Tamlana laminarinivorans sp. nov. and Tamlana sargassicola sp. nov., two novel species isolated from Sargassum, show genomic and physiological adaptations for a Sargassum-associated lifestyle.

The genus Tamlana from the Bacteroidota currently includes six validated species. Two strains designated PT2-4T and 62-3T were isolated from Sargassum abundant at the Pingtan island coast in the Fujian Province of China. 16S rRNA gene sequence analysis showed that the closest described relative of strains PT2-4T and 62-3T is Tamlana sedimentorum JCM 19808T with 98.40 and 97.98% sequence similarity, respectively. The 16S rRNA gene sequence similarity between strain PT2-4T and strain 62-3T was 98.68 %. Furthermore, the highest average nucleotide identity values were 87.34 and 88.97 % for strains PT2-4T and 62-3T, respectively. The highest DNA-DNA hybridization (DDH) value of strain PT2-4T was 35.2 % with strain 62-3T, while the DDH value of strain 62-3T was 37.7 % with T. sedimentorum JCM 19808T. Growth of strains PT2-4T and 62-3T occurs at 15-40 °C (optimum, 30 °C) with 0-4 % (w/v) NaCl (optimum 0-1 %). Strains PT2-4T and 62-3T can grow from pH 5.0 to 10.0 (optimum, pH 7.0). The major fatty acids of strains PT2-4T and 62-3T are iso-C15 : 0 and iso G-C15 : 1. MK-6 is the sole respiratory quinone. Genomic and physiological analyses of strains PT2-4T and 62-3T showed corresponding adaptive features. Significant adaptation to the growth environment of macroalgae includes the degradation of brown algae-derived diverse polysaccharides (alginate, laminarin and fucoidan). Notably, strain PT2-4T can utilize laminarin, fucoidan and alginate via specific carbohydrate-active enzymes encoded in polysaccharide utilization loci, rarely described for the genus Tamlana to date. Based on their distinct physiological characteristics and the traits of utilizing polysaccharides from Sargassum, strains PT2-4T and 62-3T are suggested to be classified into two novel species, Tamlana laminarinivorans sp. nov. and Tamlana sargassicola sp. nov. (type strain PT2-4T=MCCC 1K04427T=KCTC 92183T and type strain 62-3T=MCCC 1K04421T=KCTC 92182T).

Ecological distribution and function of comammox Nitrospira in the environment.

Complete ammonia oxidizers (Comammox) are of great significance for studying nitrification and expanding the understanding of the nitrogen cycle. Moreover, Comammox bacteria are also crucial in natural and engineered environments due to their role in wastewater treatment and maintaining the flux of greenhouse gases to the atmosphere. However, only few studies are there regarding the Comammox bacteria and their role in ammonia and nitrite oxidation in the environment. This review mainly focuses on summarizing the genomes of Nitrospira in the NCBI database. Ecological distribution of Nitrospira was also reviewed and the influence of environmental parameters on genus Nitrospira in different environments has been summarized. Furthermore, the role of Nitrospira in carbon cycle, nitrogen cycle, and sulfur cycle were discussed, especially the comammox Nitrospira. In addition, the overviews of current research and development regarding comammox Nitrospira, were summarized along with the scope of future research. KEY POINTS: • Most of Comammox Nitrospira are widely distributed in both aquatic and terrestrial ecosystems, but it has been studied less frequently in the extreme environments. • Comammox Nitrospira can be involved in different nitrogen transformation process, but rarely involved in nitrogen fixation. • The stable isotope and transcriptome techniques are important methods to study the metabolic function of comammox Nitrospira.

A new model of preoperative systemic inflammatory markers predicting overall survival of osteosarcoma: a multicenter retrospective study.

BACKGROUND: The purpose of this study was to investigate the significance of preoperative C-reactive protein-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting overall survival (OS) of osteosarcoma, to establish a nomogram of an individualized prognostic prediction model for osteosarcoma. METHODS: Two hundred thirty-five patients with osteosarcoma from multiple centers were included in this study. Receiver operating characteristic (ROC) and Youden index were used to determine the optimal cutoff values ​​for CAR, NLR, and PLR. Univariate analysis using COX proportional hazards model to identify factors associated with OS in osteosarcoma, and multivariate analysis of these factors to identify independent prognostic factors. R software (4.1.3-win) rms package was used to build a nomogram, and the concordance index (C-index) and calibration curve were used to assess model accuracy and discriminability. RESULTS: Univariate analysis revealed that the OS of osteosarcoma is significantly correlated (P < 0.05) with CAR, NLR, PLR, Enneking stage, tumor size, age, neoadjuvant chemotherapy (NACT), and high alkaline phosphatase. Multivariate analysis confirmed that CAR, NLR, Enneking stage, NACT and tumor size are independent prognostic factors for OS of osteosarcoma. The calibration curve shows that the nomogram constructed from these factors has acceptable consistency and calibration capability. CONCLUSION: Preoperative CAR and NLR were independent predictors of osteosarcoma prognosis, and the combination of nomogram model can realize individualized prognosis prediction and guide medical practice.

The role of edge-based and surface-based information in natural scene categorization: Evidence from behavior and event-related potentials.

A fundamental question in vision research is whether visual recognition is determined by edge-based information (e.g., edge, line, and conjunction) or surface-based information (e.g., color, brightness, and texture). To investigate this question, we manipulated the stimulus onset asynchrony (SOA) between the scene and the mask in a backward masking task of natural scene categorization. The behavioral results showed that correct classification was higher for line-drawings than for color photographs when the SOA was 13ms, but lower when the SOA was longer. The ERP results revealed that most latencies of early components were shorter for the line-drawings than for the color photographs, and the latencies gradually increased with the SOA for the color photographs but not for the line-drawings. The results provide new evidence that edge-based information is the primary determinant of natural scene categorization, receiving priority processing; by contrast, surface information takes longer to facilitate natural scene categorization.

MOB rules: Antibiotic Exposure Reprograms Metabolism to Mobilize Bacillus subtilis in Competitive Interactions.

Antibiotics have dose-dependent effects on exposed bacteria. The medicinal use of antibiotics relies on their growth-inhibitory activities at sufficient concentrations. At subinhibitory concentrations, exposure effects vary widely among different antibiotics and bacteria. Bacillus subtilis responds to bacteriostatic translation inhibitors by mobilizing a population of cells (MOB-Mobilized Bacillus) to spread across a surface. How B. subtilis regulates the antibiotic-induced mobilization is not known. In this study, we used chloramphenicol to identify regulatory functions that B. subtilis requires to coordinate cell mobilization following subinhibitory exposure. We measured changes in gene expression and metabolism and mapped the results to a network of regulatory proteins that direct the mobile response. Our data reveal that several transcriptional regulators coordinately control the reprogramming of metabolism to support mobilization. The network regulates changes in glycolysis, nucleotide metabolism, and amino acid metabolism that are signature features of the mobilized population. Among the hundreds of genes with changing expression, we identified two, pdhA and pucA, where the magnitudes of their changes in expression, and in the abundance of associated metabolites, reveal hallmark metabolic features of the mobilized population. Using reporters of pdhA and pucA expression, we visualized the separation of major branches of metabolism in different regions of the mobilized population. Our results reveal a regulated response to chloramphenicol exposure that enables a population of bacteria in different metabolic states to mount a coordinated mobile response.

SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness.

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.

Novel insights into the synergetic degradation of pyrene by microbial communities from mangroves in China.

Pyrene is a high molecular weight polycyclic aromatic hydrocarbon (HMW-PAHs). It is a ubiquitous, persistent, and carcinogenic environmental contaminant that has raised concern worldwide. This research explored synergistic bacterial communities for efficient pyrene degradation in seven typical Southern China mangroves. The bacterial communities of seven typical mangroves were enriched by pyrene, and enriched bacterial communities showed an excellent pyrene degradation capacity of > 95% (except for HK mangrove and ZJ mangrove). Devosia, Hyphomicrobium, Flavobacterium, Marinobacter, Algoriphahus, and Youhaiella all have significant positive correlations with pyrene (R>0, p < 0.05) by 16SrRNA gene sequencing and metagenomics analysis, indicated that these genera play a vital role in pyrene metabolism. Meanwhile, the functional genes were involved in pyrene degradation that was enriched in the bacterial communities, including the genes of nagAa, ndoR, pcaG, etc. Furthermore, the analyses of functional genes and binning genomes demonstrated that some bacterial communities as a unique teamwork to cooperatively participate in pyrene degradation. Interestingly, the genes related to biogeochemical cycles were enriched, such as narG , soxA, and cyxJ, suggested that bacterial communities were also helpful in maintaining the stability of the ecological environment. In addition, some novel species with pyrene-degradation potential were identified in the pyrene-degrading bacterial communities, which can enrich the resource pool of pyrene-degrading strains. Overall, this study will help develop further research strategies for pollutant removal.

PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation.

Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.

Biotransformation of HBCDs by the microbial communities enriched from mangrove sediments.

1,2,5,6,9,10-Hexabromocyclododecanes (HBCDs) are a sort of persistent organic pollutants (POPs). This research investigated 12 microbial communities enriched from sediments of four mangroves in China to transform HBCDs. Six microbial communities gained high transformation rates (27.5-97.7%) after 12 generations of serial transfer. Bacteria were the main contributors to transform HBCDs rather than fungi. Analyses on the bacterial compositions and binning genomes showed that Alcanivorax (55.246-84.942%) harboring haloalkane dehalogenase genes dadAH and dadBH dominated the microbial communities with high transformation rates. Moreover, expressions of dadAH and dadBH in the microbial communities and Alcanivorax isolate could be induced by HBCDs. Further, it was found that purified proteins DadAH and DadBH showed high conversion rates on HBCDs in 36 h (91.9 ± 7.4 and 101.0 ± 1.8%, respectively). The engineered Escherichia coli BL21 strains harbored two genes could convert 5.7 ± 0.4 and 35.1 ± 0.1% HBCDs, respectively, lower than their cell-free crude extracts (61.2 ± 5.2 and 56.5 ± 8.7%, respectively). The diastereoisomer-specific transforming trend by both microbial communities and enzymes were γ- > α- > ß-HBCD, differed from α- > ß- > Î³-HBCD by the Alcanivorax isolate. The identified transformation products indicated that HBCDs were dehalogenated via HBr elimination (dehydrobromination), hydrolytic and reductive debromination pathways in the enriched cultures. Two enzymes converted HBCDs via hydrolytic debromination. The present research provided theoretical bases for the biotransformation of HBCDs by microbial community and the bioremediation of HBCDs contamination in the environment.

Quality Metric Guided Portrait Line Drawing Generation From Unpaired Training Data.

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods.

Complete 3D Relationships Extraction Modality Alignment Network for 3D Dense Captioning.

3D dense captioning aims to semantically describe each object detected in a 3D scene, which plays a significant role in 3D scene understanding. Previous works lack a complete definition of 3D spatial relationships and the directly integrate visual and language modalities, thus ignoring the discrepancies between the two modalities. To address these issues, we propose a novel complete 3D relationship extraction modality alignment network, which consists of three steps: 3D object detection, complete 3D relationships extraction, and modality alignment caption. To comprehensively capture the 3D spatial relationship features, we define a complete set of 3D spatial relationships, including the local spatial relationship between objects and the global spatial relationship between each object and the entire scene. To this end, we propose a complete 3D relationships extraction module based on message passing and self-attention to mine multi-scale spatial relationship features and inspect the transformation to obtain features in different views. In addition, we propose the modality alignment caption module to fuse multi-scale relationship features and generate descriptions to bridge the semantic gap from the visual space to the language space with the prior information in the word embedding, and help generate improved descriptions for the 3D scene. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art methods on the ScanRefer and Nr3D datasets.

STD-Net: Structure-Preserving and Topology-Adaptive Deformation Network for Single-View 3D Reconstruction.

3D reconstruction from single-view images is a long-standing research problem. There have been various methods based on point clouds and volumetric representations. In spite of success in 3D models generation, it is quite challenging for these approaches to deal with models with complex topology and fine geometric details. Thanks to the recent advance of deep shape representations, learning the structure and detail representation using deep neural networks is a promising direction. In this article, we propose a novel approach named STD-Net to reconstruct 3D models utilizing mesh representation that is well suited for characterizing complex structures and geometry details. Our method consists of (1) an auto-encoder network for recovering the structure of an object with bounding box representation from a single-view image; (2) a topology-adaptive GCN for updating vertex position for meshes of complex topology; and (3) a unified mesh deformation block that deforms the structural boxes into structure-aware meshes. Evaluation on ShapeNet and PartNet shows that STD-Net has better performance than state-of-the-art methods in reconstructing complex structures and fine geometric details.

Yarn-Level Simulation of Hygroscopicity of Woven Textiles.

Simulating liquid-textile interaction has received great attention in computer graphics recently. Most existing methods take textiles as particles or parameterized meshes. Although these methods can generate visually pleasing results, they cannot simulate water content at a microscopic level due to the lack of geometrically modeling of textile's anisotropic structure. In this paper, we develop a method for yarn-level simulation of hygroscopicity of textiles and evaluate it using various quantitative metrics. We model textiles in a fiber-yarn-fabric multi-scale manner and consider the dynamic coupled physical mechanisms of liquid spreading, including wetting, wicking, moisture sorption/desorption, and transient moisture-heat transfer in textiles. Our method can accurately simulate liquid spreading on textiles with different fiber materials and geometrical structures with consideration of air temperatures and humidity conditions. It visualizes the hygroscopicity of textiles to demonstrate their moisture management ability. We conduct qualitative and quantitative experiments to validate our method and explore various factors to analyze their influence on liquid spreading and hygroscopicity of textiles.

Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition.

Recent works have achieved remarkable performance for action recognition with human skeletal data by utilizing graph convolutional models. Existing models mainly focus on developing graph convolutional operations to encode structural properties of a skeletal graph, whose topology is manually predefined and fixed over all action samples. Some recent works further take sample-dependent relationships among joints into consideration. However, the complex relationships between arbitrary pairwise joints are difficult to learn and the temporal features between frames are not fully exploited by simply using traditional convolutions with small local kernels. In this paper, we propose a motif-based graph convolution method, which makes use of sample-dependent latent relations among non-physically connected joints to impose a high-order locality and assigns different semantic roles to physical neighbors of a joint to encode hierarchical structures. Furthermore, we propose a sparsity-promoting loss function to learn a sparse motif adjacency matrix for latent dependencies in non-physical connections. For extracting effective temporal information, we propose an efficient local temporal block. It adopts partial dense connections to reuse temporal features in local time windows, and enrich a variety of information flow by gradient combination. In addition, we introduce a non-local temporal block to capture global dependencies among frames. Our model can capture local and non-local relationships both spatially and temporally, by integrating the local and non-local temporal blocks into the sparse motif-based graph convolutional networks (SMotif-GCNs). Comprehensive experiments on four large-scale datasets show that our model outperforms the state-of-the-art methods. Our code is publicly available at https://github.com/wenyh1616/SAMotif-GCN.

Exploring user experience and performance of a tedious task through human-agent relationship.

Positive human-agent relationships can effectively improve human experience and performance in human-machine systems or environments. The characteristics of agents that enhance this relationship have garnered attention in human-agent or human-robot interactions. In this study, based on the rule of the persona effect, we study the effect of an agent's social cues on human-agent relationships and human performance. We constructed a tedious task in an immersive virtual environment, designing virtual partners with varying levels of human likeness and responsiveness. Human likeness encompassed appearance, sound, and behavior, while responsiveness referred to the way agents responded to humans. Based on the constructed environment, we present two studies to explore the effects of an agent's human likeness and responsiveness to agents on participants' performance and perception of human-agent relationships during the task. The results indicate that when participants work with an agent, its responsiveness attracts attention and induces positive feelings. Agents with responsiveness and appropriate social response strategies have a significant positive effect on human-agent relationships. These results shed some light on how to design virtual agents to improve user experience and performance in human-agent interactions.

A New Species of Pareas (Squamata, Pareidae) from Guangxi Province, China.

We described a new species of genus Pareas from Baise City, Guangxi Zhuang Autonomous Region, China, based on morphological and molecular evidence. Pareas baiseensis sp. nov. is distinguished from its congeners by the combination of (1) Yellowish-brown body colouration; (2) Frontal subhexagonal to diamond-shaped with its lateral sides converging posteriorly; (3) The anterior pair of chin shields is longer than it is broad; (4) Loreal not in contact with the eye, prefrontal in contact with the eye, two or three suboculars; (5) Rows of 15-15-15 dorsal scales, five rows of mid-dorsal scales keeled at the middle of the body, one vertebral scale row enlarged; (6) 187-191 ventrals, 89-97 subcaudals, all divided, cloacal plate single; (7) Two postocular stripes, the nuchal area forming a dark black four-pointed fork collar with the middle tines shorter than the outside tines. The genetic divergence (uncorrected p-distance) between the new species and other representatives of Pareas ranged from 13.9% to 24.4% for Cytochrome b (Cyt b) and 12.1% to 25.5% for NADH dehydrogenase subunit 4 (ND4). Phylogenetic analyses of mitochondrial DNA gene data recovered the new species from being the sister taxon to (P. boulengeri + P. chinensis) from China.

PU-Flow: A Point Cloud Upsampling Network With Normalizing Flows.

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/puflow.

MSL-Net: Sharp Feature Detection Network for 3D Point Clouds.

As a significant geometric feature of 3D point clouds, sharp features play an important role in shape analysis, 3D reconstruction, registration, localization, etc. Current sharp feature detection methods are still sensitive to the quality of the input point cloud, and the detection performance is affected by random noisy points and non-uniform densities. In this paper, using the prior knowledge of geometric features, we propose a Multi-scale Laplace Network (MSL-Net), a new deep-learning-based method based on an intrinsic neighbor shape descriptor, to detect sharp features from 3D point clouds. Firstly, we establish a discrete intrinsic neighborhood of the point cloud based on the Laplacian graph, which reduces the error of local implicit surface estimation. Then, we design a new intrinsic shape descriptor based on the intrinsic neighborhood, combined with enhanced normal extraction and cosine-based field estimation function. Finally, we present the backbone of MSL-Net based on the intrinsic shape descriptor. Benefiting from the intrinsic neighborhood and shape descriptor, our MSL-Net has simple architecture and is capable of establishing accurate feature prediction that satisfies the manifold distribution while avoiding complex intrinsic metric calculations. Extensive experimental results demonstrate that with the multi-scale structure, MSL-Net has a strong analytical ability for local perturbations of point clouds. Compared with state-of-the-art methods, our MSL-Net is more robust and accurate. The code is publicly available at.

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation From Normal Face Photos.

Caricature is a type of artistic style of human faces that attracts considerable attention in the entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricatures directly from a normal 2D face photo. The most challenging issue for our system is that the source domain of face photos (characterized by normal 2D faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and textures). To address this challenge, we: (1) build a large dataset of 5,343 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space; (2) reconstruct a normal full 3D head from the input face photo and use its PCA representation in the 3D caricature shape space to establish correspondences between the input photo and 3D caricature shape; and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.

Fine-Grained Video Retrieval With Scene Sketches.

Benefiting from the intuitiveness and naturalness of sketch interaction, sketch-based video retrieval (SBVR) has received considerable attention in the video retrieval research area. However, most existing SBVR research still lacks the capability of accurate video retrieval with fine-grained scene content. To address this problem, in this paper we investigate a new task, which focuses on retrieving the target video by utilizing a fine-grained storyboard sketch depicting the scene layout and major foreground instances' visual characteristics (e.g., appearance, size, pose, etc.) of video; we call such a task "fine-grained scene-level SBVR". The most challenging issue in this task is how to perform scene-level cross-modal alignment between sketch and video. Our solution consists of two parts. First, we construct a scene-level sketch-video dataset called SketchVideo, in which sketch-video pairs are provided and each pair contains a clip-level storyboard sketch and several keyframe sketches (corresponding to video frames). Second, we propose a novel deep learning architecture called Sketch Query Graph Convolutional Network (SQ-GCN). In SQ-GCN, we first adaptively sample the video frames to improve video encoding efficiency, and then construct appearance and category graphs to jointly model visual and semantic alignment between sketch and video. Experiments show that our fine-grained scene-level SBVR framework with SQ-GCN architecture outperforms the state-of-the-art fine-grained retrieval methods. The SketchVideo dataset and SQ-GCN code are available in the project webpage https://iscas-mmsketch.github.io/FG-SL-SBVR/.