Voluminous Storage and Rapid Magma Ascent Beneath La Palma Revealed by Seismic Tomography

Seismic tomography provides a window into magmatic plumbing systems; however, obtaining sufficient data for ‘real-time’ imaging is challenging. Until now, syn-eruptive tomography has not been successfully demonstrated. For the first time, we obtained high-resolution images of Earth's interior during an ongoing volcanic eruption. We used data from 11,349 earthquakes, most of which during La Palma eruption (19 September-13 December, 2021), to perform travel-time seismic tomography. We present high-precision earthquake relocations and 3D distributions of P and S-wave velocities highlighting the geometry of magma sources. We identified three distinct structures: (1) a shallow localised region (< 3 km) of hydrothermal alteration; (2) spatially extensive, consolidated, oceanic crust extending to ~10 km depth and; (3) a large (> 400 km3) sub-crustal magma-filled rock volume intrusion extending from ~7 to 25 km depth. Our results suggest that this large magma reservoir feeds the La Palma eruption continuously for almost three months. Prior to eruption onset, magma ascended from ~10 km depth to the surface in < 7 days. In the upper 3 km, melt migration is along the western contact between consolidated oceanic crust and altered hydrothermal material. Similar structural weaknesses along the eastern contact could potentially cause new eruptive centres in the future.

First Carpometacarpal Joint Anatomy and Osteoarthritis: MR Imaging Overview

The first carpometacarpal (CMC) joint consists of seven ligaments. The magnetic resonance imaging (MRI) examination of the first CMC joint should be performed in a high field 1.5/3 T MRI with a dedicated hand coil for high-resolution images. Degeneration of anterior oblique ligament (AOL) is the most important cause for the development of osteoarthritis of first CMC joint. Since the AOL undergoes a predictable pattern of alteration at its metacarpal attachment as degeneration proceeds, MRI imaging can provide an accurate assessment of this ligament.

Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

A Super-Resolution Reconstruction Driven Helmet Detection Workflow

The degrading of input images due to the engineering environment decreases the performance of helmet detection models so as to prevent their application in practice. To overcome this problem, we propose an end-to-end helmet monitoring system, which implements a super-resolution (SR) reconstruction driven helmet detection workflow to detect helmets for monitoring tasks. The monitoring system consists of two modules, the super-resolution reconstruction module and the detection module. The former implements the SR algorithm to produce high-resolution images, the latter performs the helmet detection. Validations are performed on both a public dataset as well as the realistic dataset obtained from a practical construction site. The results show that the proposed system achieves a promising performance and surpasses the competing methods. It will be a promising tool for construction monitoring and is easy to be extended to corresponding tasks.

Deep learning for enhancing multi-source reverse time migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

Deep learning for enhancing multi-source reverse time migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

BioInfograph: An Online Tool to Design and Display Multi-Panel Scientific Figure Interactively

Many fit-for-purpose bioinformatics tools generate plots to interpret complex biological data and illustrate findings. However, assembling individual plots in different formats from various sources into one high-resolution figure in the desired layout requires mastery of commercial tools or even programming skills. In addition, it is a time-consuming and sometimes frustrating process even for a computationally savvy scientist who frequently takes a trial-and-error iterative approach to get satisfactory results. To address the challenge, we developed bioInfograph, a web-based tool that allows users to interactively arrange high-resolution images in diversified formats, mainly Scalable Vector Graphics (SVG), to produce one multi-panel publication-quality composite figure in both PDF and HTML formats in a user-friendly manner, requiring no programming skills. It solves stylesheet conflicts of coexisting SVG plots, integrates a rich-text editor, and allows creative design by providing advanced functionalities like image transparency, controlled vertical stacking of plots, versatile image formats, and layout templates. To highlight, the sharable interactive HTML output with zoom-in function is a unique feature not seen in any other similar tools. In the end, we make the online tool publicly available at https://baohongz.github.io/bioInfograph while releasing the source code at https://github.com/baohongz/bioInfograph under MIT open-source license.

Review on Gold Nanoparticles-Based Biosensors in Clinical and Non-Clinical Applications

Gold nanoparticles (GNP) acquire unique properties that have made significant contributions to clinical and non-clinical fields, specifically in the application of GNP’s for designing biosensor devices in which exhibit novel functional properties. Many properties of GNP’s are reviewed in this literature including optical properties, biocompatibility, conductivity, catalytic properties, high surface-to-volume ratio, and high density of the GNPs, that make them excellent in the application of constructing GNP-based biosensors. This literature review covers a specific comparison between the optical, electrochemical, and piezoelectric biosensors, as these are the three most common GNP-based biosensors. Optical biosensors are optimal due to their ability to cater to surface modification, which then leads to the ability for selective bonding. Furthermore, with the use of GNP and the sensor's non-invasive and non-toxic method of use, high-resolution images and signals can be formed. The sensitivity and specificity of electrochemical biosensors with the conductivity of GNPs, the electrodes of this stable biosensor can detect tumour markers in the human body. Piezoelectric biosensors are mass sensitive sensors and with the use of GNP, it amplifies the changes in mass. Through this, these sensors progress to be immunosensors which determine microorganisms and macromolecular compounds. As well, this review will conclude with an outline of present and future research recommendations for real-world application of the three GNP-based biosensors discussed.