A new loach species of the genus Oreonectes (Teleostei, Cypriniformes, Nemacheilidae) from Guangxi, China.

A new loach species, Oreonectesandongensissp. nov. is described from the Guangxi Zhuang Autonomous Region, China. The new species can be differentiated from other members of the genus by combinations of characters: a developed posterior chamber of the swim bladder, 13-14 branched caudal-fin rays, 8-16 lateral-line pores, body width 12-15% of standard length (SL), interorbital width 42-47% of head length (HL), and caudal peduncle length 11-16% of SL. Bayesian inference phylogenetic analysis based on mitochondrial Cyt b provided strong support for validity of O.andongensissp. nov. (uncorrected p-distance 6.0-7.5%).

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

Anti-Aging Drugs and the Related Signal Pathways.

Aging is a multifactorial biological process involving chronic diseases that manifest from the molecular level to the systemic level. From its inception to 31 May 2022, this study searched the PubMed, Web of Science, EBSCO, and Cochrane library databases to identify relevant research from 15,983 articles. Multiple approaches have been employed to combat aging, such as dietary restriction (DR), exercise, exchanging circulating factors, gene therapy, and anti-aging drugs. Among them, anti-aging drugs are advantageous in their ease of adherence and wide prevalence. Despite a shared functional output of aging alleviation, the current anti-aging drugs target different signal pathways that frequently cross-talk with each other. At present, six important signal pathways were identified as being critical in the aging process, including pathways for the mechanistic target of rapamycin (mTOR), AMP-activated protein kinase (AMPK), nutrient signal pathway, silent information regulator factor 2-related enzyme 1 (SIRT1), regulation of telomere length and glycogen synthase kinase-3 (GSK-3), and energy metabolism. These signal pathways could be targeted by many anti-aging drugs, with the corresponding representatives of rapamycin, metformin, acarbose, nicotinamide adenine dinucleotide (NAD+), lithium, and nonsteroidal anti-inflammatory drugs (NSAIDs), respectively. This review summarized these important aging-related signal pathways and their representative targeting drugs in attempts to obtain insights into and promote the development of mechanism-based anti-aging strategies.

Regularization parameter based on incomplete variables for X-ray luminescence computed tomography.

X-ray luminescence computed tomography (XLCT) is an emerging molecular imaging technique for biological application. However, it is still a challenge to get a stable and accurate solution of the reconstruction of XLCT. This paper presents a regularization parameter selection strategy based on incomplete variables frame for XLCT. A residual information, which is derived from Karush-Kuhn-Tucker (KKT) equivalent condition, is employed to determine the regularization parameter. This residual contains the relevant information about the solution norm and gradient norm, which improved the recovered results. Simulation and phantom experiments are designed to test the performance of the algorithm.Clinical Relevance- The results have not yet been used in clinical relevance currently, we believed that this strategy will facilitate the development of the preclinical applications in FMT.

Adaptive Sparsity Orthogonal Least Square with Neighbor Strategy for Fluorescence Molecular Tomography.

Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive optical imaging technique which has been widely applied to disease diagnosis and drug discovery. However, FMT reconstruction is a highly ill-posed problem. In this work, L0-norm regularization is employed to construct the mathematical model of the inverse problem of FMT. And an adaptive sparsity orthogonal least square with a neighbor strategy (ASOLS-NS) is proposed to solve this model. This algorithm can provide an adaptive sparsity and can establish the candidate sets by a novel neighbor expansion strategy for the orthogonal least square (OLS) algorithm. Numerical simulation experiments have shown that the ASOLS-NS improves the reconstruction of images, especially for the double targets reconstruction.Clinical relevance- The purpose of this work is to improve the reconstruction results of FMT. Current experiments are focused on simulation experiments, and the proposed algorithm will be applied to the clinical tumor detection in the future.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography.

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Construction of Zr-Metal-Organic Frameworks-Based Composite Materials toward Anhydrous Proton Conduction and Photocatalytic CO2 Reduction.

Metal-organic frameworks constructed from Zr usually possess excellent chemical and physical stability. Therefore, they have become attractive platforms in various fields. In this work, two families of hybrid materials based on ZrSQU have been designed and synthesized, named Im@ZrSQU and Cu@ZrSQU, respectively. Im@ZrSQU was prepared through the impregnation method and employed for proton conduction. Im@ZrSQU exhibited terrific proton conduction performance in an anhydrous environment, with the highest proton conduction value of 3.6 × 10-2 S cm-1 at 110 °C. In addition, Cu@ZrSQU was synthesized via the photoinduction method for the photoreduction of CO2, which successfully promoted the conversion of CO2 into CO and achieved the CO generation rate of up to 12.4 µmol g-1 h-1. The photocatalytic performance of Cu@ZrSQU is derived from the synergistic effect of Cu NPs and ZrSQU. Based on an in-depth study and discussion toward ZrSQU, we provide a versatile platform with applications in the field of proton conduction and photocatalysis, which will guide researchers in their further studies.

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction.

In this article, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset releases 16,940 textured 3D faces, captured from 847 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniform. These fine 3D facial models can be represented as a 3D morphable model for coarse shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different from most previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography.

Significance: Fluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements. Aim: We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction. Approach: The proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments. Results: The results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction. Conclusion: NASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.

Study on the Preparation of Montmorillonite-Type Multiple Network Composite Gel for Coal Spontaneous Combustion and Its Firefighting Mechanism.

This paper proposes a new montmorillonite-type multiple network composite gel for the prevention of coal spontaneous combustion. The first network is formed by the cross-linking of polyvinyl alcohol (PVA) and boric acid under alkaline conditions. The second network is formed as a result of intermolecular hydrogen-bonding interactions between polyacrylamide (PAM) and polyvinyl alcohol. Montmorillonite (MMT) is designed as the backbone material in the preparation of composite gels. The optimal ratios of the reactants of the composite gel were determined through orthogonal experiments. The experimental results showed that PVA had the greatest influence on the gelation time, whereas the PAM concentration had the strongest influence on the gel permeability. The optimal blending ratio was 4% MMT + 2.5% PVA + 1.5% PAM. The chemical performances of the composite colloids, such as inhibition rate, reactive functional groups, and kinetics, were investigated. Results showed that multiple network composite gels could effectively inhibit the coal spontaneous combustion reaction. Based on the principle of coal spontaneous combustion and the cross-linking network structure of the composite gel, the flame-retardant and fire-extinguishing mechanisms were also explored in terms of both physical and chemical inhibition pathways.

A porous Ti-based metal-organic framework for CO2 photoreduction and imidazole-dependent anhydrous proton conduction.

The anhydrous proton conductivity of Im@IEF-11 resulting from the integration of imidazole and porous IEF-11 has been investigated, and the highest proton conductive value can reach up to 7.64 × 10-2 S cm-1. Furthermore, IEF-11 is also developed to reduce CO2 due to its reasonable structure and suitable energy band, and its CO formation rate is 31.86 µmol g-1 h-1.

Identification and Functional Analysis of Acyl-Acyl Carrier Protein Δ9 Desaturase from Nannochloropsis oceanica.

The oleaginous marine microalga Nannochloropsis oceanica strain IMET1 has attracted increasing attention as a promising photosynthetic cell factory due to its unique excellent capacity to accumulate large amounts of triacylglycerols and eicosapentaenoic acid. To complete the genomic annotation for genes in the fatty acid biosynthesis pathway of N. oceanica, we conducted the present study to identify a novel candidate gene encoding the archetypical chloroplast stromal acyl-acyl carrier protein Δ9 desaturase. The full-length cDNA was generated using rapid-amplification of cDNA ends, and the structure of the coding region interrupted by four introns was determined. The RT-qPCR results demonstrated the upregulated transcriptional abundance of this gene under nitrogen starvation condition. Fluorescence localization studies using EGFP-fused protein revealed that the translated protein was localized in chloroplast stroma. The catalytic activity of the translated protein was characterized by inducible expression in Escherichia coli and a mutant yeast strain BY4389, indicating its potential desaturated capacity for palmitoyl-ACP (C16:0-ACP) and stearoyl-ACP (C18:0-ACP). Further functional complementation assay using BY4839 on plate demonstrated that the expressed enzyme restored the biosynthesis of oleic acid. These results support the desaturated activity of the expressed protein in chloroplast stroma to fulfill the biosynthesis and accumulation of monounsaturated fatty acids in N. oceanica strain IMET1.

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection From Point Clouds.

Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., point cloud videos. We empirically categorize the temporal information into short-term and long-term patterns. To encode the short-term data, we present a Grid Message Passing Network (GMPNet), which considers each grid (i.e., the grouped points) as a node and constructs a k-NN graph with the neighbor grids. To update features for a grid, GMPNet iteratively collects information from its neighbors, thus mining the motion cues in grids from nearby frames. To further aggregate long-term frames, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU), which contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module. STA and TTA enhance the vanilla GRU to focus on small objects and better align moving objects. Our overall framework supports both online and offline video object detection in point clouds. We implement our algorithm based on prevalent anchor-based and anchor-free detectors. Evaluation results on the challenging nuScenes benchmark show superior performance of our method, achieving first on the leaderboard (at the time of paper submission) without any "bells and whistles." Our source code is available at https://github.com/shenjianbing/GMP3D.

Ag Nanoparticle-Modified Polyoxometalate-Based Metal-Organic Framework for Enhanced CO2 Photoreduction.

The photoreduction deposition method is employed to fabricate a family of silver nanoparticle (Ag NP)-modified polyoxometalate-based metal-organic framework (NENU-5) photocatalysts, named Ag/NENU-5. The title photocatalysts, Ag/NENU-5, can be used for the photocatalytic reduction of CO2 and are observed to efficiently reduce CO2 into CO, in which the highest reduction rate is 22.28 µmol g-1 h-1, 3 times greater than that of NENU-5. Photocatalytic reduction performances of CO2 have been extremely improved after the incorporation of Ag NPs as the cocatalyst. The enhancement of the photocatalytic reduction of CO2 has been attributed to the synergistic effects of Ag NPs and NENU-5, inhibiting the charge recombination during the photocatalytic process and increasing the reaction active sites. Furthermore, the influence of Ag NPs on the photocatalytic activity has also been investigated. The experimental results clearly reveal that the size of Ag NPs could exert a main effect on the photocatalytic activity, and the reasonable size of Ag NPs is able to enhance the photocatalytic reduction activity toward CO2 significantly.

An Intelligent Self-Driving Truck System for Highway Transportation.

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

A multifunctional anionic metal-organic framework for high proton conductivity and photoreduction of CO2 induced by cation exchange.

Metal-organic frameworks (MOFs) provide an ideal platform for loading various guests owing to their available space, and can be developed as a class of multifunctional materials. Herein, we cover the design and synthesis of two kinds of exchanged frameworks with multifunctional applications based on H3ImDC and In(NO3)3·2H2O through guest exchange inside the framework. The guest ammonium ion (NH4+) and [Ru(2,2'-bipyridine)3]2+ (Rubpy) are selected to exchange the dimethylammonium cation (Me2NH2+) encapsulated within In-MOF, giving birth to two kinds of new MOFs, named NH4+@In-MOF and Rubpy@In-MOF respectively. The proton conduction of NH4+@In-MOF and the CO2 photoreduction of Rubpy@In-MOF are investigated. Under different test conditions, the proton conductive behaviors of NH4+@In-MOF are evaluated and the best proton conductive value can reach up to 9.81 × 10-3 S cm-1. Compared to the original In-MOF, Rubpy@In-MOF exhibits a significantly enhanced CO2 photoreduction performance under a pure CO2 atmosphere. Furthermore, its photocatalytic activity is retained even under a 10% CO2 gas atmosphere, displaying a synergistic effect between Rubpy and In-MOF24 within Rubpy@In-MOF.

Salient Object Detection in the Deep Learning Era: An In-Depth Survey.

As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions. All the saliency prediction maps, our constructed dataset with annotations, and codes for evaluation are publicly available at https://github.com/wenguanwang/SODsurvey.

Part-Level Car Parsing and Reconstruction in Single Street View Images.

Part information has been proven to be resistant to occlusions and viewpoint changes, which are main difficulties in car parsing and reconstruction. However, in the absence of datasets and approaches incorporating car parts, there are limited works that benefit from it. In this paper, we propose the first part-aware approach for joint part-level car parsing and reconstruction in single street view images. Without labor-intensive part annotations on real images, our approach simultaneously estimates pose, shape, and semantic parts of cars. There are two contributions in this paper. First, our network introduces dense part information to facilitate pose and shape estimation, which is further optimized with a novel 3D loss. To obtain part information in real images, a class-consistent method is introduced to implicitly transfer part knowledge from synthesized images. Second, we construct the first high-quality dataset containing 348 car models with physical dimensions and part annotations. Given these models, 60K synthesized images with randomized configurations are generated. Experimental results demonstrate that part knowledge can be effectively transferred with our class-consistent method, which significantly improves part segmentation performance on real street views. By fusing dense part information, our pose and shape estimation results achieve the state-of-the-art performance on the ApolloCar3D and outperform previous approaches by large margins in terms of both A3DP-Abs and A3DP-Rel.

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception.

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. The proposed model acts as a backbone and the learned features from this model can be used for downstream tasks such as point cloud semantic and panoptic segmentation or 3D detection. In this paper, we benchmark our model on these three tasks. For semantic segmentation, we evaluate the proposed model on several large-scale datasets, i.e., SemanticKITTI, nuScenes and A2D2. Our method achieves the state-of-the-art on the leaderboard of SemanticKITTI (both single-scan and multi-scan challenge), and significantly outperforms existing methods on nuScenes and A2D2 dataset. Furthermore, the proposed 3D framework also shows strong performance and good generalization on LiDAR panoptic segmentation and LiDAR 3D detection.

Detailed Avatar Recovery From Single Image.

This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric-based template that lacks the surface details. As such resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of the parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. Our method can restore detailed human body shapes with complete textures beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance.