Spin-current Seebeck effect in quantum dot systems

We first bring up the concept of spin-current Seebeck effect based on a recent experiment [Nat. Phys. {\bf 8}, 313 (2012)], and investigate the spin-current Seebeck effect in quantum dot (QD) systems. Our results show that the spin-current Seebeck coefficient $S$ is sensitive to different polarization states of QD, and therefore can be used to detect the polarization state of QD and monitor the transitions between different polarization states of QD. The intradot Coulomb interaction can greatly enhance the $S$ due to the stronger polarization of QD. By using the parameters for a typical QD, we demonstrate that the maximum $S$ can be enhanced by a factor of 80. On the other hand, for a QD whose Coulomb interaction is negligible, we show that one can still obtain a large $S$ by applying an external magnetic field.Comment: 6 pages, 8 figure

Methyl 3,4-bis­(cyclo­propyl­meth­oxy)benzoate

The title compound, C16H20O4, was obtained unintentionally as the byproduct of an attempted synthesis of methyl 3-(cyclo­propyl­meth­oxy)-4-hy­droxy­benzoate. In the crystal, the mol­ecules are linked by inter­molecular C—H⋯O inter­actions

Enchanced levels of apolipoprotein M during HBV infection feedback suppresses HBV replication

<p>Abstract</p> <p>Background</p> <p>Chronic liver diseases can interfere with hepatic metabolism of lipoproteins, apolipoproteins. Hepatitis B virus (HBV) is a major etiological agent causing acute and chronic liver diseases. Apolipoprotein M (ApoM) is a high-density lipoprotein (HDL) apolipoprotein and exclusively expressed in the liver parenchyma cells and in the tubular cells of the kidney. This study was to determine the correlation between HBV infection and ApoM expression.</p> <p>Materials and methods</p> <p>Serum ApoM levels in patients with HBV infection and in healthy individuals were measured by ELISA, ApoM mRNA expression were determined by RT-PCR, and the expression of S and E proteins of HBV, as well as the synthesis of viral DNA were measured by ELISA and real-time PCR.</p> <p>Results</p> <p>The levels of serum ApoM was significantly elevated in patients as compared to healthy individuals (<it>P </it>< 0.001), ApoM promoter activity, mRNA and protein expression were all stimulated in cells transfected with infectious HBV clone. In addition, ApoM decreases the expression of S and E proteins of HBV and the synthesis of viral DNA.</p> <p>Conclusion</p> <p>Raised ApoM levels in HBV infection may in turn suppress HBV replication, one of the protective mechanisms of nature.</p

Identification of a Novel Gene for Biosynthesis of a Bacteroid-Specific Electron Carrier Menaquinone

Ubiquinone (UQ) has been considered as an electron mediator in electron transfer that generates ATP in Rhizobium under both free-living and symbiosis conditions. When mutated, the dmtH gene has a symbiotic phenotype of forming ineffective nodules on Astragalus sinicus. The gene was isolated from a Mesorhizobium huakuii 7653R transposon-inserted mutant library. The DNA sequence and conserved protein domain analyses revealed that dmtH encodes demethylmenaquinone (DMK) methyltransferase, which catalyzes the terminal step of menaquinone (MK) biosynthesis. Comparative analysis indicated that dmtH homologs were present in only a few Rhizobia. Real-time quantitative PCR showed dmtH is a bacteroid-specific gene. The highest expression was seen at 25 days after inoculation of strain 7653R. Gene disruption and complementation tests demonstrated that the dmtH gene was essential for bacteroid development and symbiotic nitrogen fixation ability. MK and UQ were extracted from the wild type strain 7653R and mutant strain HK116. MK-7 was accumulated under microaerobic condition and UQ-10 was accumulated under aerobic condition in M. huakuii 7653R. The predicted function of DmtH protein was confirmed by the measurement of methyltransferase activity in vitro. These results revealed that MK-7 was used as an electron carrier instead of UQ in M. huakuii 7653R bacteroids

Hypergraph Transformer for Skeleton-based Action Recognition

Skeleton-based action recognition aims to predict human actions given human joint coordinates with skeletal interconnections. To model such off-grid data points and their co-occurrences, Transformer-based formulations would be a natural choice. However, Transformers still lag behind state-of-the-art methods using graph convolutional networks (GCNs). Transformers assume that the input is permutation-invariant and homogeneous (partially alleviated by positional encoding), which ignores an important characteristic of skeleton data, i.e., bone connectivity. Furthermore, each type of body joint has a clear physical meaning in human motion, i.e., motion retains an intrinsic relationship regardless of the joint coordinates, which is not explored in Transformers. In fact, certain re-occurring groups of body joints are often involved in specific actions, such as the subconscious hand movement for keeping balance. Vanilla attention is incapable of describing such underlying relations that are persistent and beyond pair-wise. In this work, we aim to exploit these unique aspects of skeleton data to close the performance gap between Transformers and GCNs. Specifically, we propose a new self-attention (SA) extension, named Hypergraph Self-Attention (HyperSA), to incorporate inherently higher-order relations into the model. The K-hop relative positional embeddings are also employed to take bone connectivity into account. We name the resulting model Hyperformer, and it achieves comparable or better performance w.r.t. accuracy and efficiency than state-of-the-art GCN architectures on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. On the largest NTU RGB+D 120 dataset, the significantly improved performance reached by our Hyperformer demonstrates the underestimated potential of Transformer models in this field

Poly[[bis­(μ-4,4′-bipyridine-κ2 N:N′)copper(I)] perchlorate 0.24-hydrate]

The title copper(I) polymeric compound, {[Cu(C10H8N2)2]ClO4·0.24H2O}n, obtained by the reaction of Cu(ClO4)2 and 4,4′-bipyridine (4,4′-bpy) under hydro­thermal conditions, features a fourfold-inter­penetrated diamondoid coordination framework. The asymmetric unit consists of two CuI atoms, three 4,4′-bpy ligands in general positions and two halves of two centrosymmetric 4,4′-bpy ligands, two ClO4 − anions and water mol­ecule with a site-occupancy factor of 0.480 (17). The CuI atoms are in a distorted tetra­hedral coordination environment and are bridged by 4,4′-bpy ligands, forming a diamondoid cationic polymeric framework that encloses two symmetry-independent channels along [100], which accommodate perchlorate anions and water mol­ecules

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