS data, by increasing the number of blocks in the curve and the distance threshold control in curved lane centerline extraction. Quantitative evaluations show that the average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. The generated lane centerlines are evaluated by overlaying them on manually labeled reference buffers from 4 cm resolution orthoimagery. The comparative study indicates that the proposed methods can achieve higher accuracy and robustness than most state-of-the-art methods.
The prototype development of VIASAT, a precise mobile survey system for road inventory and associated GIS applications, is discussed. The system integrates inertial, satellite, and charge coupled device (CCD) camera technology to achieve an overall positioning accuracy of 0.3 m (1 ft) or better for all objects within a 50 m corridor on both sides of a highway. Combining the high positioning accuracy of Global Positioning System (GPS) with the precise pointing accuracy of inertial navigation system (INS) a cluster of CCD cameras will be accurately positioned and oriented at any instant of time. Using this information, objects along the highway can be precisely surveyed in three dimensions by processing the digital image information of the CCD camera cluster. The system concepts are discussed. Special emphasis is given to sensor integration, testing of the INS/GPS component and data flow from the acquisition process to the workstation environment.
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