Single Branch Research Articles

Rare Cause of Basal Ganglionic Haematoma: Rete Anomaly of Middle Cerebral Artery

ABSTRACT A 52-year-old female without co-morbidities presented to us with a history of sudden onset of severe headache and vomiting followed by drowsiness. Computed tomography brain plain was done which showed left capsuloganglionic bleed with intraventricular haemorrhage. In view of normal blood pressure and no co-morbidities, we suspected a vascular pathology and hence planned for digital subtraction angiogram (DSA). DSA showed rete anomaly of the middle cerebral artery (MCA) with aneurysm arising from the one of the anterior branches. The patient was taken up for surgery and left pterional craniotomy and evacuation of haematoma with clipping of aneurysm was done. Rete anomaly of MCA is an extremely rare entity with the incidence of 0.11%. It is web-like anomaly of MCA that does not coalesce to form a prominent, large single branch from the plexiform vessels in the foetal stage. The most common form of presentation is haemorrhagic stroke. The scan mimics a hypertensive basal ganglionic haematoma. Any non-hypertensive patient with a basal ganglionic haematoma needs an angiogram to look for other causes. In cases with rete anomaly with aneurysm, clipping is advocated over endovascular procedures as it is difficult to negotiate through these tiny vessels.

Discussion on the Integrated Development Path of Financial Accounting and Management Accounting

The gradual development of economy and rapid progress of science and technology have promoted human society to step forward into the era of digital economy, and then promoted the transformation of financial accounting function based on accounting and supervision to management accounting function based on value creation and value increment. However, both financial accounting and management accounting are branches of the accounting discipline, and the professional knowledge of a single branch is slightly insufficient to solve problems in a certain field. Only by combining and integrating the knowledge of multiple branches, and analyzing and solving problems from multiple perspectives can enterprises achieve sustainable development. Therefore, in combination with the background and direction of industry development in the era of digital economy, this paper analyzes the necessity of the integrated development of financial accounting and management accounting based on the functional positioning of enterprise financial accounting and management accounting, and discusses the integrated development mode of financial accounting and management accounting from the aspects of goal orientation, platform construction and content construction. And then put forward the two in the concept of mutual integration, content mutual penetration, system framework, technical personnel mutual integration and other aspects of the feasible path, in order to improve the accounting staff's business ability and comprehensive quality, conform to the market demand and the development of The Times.

Customer Service Combining Human Operators and Virtual Agents: A Call for Multidisciplinary AI Research

The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

Testing a Method Based on an Improved UNet and Skeleton Thinning Algorithm to Obtain Branch Phenotypes of Tall and Valuable Trees Using Abies beshanzuensis as the Research Sample.

Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang-Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis, and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R2) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants.

Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs.

MODE: Monocular omnidirectional depth estimation via consistent depth fusion

Monocular depth estimation has seen significant progress in recent years, especially in outdoor scenes. However, depth estimation results are not satisfying in omnidirectional images. As compared to perspective images, estimating the depth map from an omnidirectional image captured in the outdoor scene, using neural networks, has two additional challenges: (i) the depth range of outdoor images varies a lot across different scenes, making it difficult for the depth network to predict accurate depth results for training with an indoor dataset, besides the maximum distance in outdoor scenes mostly stays the same as the camera sees the sky, but depth labels in this region are entirely missing in existing datasets; (ii) a standard representation of omnidirectional images introduces spherical distortion, which causes difficulties for the vanilla network to predict accurate relative structural depth details. In this paper, we propose a novel network-MODE by giving special considerations to those challenges and designing a set of flexible modules for improving the performance of omnidirectional depth estimation. First, a consistent depth structure module is proposed to estimate a consistent depth structure map, and the predicted structural map can improve depth details. Second, to suit the characteristics of spherical sampling, we propose a strip convolution fusion module to enhance long-range dependencies. Third, rather than using a single depth decoder branch as in previous methods, we propose a semantics decoder branch to estimate sky regions in the omnidirectional image. The proposed method is validated on three widely used datasets, demonstrating the state-of-the-art performance. Moreover, the effectiveness of each module is shown through an ablation study on real-world datasets. Our code is available at https://github.com/lkku1/MODE.© 2017 Elsevier Inc. All rights reserved.

Compact Dual-Band MIMO Antenna With Shared Decoupling Structure for 5G Mobile Terminals

In this letter, a compact dual-band MIMO antenna, consisting of four antenna pairs, is presented for 5G mobile terminal applications. Each pair includes two tightly-arranged monopoles. By folding the L-shaped monopoles into U-shaped structures, both the 3.5 GHz band and 4.9 GHz band can share the same decoupling inductor. By adding stop-band filter on the monopole, dual-band operation can be provided by a single branch. The size of the dual-band antenna pair is merely 19×4 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The measured -6 dB bandwidth can cover 3.4-3.6 GHz and 4.8-5.0 GHz, with port isolation higher than 12 dB. The measured radiation efficiency varies from 41% to 59% in the lower band, and varies from 38% to 62% in the upper band.

Semi-supervised cross-domain feature fusion classification network for coastal wetland classification with hyperspectral and LiDAR data

Multi-source remote sensing monitoring plays a crucial part in the ecological protection and restoration of coastal wetlands. However, due to the inaccessible of wetlands environment, lacking of labeled samples is a challenge in wetland classification. In this article, an unsupervised cross-domain feature fusion and supervised classification network (UF2SCN) is proposed for coastal wetland classification, which fuses hyperspectral image (HSI) and light detection and ranging (LiDAR) data. First, an unsupervised single branch end to end network is developed to get HSI and LiDAR fusion feature, in which a feature extraction model with spectral attention is deployed to obtain the average distribution characteristics of all samples, and the HSI and LiDAR data is utilized to guide the whole process. Second, a supervised classification network with spatial attention is applied to used fusion feature for classification, which uses the limited samples. Finally, a two stages training strategy is proposed to improve the ability of feature fusion. Experiments conducted on two coastal wetland datasets created by ourselves prove the validity of the proposed method on HSI and LiDAR classification for coastal wetland.

Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations.

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.

Differentiated digestion resistance and physicochemical properties of linear and α-1,2/α-1,3 branched isomaltodextrins prepared by 4,6-α-glucanotransferase and branching sucrases

Isomaltodextrins (IMDs) are starch-based dietary fibers (DF) prepared enzymatically, which show great potential as a functional food ingredient. In this study, a series of novel IMDs with diverse structures were generated by 4,6-α-glucanotransferase GtfBΔN from Limosilactobacillus fermentum NCC 3057, combined with two α-1,2 and α-1,3 branching sucrases. Results indicated that α-1,2 and α-1,3 branching significantly improved the DF contents of α-1,6 linear products up to 60.9–62.8%. When altering the ratios of [sucrose]/[maltodextrin], IMDs containing 25.8–89.0% α-1,6 bonds, 0–59.6% α-1,2 bonds and 0–35.1% α-1,3 bonds and Mw ranged from 1967 to 4876 Da were obtained. Physicochemical property analysis showed that grafting with α-1,2 or α-1,3 single glycosyl branches can improve the solubility of the α-1,6 linear product, in which α-1,3 branched products were better. Moreover, α-1,2 or α-1,3 branching did no effect on the viscosity of the products but Mw did, the larger Mw the greater viscosity. In addition, α-1,6 linear and α-1,2 or α-1,3 branched IMDs all exhibited strong acid-heating stabilities, freeze–thaw stabilities, and good resistance to browning caused by the Maillard reaction. Branched IMDs showed excellent storage stabilities at room temperature for one year at a concentration of 60%, whereas 45% α-1,6 linear IMD precipitated quickly within 12 h. Most importantly, α-1,2 or α-1,3 branching remarkably increased the contents of resistant starch in the α-1,6 linear IMDs to 74.5–76.8%. These clear qualitative assessments demonstrated the outstanding processing and application properties of the branched IMDs and were expected to provide valuable perspectives toward the technological innovation of functional carbohydrates.

New Fusion Network with Dual-Branch Encoder and Triple-Branch Decoder for Remote Sensing Image Change Detection

Deep learning plays a highly essential role in the domain of remote sensing change detection (CD) due to its high efficiency. From some existing methods, we can observe that the fusion of information at each scale is quite vital for the accuracy of the CD results, especially for the common problems of pseudo-change and the difficult detection of change edges in the CD task. With this in mind, we propose a New Fusion network with Dual-branch Encoder and Triple-branch Decoder (DETDNet) that follows a codec structure as a whole, where the encoder adopts a siamese Res2Net-50 structure to extract the local features of the bitemporal images. As for the decoder in previous works, they usually employed a single branch, and this approach only preserved the fusion features of the encoder’s bitemporal images. Distinguished from these approaches, we adopt the triple-branch architecture in the decoder for the first time. The triple-branch structure preserves not only the dual-branch features from the encoder in the left and right branches, respectively, to learn the effective and powerful individual features of each temporal image but also the fusion features from the encoder in the middle branch. The middle branch utilizes triple-branch aggregation (TA) to realize the feature interaction of the three branches in the decoder, which enhances the integrated features and provides abundant and supplementary bitemporal feature information to improve the CD performance. The triple-branch architecture of the decoder ensures that the respective features of the bitemporal images as well as their fused features are preserved, making the feature extraction more integrated. In addition, the three branches employ a multiscale feature extraction module (MFE) per layer to extract multiscale contextual information and enhance the feature representation capability of the CD. We conducted comparison experiments on the BCDD, LEVIR-CD, and SYSU-CD datasets, which were created in New Zealand, the USA, and Hong Kong, respectively. The data were preprocessed to contain 7434, 10,192, and 20,000 image pairs, respectively. The experimental results show that DETDNet achieves F1 scores of 92.7%, 90.99%, and 81.13%, respectively, which shows better results compared to some recent works, which means that the model is more robust. In addition, the lower FP and FN indicate lower error and misdetection rates. Moreover, from the analysis of the experimental results, compared with some existing methods, the problem of pseudo-changes and the difficulty of detecting small change areas is better solved.

The identity of Sasaoblongula C.H.Hu (Poaceae, Bambusoideae, Arundinarieae): evidence from morphology and molecular data.

Sasaoblongula was described in 1987 based on a cultivated plant at the bamboo garden of Sun Yat-sen University. This species has two or three branches at the upper nodes, which differ from the rest of Sasa species that have a single branch per node. During the field trip to Baishi Town, Yunfu City, Guangdong Province in July 2021, one bamboo species with oblong foliage leaves was collected and matches the isotype. Then, our question was to test the identity of S.oblongula concerning other Sasa species based on morphology and molecular data. To do that, we sequenced the whole chloroplast genome of S.oblongula and did a phylogenetic analysis. Our morphological results indicate that the new collection is S.oblongula. The phylogenetic tree showed that S.oblongula is close to Pseudosasa, instead of Sasa species. Therefore, we transferred it to the genus Pseudosasa, and a revised description of P.oblongula is provided here.

Induction of tetraspanin 13 contributes to the synergistic anti-inflammatory effects of parasympathetic and sympathetic stimulation in macrophages

The autonomic nervous system plays an important role in the regulation of peripheral inflammation. Sympathetic nervous activation stimulates inflammatory gene expression and cytokines, whereas parasympathetic nervous activation suppresses the production of inflammatory cytokines by immune cells. However, most studies on the relationship between the autonomic nervous system and immune processes have analyzed a single branch of the autonomic nerves in isolation. Therefore, this study aimed to examine the effects of sympathetic and parasympathetic stimulation on macrophages, which are controlled by autonomic regulation.Macrophages were stimulated with lipopolysaccharide (LPS) to induce TNF-α. Then, the effects of β2 adrenergic receptor and α7 nicotinic acetylcholine receptor activation on TNF-α production were assessed using concentration-dependent assays. RNA-seq data were also used to identify genes whose expression was enhanced by parasympathetic and sympathetic stimulation.The simultaneous activation of β2 adrenergic receptors and α7 nicotinic acetylcholine receptors suppressed LPS-induced TNF-α production in a concentration-dependent manner. Moreover, simultaneous activation of these receptors had synergistic anti-inflammatory effects and induced Tspan13 expression, thereby contributing to anti-inflammatory mechanisms in macrophages.Our study revealed the synergistic anti-inflammatory effects of the parasympathetic and sympathetic stimulation of macrophages. Our results suggest that targeting both sympathetic and parasympathetic signaling is a promising therapeutic approach for inflammatory diseases.

A Hybrid Mock Circulatory Loop for Fluid Dynamic Characterization of 3D Anatomical Phantoms.

This work presents the development of a Hybrid Mock Circulatory Loop (HMCL) to simulate hemodynamics at patient-specific level in terms of both 3D geometry and inlet/outlet boundary conditions. Clinical data have been processed to define the morphological and functional patient-specific settings. A piston pump is used to impose a parametric flow rate profile at the inlet of the hemodynamic circuit. In order to guarantee the physiological pressure and flow conditions, a specific hybrid chamber system including a real-time control has been designed and implemented. The developed system was validated firstly in a single outlet branch model and, secondly, on a 3D printed patient-specific multi-branch phantom. Finally, for the 3D phantom, the outlet flow profiles were compared with the corresponding in-vivo flow data. Results showed that the root mean squared error between the prescribed setpoint and the measured pressures was always below 3mmHg (about 2.5%) for all cases. The obtained flow profiles for the patient-specific model were in agreement with the related functional in-vivo data. The capability to reproduce physiological hemodynamics condition, with high-fidelity, plays a significant role in the cardiovascular research. The developed platform can be used to assess the performances of cardiovascular devices, to validate numerical simulations, and to test imaging systems.

Direct stress minimization in electro‐mechanical metamaterials

AbstractMetamaterials are artificially created multiscale materials with many different applications [1]. We assume periodic microstructure. No specific length scale is demanded for the differentiation between the scales. Instead, we consider the microscale as consisting of repeating unit cells, whereas the macroscale is the scale, where desired and sometimes unusual physical effects occur. The precise arrangement and geometry at the microscale level is responsible for the macroscopic material behavior, which can substantially differ from the original components it is made from. This way, unique material properties otherwise not found in nature (e.g. negative refractive materials [2]) are possible. Most previous research regarding metamaterials concentrated only on a single physical branch in each case, e.g. electromagnetic or acoustic metamaterials [3,4].In this contribution we present a class of theoretical electro‐mechanical metamaterials by combining insulating and conducting materials. Our aim is to directly control and reduce the resulting total stress of insulating materials by counteracting the mechanical stress through the application of an electric field, which is created by a conducting material. The solution of the resulting minimization problem is related to the eigenvalues of the mechanical stress tensor. Additionally, we discuss the constrained cases of tension and compression and cover the plane stress case. We show numerical results for all cases and discuss the limits of such a material.

The neuronal innervation pattern of the subgenual organ complex in Peruphasma schultei (Insecta: Phasmatodea)

The proximal tibia of orthopteroid insects contains sensory organs, the subgenual organ complex, detecting mechanical stimuli including substrate vibration. In stick insects, two chordotonal organs occur in close proximity, the subgenual organ and the distal organ, which likely detect substrate vibrations. In most stick insects, both organs are innervated by separate nerve branches. To obtain more data on the neuroanatomy of the subgenual organ complex from the New World phasmids (Occidophasmata), the present study documents the neuronal innervation of sensory organs in the subgenual organ complex of Peruphasma schultei, the first species from Pseudophasmatinae investigated for this sensory complex. The innervation pattern shows a distinct nerve branch for the subgenual organ and for the distal organ in most cases. Some variability in the innervation, which generally occurs for these chordotonal organs, was noted for both organs in P. schultei. The most common innervation for both organs was by a single nerve branch for each organ. The innervation of the subgenual organ resembled the nerve pattern of another New World phasmid, but was simpler than in the Old World phasmids (Oriophasmata) studied so far. Therefore, the peripheral neuronal innervation of sensory organs could reflect phylogenetic relationships and provide phylogenetic information, while the overall neuroanatomy of the subgenual organ complex is similar in stick insects.

AWL-GCN: Branch Parameter Identification Considering Grid Spatial Structure Constraints

Parameter identification plays an important role in power system. The existing parameter identification methods usually have two limitations: (1) The existing methods only consider the information of single branch and ignore the influence of adjacent branch information, and do not effectively use the topological structure constraints of the power grid. (2) The measurement data has the problems of poor numerical stability, numerical divergence and noise interference. Data contamination has become an important factor restricting the prediction accuracy of branch parameters. In order to solve these problems, this work proposes an Automatic Weighted Loss-Graph Convolution Networks model combined with the spatial structure of power grid. Based on the graphical modeling, the correlation between power grid branches is effectively used, and the adjacent branches are used to provide more effective information for the parameter identification of a branch, which improves the identification accuracy. In addition, the model enhances the local characteristics through the power grid structure information constraints, realizes the local optimal fitting, and constructs a self tradeoff loss function to weaken the impact of pollution data (data noise and data loss) on the model. The test results show that compared with the traditional method, this method has higher accuracy and stronger robustness.

Control Strategy for Array Buoy Type Wave Energy Converters' Power Conversion

In order to solve the problem of complex inner‐loop control structure, difficult debugging of control parameters and poor anti‐interference ability in the double PI power conversion control method of array buoy type wave energy converter, this article proposes a power conversion control strategy in which the one step delay compensation model predictive current control strategy is proposed for the machine side rectifier and the three closed‐loop control structure with PI voltage outer‐loop, quasi‐proportional resonant (QPR) current inner‐loop and proportional capacitor current inner‐loop is proposed for the grid‐side LCL inverter. In addition, this article proposes a graded power generation scheme for a single oscillating buoy and two power generation branches, which aims to address the issue of a restricted safe working pressure range for the accumulator in the single buoy and single power generation branch mode. Based on the mathematical model of the system's key components, the MATLAB/Simulink program is applied for modeling and simulation. The results indicate that the recommended graded power generation scheme and power conversion control mechanism may accomplish stable operation of the system under three diverse wave conditions, showing the scheme's practicality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Complete genome sequence analysis of a novel citlodavirus isolated from the leaves of Myrica rubra in Yunnan.

Through high-throughput sequencing, a novel citlodavirus, tentatively named "Myrica rubra citlodavirus 1" (MRV1,accession no. OP374189), was isolated from the leaves of Myrica rubra in Yunnan exhibiting narrow deformity of leaf tips, shrinkage, and chlorosis along the veins. The complete genome sequence was determined and analyzed using cloning and Sanger sequencing. MRV1 is a single-stranded circular non-enveloped DNA virus with a genome size of 3775 nucleotides and contains six open reading frames (ORFs). The virion-sense genome strand encodes a coat protein (CP, nt 750-1,493, 247 aa), two hypothetical movement proteins (V3, nt 382-666, 94 aa; and V2, nt 461-895, 144 aa), and one movement protein (MP, nt 1,527-2,438, 303 aa). The complementary strand of the genome encodes two replication proteins (RepA, nt 3,712-2,834, 292 aa; Rep, nt 2,867-2,553, 104 aa). The MRV1 genome contains the stem-loop motif 5'-TAATATTAC-3', which is a highly conserved nonanucleotide motif found in the origin of virion-strand replication in geminiviruses. Genome sequence alignment analysis showed that citrus chlorotic dwarf associated virus (CCDaV, accession no. JQ920490) shared the highest nucleotide sequence similarity (66.10% identity) with MRV1. Phylogenetic analysis showed that CCDaV is the closest known relative of MRV1, and that these viruses clustered in a single branch within a clade consisting of citlodaviruses. These results indicate that MRV1 should be regarded as a new species of the genus Citlodavirus in the family Geminiviridae.

Surface-wave dispersion curves extraction method from ambient noise based on U-net++ and density clustering algorithm

It is crucial to accurately and rapidly extract dispersion curves of surface waves from ambient noise recordings for inverting the subsurface shear (S) wave velocity structures. Conventional manual picking methods are labor-intensive and affected by subjective factors related to seismologists. The existing deep learning methods can automatically and efficiently extract dispersion curves from dispersion spectrograms with a single branch. However, extracting dispersion curves from phase-velocity dispersion spectrograms with multiple branches remains challenging due to complex label-making processes and extensive label-preparation times. Notably, phase velocities typically display a positive correlation with periods and are slightly higher than group velocities. Given the significance of this empirical relationship, we present an intelligent surface-wave dispersion curves extraction method based on U-net++ and density clustering algorithm. Initially, guided by domain knowledge that dispersion curves are smooth, a global searching method is employed to automatically label group-velocity dispersion curves from group-velocity dispersion spectrograms. Subsequently, the group-velocity dispersion curves undergo transformation into probability images of the curves using a Gaussian function. The U-net++ then nonlinearly converts group-velocity dispersion spectrograms into probability images of group-velocity dispersion curves within a high-dimensional space. Following this, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied to obtain multi-mode phase-velocity dispersion curves from phase-velocity dispersion spectrograms. We then remove invalid dispersion values in the multi-mode phase-velocity dispersion curves based on the empirical relationship between phase and group velocities, ultimately obtaining the phase-velocity dispersion curve corresponding to the group-velocity dispersion curve. Our proposed method has been tested using synthetic data and 3D real-world data collected near Lake Chao in Anhui Province. The test results show that our approach effectively extracts both group-velocity and phase-velocity dispersion curves. Inverted S-wave velocity structures based on extracted dispersion curves in real-world data can characterize the Tan-Lu fault zone, demonstrating the effectiveness of our proposed method.