catena-Poly[[triaqua­(pyridine-κN)nickel(II)]-μ-sulfato-κ2 O:O′]

The title compound, [Ni(SO4)(C5H5N)(H2O)3]n, was synthesized by the hydro­thermal reaction of NiSO4·6H2O, pyridine and water. The central NiII atom is coordinated in a distorted octa­hedral environment by a pyridine N atom, three aqua O atoms and two O atoms of bridging sulfate anions, yielding a zigzag chain. A three-dimensional network is generated via complex hydrogen bonds involving the sulfate and aqua ligands and a pyridine C—H group

Metasomatized lithospheric mantle for Mesozoic giant gold deposits in the North China craton

The origin of giant lode gold deposits of Mesozoic age in the North China craton (NCC) is enigmatic because high-grade metamorphic ancient crust would be highly depleted in gold. Instead, lithospheric mantle beneath the crust is the likely source of the gold, which may have been anomalously enriched by metasomatic processes. However, the role of gold enrichment and metasomatism in the lithospheric mantle remains unclear. Here, we present comprehensive data on gold and platinum group element contents of mantle xenoliths (n = 28) and basalts (n = 47) representing the temporal evolution of the eastern NCC. The results indicate that extensive mantle metasomatism and hydration introduced some gold (<1–2 ppb) but did not lead to a gold-enriched mantle. However, volatile-rich basalts formed mainly from the metasomatized lithospheric mantle display noticeably elevated gold contents as compared to those from the asthenosphere. Combined with the significant inheritance of mantle-derived volatiles in auriferous fluids of ore bodies, the new data reveal that the mechanism for the formation of the lode gold deposits was related to the volatile-rich components that accumulated during metasomatism and facilitated the release of gold during extensional craton destruction and mantle melting. Gold-bearing, hydrous magmas ascended rapidly along translithospheric fault zones and evolved auriferous fluids to form the giant deposits in the crust

Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness

Graph Convolutional Networks (GCNs) have long defined the state-of-the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However, an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process, while seemingly efficient, causes a gradual decay of bone connectivity data, culminating in a model indifferent to the very topology it sought to map. As a remedy, we propose a threefold strategy: (1) We forge an innovative pathway that encodes bone connectivity by harnessing the power of graph distances. This approach preserves the vital topological nuances often lost in conventional GCNs. (2) We highlight an oft-overlooked feature - the temporal mean of a skeletal sequence, which, despite its modest guise, carries highly action-specific information. (3) Our investigation revealed strong variations in joint-to-joint relationships across different actions. This finding exposes the limitations of a single adjacency matrix in capturing the variations of relational configurations emblematic of human movement, which we remedy by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC. This evolution slashes parameters by a substantial margin (above 40%), while elevating performance beyond original GCNs. Our full model, the BlockGCN, establishes new standards in skeleton-based action recognition for small model sizes. Its high accuracy, notably on the large-scale NTU RGB+D 120 dataset, stand as compelling proof of the efficacy of BlockGCN

Refined Temporal Pyramidal Compression-and-Amplification Transformer for 3D Human Pose Estimation

Accurately estimating the 3D pose of humans in video sequences requires both accuracy and a well-structured architecture. With the success of transformers, we introduce the Refined Temporal Pyramidal Compression-and-Amplification (RTPCA) transformer. Exploiting the temporal dimension, RTPCA extends intra-block temporal modeling via its Temporal Pyramidal Compression-and-Amplification (TPCA) structure and refines inter-block feature interaction with a Cross-Layer Refinement (XLR) module. In particular, TPCA block exploits a temporal pyramid paradigm, reinforcing key and value representation capabilities and seamlessly extracting spatial semantics from motion sequences. We stitch these TPCA blocks with XLR that promotes rich semantic representation through continuous interaction of queries, keys, and values. This strategy embodies early-stage information with current flows, addressing typical deficits in detail and stability seen in other transformer-based methods. We demonstrate the effectiveness of RTPCA by achieving state-of-the-art results on Human3.6M, HumanEva-I, and MPI-INF-3DHP benchmarks with minimal computational overhead. The source code is available at https://github.com/hbing-l/RTPCA.Comment: 11 pages, 5 figure

The conserved aromatic residue W-122 is a determinant of potyviral coat protein stability, replication, and cell-to-cell movement in plants

Coat proteins (CPs) play critical roles in potyvirus cell-to-cell movement. However, the underlying mechanism controlling them remains unclear. Here, we show that substitutions of alanine, glutamic acid, or lysine for the conserved residue tryptophan at position 122 (W-122) in tobacco vein banding mosaic virus (TVBMV) CP abolished virus cell-to-cell movement in Nicotiana benthamiana plants. In agroinfiltrated N. benthamiana leaf patches, both the CP and RNA accumulation levels of three W-122 mutant viruses were significantly reduced compared with those of wild-type TVBMV, and CP accumulated to a low level similar to that of a replication-deficient mutant. The results of polyprotein transient expression experiments indicated that CP instability was responsible for the significantly low CP accumulation levels of the three W-122 mutant viruses. The substitution of W-122 did not affect CP plasmodesmata localization or virus particle formation; however, the substitution significantly reduced the number of virus particles. The wild-type TVBMV CP could complement the reduced replication and abolished cell-to-cell movement of the mutant viruses. When the codon for W-122 was mutated to that for a different aromatic residue, phenylalanine or tyrosine, the resultant mutant viruses moved systemically and accumulated up to 80% of the wild-type TVBMV level. Similar results were obtained for the corresponding amino acids of W-122 in the watermelon mosaic virus and potato virus Y CPs. Therefore, we conclude that the aromatic ring in W-122 in the core domain of the potyviral CP is critical for cell-to-cell movement through the effects on CP stability and viral replication.Peer reviewe

Fast kNN Graph Construction with Locality Sensitive Hashing

Abstract. The k nearest neighbors (kNN) graph, perhaps the most popular graph in machine learning, plays an essential role for graphbased learning methods. Despite its many elegant properties, the brute force kNN graph construction method has computational complexity of O(n 2 ), which is prohibitive for large scale data sets. In this paper, based on the divide-and-conquer strategy, we propose an efficient algorithm for approximating kNN graphs, which has the time complexity of O(l(d + log n)n) only (d is the dimensionality and l is usually a small number). This is much faster than most existing fast methods. Specifically, we engage the locality sensitive hashing technique to divide items into small subsets with equal size, and then build one kNN graph on each subset using the brute force method. To enhance the approximation quality, we repeat this procedure for several times to generate multiple basic approximate graphs, and combine them to yield a high quality graph. Compared with existing methods, the proposed approach has features that are: (1) much more efficient in speed (2) applicable to generic similarity measures; (3) easy to parallelize. Finally, on three benchmark large-scale data sets, our method beats existing fast methods with obvious advantages

DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we present DAMO-StreamNet, an optimized framework that combines recent advances from the YOLO series with a comprehensive analysis of spatial and temporal perception mechanisms, delivering a cutting-edge solution. The key innovations of DAMO-StreamNet are: (1) A robust neck structure incorporating deformable convolution, enhancing the receptive field and feature alignment capabilities. (2) A dual-branch structure that integrates short-path semantic features and long-path temporal features, improving motion state prediction accuracy. (3) Logits-level distillation for efficient optimization, aligning the logits of teacher and student networks in semantic space. (4) A real-time forecasting mechanism that updates support frame features with the current frame, ensuring seamless streaming perception during inference. Our experiments demonstrate that DAMO-StreamNet surpasses existing state-of-the-art methods, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) sAP without using extra data. This work not only sets a new benchmark for real-time perception but also provides valuable insights for future research. Additionally, DAMO-StreamNet can be applied to various autonomous systems, such as drones and robots, paving the way for real-time perception. The code is available at https://github.com/zhiqic/DAMO-StreamNet

Repellent activity of Glycosmis plant extracts against two stored product insects

In the present study, the repellent activities of the leaf and/or stem crude extracts of Glycosmis lucida Wall. ex Huang, G. craibii var. glabra, G. craibii Tanaka, G. oligantha Huang, G. pentaphylla (Retz) Correa. and G. esquirolii (Levl.) Tanaka were analyzed by using assays on petri dishes against Tribolium castaneum and Liposcelis bostrychophila. The leaf and stem extracts of G. lucida, G. craibii var. glabra, G. craibii Tanaka, G. oligantha and G. esquirolii possessed significant repellent activities against T. castaneum, the same level repellent with the positive control, DEET. However, the extracts of G. pentaphylla, no repellency but some insect attractant was observed. Moreover, they also showed repellent activities against L. bostrychophila. These results indicate that extracts from G. lucida and G. oligantha leaf could be a source of novel repellent against insects

PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation

Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis \textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge this data disparity gap in target domain. Typically, PoSynDA uses a diffusion-inspired structure to simulate 3D pose distribution in the target domain. By incorporating a multi-hypothesis network, PoSynDA generates diverse pose hypotheses and aligns them with the target domain. To do this, it first utilizes target-specific source augmentation to obtain the target domain distribution data from the source domain by decoupling the scale and position parameters. The process is then further refined through the teacher-student paradigm and low-rank adaptation. With extensive comparison of benchmarks such as Human3.6M and MPI-INF-3DHP, PoSynDA demonstrates competitive performance, even comparable to the target-trained MixSTE model\cite{zhang2022mixste}. This work paves the way for the practical application of 3D human pose estimation in unseen domains. The code is available at https://github.com/hbing-l/PoSynDA.Comment: Accepted to ACM Multimedia 2023; 10 pages, 4 figures, 8 tables; the code is at https://github.com/hbing-l/PoSynD