FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%

Meta-analysis of ridge-furrow cultivation effects on maize production and water use efficiency

Ridge-furrow cultivation (RF) is a popular dryland agricultural technique in China, but its effects on maize yield, total water consumption during crop growing stage (ET), and water use efficiency (WUE) have not been systematically analyzed. Here we conducted a meta-analysis of the RF effects on maize yield, ET and WUE based on the data collected from peer-reviewed literature. Yield, ET and WUE varied with climate, soil and mulching management. Averaged across all the geographic locations, RF increased the yield and WUE of maize by 47 % and 39 %, respectively, but no effects on ET. An increase in the yield and WUE occurred under RF in regions regardless of the mean growing season air temperature (MT) or a mean precipitation during the growing season (MP), although there was a trend that RF is more beneficial under low MP. RF also decreased ET in regions with MT>12 °C. RF increased the yield and WUE in regions with medium or fine soil texture. RF increased the yield, ET, and WUE in regions with low soil bulk density (BD) (≤1.3 g cm−3). But in areas where BD is larger than 1.3 g cm−3, RF only increased the yield and WUE. RF increased the yield and WUE with or without mulching, but decreased ET when no mulching was used. Together, optimizing RF effects on the yield, ET and WUE in maize was largely dependent on environmental conditions and management practices

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail

VPRNet: Virtual Points Registration Network for Partial-to-Partial Point Cloud Registration

With the development of high-precision and high-frame-rate scanning technology, we can quickly obtain scan data of various large-scale scenes. As a manifestation of information fusion, point cloud registration is of great significance in various fields, such as medical imaging, autonomous driving, and 3D reconstruction. The Iterative Closest Point (ICP) algorithm, as the most classic algorithm, leverages the closest point to search corresponding points, which is the pioneer of correspondences-based approaches. Recently, some deep learning-based algorithms witnessed extracting deep features to compress point cloud information, then calculate corresponding points, and finally output the optimal rigid transformation like Deep Closest Point (DCP) and DeepVCP. However, the partiality of point clouds hinders the acquisition of enough corresponding points when dealing with the partial-to-partial registration problem. To this end, we propose Virtual Points Registration Network (VPRNet) for this intractable problem. We first design a self-supervised virtual point generation network (VPGnet), which utilizes the attention mechanism of Transformer and Self-Attention to fuse the geometric information of two partial point clouds, combined with the Generative Adversarial Network (GAN) structure to produce missing points. Subsequently, the following registration network structure is spliced to the end of VPGnet, thus estimating rich corresponding points. Unlike the existing methods, our network tries to eliminate the side effects of incompleteness on registration. Thus, our method expresses resilience to the initial rotation and sparsity. Various experiments indicate that our proposed algorithm shows advanced performance compared to recent deep learning-based and classical methods

VPRNet: Virtual Points Registration Network for Partial-to-Partial Point Cloud Registration

With the development of high-precision and high-frame-rate scanning technology, we can quickly obtain scan data of various large-scale scenes. As a manifestation of information fusion, point cloud registration is of great significance in various fields, such as medical imaging, autonomous driving, and 3D reconstruction. The Iterative Closest Point (ICP) algorithm, as the most classic algorithm, leverages the closest point to search corresponding points, which is the pioneer of correspondences-based approaches. Recently, some deep learning-based algorithms witnessed extracting deep features to compress point cloud information, then calculate corresponding points, and finally output the optimal rigid transformation like Deep Closest Point (DCP) and DeepVCP. However, the partiality of point clouds hinders the acquisition of enough corresponding points when dealing with the partial-to-partial registration problem. To this end, we propose Virtual Points Registration Network (VPRNet) for this intractable problem. We first design a self-supervised virtual point generation network (VPGnet), which utilizes the attention mechanism of Transformer and Self-Attention to fuse the geometric information of two partial point clouds, combined with the Generative Adversarial Network (GAN) structure to produce missing points. Subsequently, the following registration network structure is spliced to the end of VPGnet, thus estimating rich corresponding points. Unlike the existing methods, our network tries to eliminate the side effects of incompleteness on registration. Thus, our method expresses resilience to the initial rotation and sparsity. Various experiments indicate that our proposed algorithm shows advanced performance compared to recent deep learning-based and classical methods

Endophilin A1 Promotes Actin Polymerization in Dendritic Spines Required for Synaptic Potentiation

Endophilin A1 is a member of the N-BAR domain-containing endophilin A protein family that is involved in membrane dynamics and trafficking. At the presynaptic terminal, endophilin As participate in synaptic vesicle recycling and autophagosome formation. By gene knockout studies, here we report that postsynaptic endophilin A1 functions in synaptic plasticity. Ablation of endophilin A1 in the hippocampal CA1 region of mature mouse brain impairs long-term spatial and contextual fear memory. Its loss in CA1 neurons postsynaptic of the Schaffer collateral pathway causes impairment in their AMPA-type glutamate receptor-mediated synaptic transmission and long-term potentiation. In KO neurons, defects in the structural and functional plasticity of dendritic spines can be rescued by overexpression of endophilin A1 but not A2 or A3. Further, endophilin A1 promotes actin polymerization in dendritic spines during synaptic potentiation. These findings reveal a physiological role of endophilin A1 distinct from that of other endophilin As at the postsynaptic site

Interactive effects of urine components and treatment conditions on antibiotic degradation of combined system integrating thermally activated peroxydisulfate and membrane distillation using machine learning

The antibiotic degradation in urine is gaining focus as it's essential for resource recovery. Complex organic and inorganic substances in urine impact degradation efficiency through their involvement in radical-type chain reactions. In this study, more than 95 % of sulfamethoxazole (SMX) in urine was successfully degraded in both concurrent and sequential modes of a combined system integrating thermally activated peroxydisulfate and membrane distillation (TAP-MD), previously shown to be effective for resource recovery. Three algorithms, including random forest (RF), XGBoost, and support vector machine (SVM), were applied to model the impact of urine components and treatment conditions on SMX degradation. The XGBoot and RF models, fine-tuned by Bayesian optimization, are accurate and credible in both prediction and interpretation (R2 &gt; 0.90). The models suggest that the improvement of SMX degradation by the MD process is due to PDS enrichment, with high PDS concentrations significantly promoting SMX degradation. Urea and HCO3− are the key factors impacting SMX degradation efficiency, followed by temperature, PDS, Cl−, and NH4+. Additionally, strong interaction effects between urine components were found on SMX degradation, contrasting with results from individual ions that were present in isolation. At high urea concentration (&gt;100 mM), increasing concentrations of Cl−, NH4+, and HCO3− significantly inhibited SMX degradation. As the second key factor on SMX degradation, HCO3− can react with S2O82− without producing free radicals, resulting in a reduction in its yield. Furthermore, in the presence of high concentrations of HCO3−, the Cl− and NH4+ negatively affect the kobs of SMX degradation. The HCO3− competes with SMX for Cl[rad] to produce the weak oxidant CO3∙−, which reacts quickly with NH2∙ without producing other free radicals, causing a loss of free radicals.</p

Interactive effects of urine components and treatment conditions on antibiotic degradation of combined system integrating thermally activated peroxydisulfate and membrane distillation using machine learning

The antibiotic degradation in urine is gaining focus as it's essential for resource recovery. Complex organic and inorganic substances in urine impact degradation efficiency through their involvement in radical-type chain reactions. In this study, more than 95 % of sulfamethoxazole (SMX) in urine was successfully degraded in both concurrent and sequential modes of a combined system integrating thermally activated peroxydisulfate and membrane distillation (TAP-MD), previously shown to be effective for resource recovery. Three algorithms, including random forest (RF), XGBoost, and support vector machine (SVM), were applied to model the impact of urine components and treatment conditions on SMX degradation. The XGBoot and RF models, fine-tuned by Bayesian optimization, are accurate and credible in both prediction and interpretation (R2 &gt; 0.90). The models suggest that the improvement of SMX degradation by the MD process is due to PDS enrichment, with high PDS concentrations significantly promoting SMX degradation. Urea and HCO3− are the key factors impacting SMX degradation efficiency, followed by temperature, PDS, Cl−, and NH4+. Additionally, strong interaction effects between urine components were found on SMX degradation, contrasting with results from individual ions that were present in isolation. At high urea concentration (&gt;100 mM), increasing concentrations of Cl−, NH4+, and HCO3− significantly inhibited SMX degradation. As the second key factor on SMX degradation, HCO3− can react with S2O82− without producing free radicals, resulting in a reduction in its yield. Furthermore, in the presence of high concentrations of HCO3−, the Cl− and NH4+ negatively affect the kobs of SMX degradation. The HCO3− competes with SMX for Cl[rad] to produce the weak oxidant CO3∙−, which reacts quickly with NH2∙ without producing other free radicals, causing a loss of free radicals.</p

Synthesis and Networking of Spaceborne Deployable Prismatic Antenna Mechanisms Based on Graph Theory

Spaceborne deployable cylindrical antennas have broad application prospects in the fields of space earth observation and remote-sensing detection because of their significant advantages of ultralong aperture, high gain, and flexible beam scanning. As application requirements rapidly develop, a new type of spaceborne deployable cylindrical antenna mechanism with a large diameter and deployability is urgently needed. This paper presents an innovative design for a cylindrical deployable antenna mechanism based on 18R triangular prism elements based on graph theory. The correctness of the configuration is verified by developing a prototype. First, four kinds of nonoverconstrained 12-bar triangular prism-stabilized truss structure configurations and their corresponding topological diagrams are constructed. Second, based on graph theory, three types of 102 triangular prism-stabilized truss mechanism configurations that can be folded into linear mechanisms are derived. Third, the kinematic pair configuration is established to achieve a single-degree-of-freedom 7R2U9S triangular prism deployable unit. Fourth, combined with the geometric topology characteristics of the unit network, a triangular prism unit networking method is proposed, and a cylindrical network mechanism configuration based on 18R triangular prism units is obtained. A prototype was fabricated by 3D printing, and an expansion and retraction function test was conducted, which verified the correctness of the theoretical analysis in this paper. Finally, a new concept configuration for a parabolic cylindrical antenna is proposed. This paper provides a reference for the configuration of large-scale folding truss structures with unit expansion