Tetra­ethyl­ammonium dicyanidobis(ethane-1,2-dithiol­ato)tetra-μ3-sulfido-dimolybdenum(V)dicopper(I)

The title compound, (C8H20N)2[Cu2Mo2(C2H4S2)2(CN)2S4], is a sulfide-bridged tetranuclear complex in which the complex anion comprises one [(edt)2Mo2S2(μ-S)2]2− unit (edt = ethanedithiol­ate) and two CuCN units joined through six Cu—μ3-S bonds, thus forming a cubane-like [Mo2S4Cu2] core. There are two independent cation–anion complex entities in the asymmetric unit. Bond distances are normal for this type of complex [ranges: Mo—S = 2.193 (2)–2.390 (2); Cu—S = 2.266 (2)–2.470 (2); Cu—C = 1.899 (7)–1.911 (9) Å]. One of the thiol­ato C atoms is disordered over two sites in a 0.52 (3):0.48 (3) ratio

The Sparse-Grid-Based Adaptive Spectral Koopman Method

The adaptive spectral Koopman (ASK) method was introduced to numerically solve autonomous dynamical systems that lay the foundation of numerous applications across different fields in science and engineering. Although ASK achieves high accuracy, it is computationally more expensive for multi-dimensional systems compared with conventional time integration schemes like Runge-Kutta. In this work, we combine the sparse grid and ASK to accelerate the computation for multi-dimensional systems. This sparse-grid-based ASK (SASK) method uses the Smolyak structure to construct multi-dimensional collocation points as well as associated polynomials that are used to approximate eigenfunctions of the Koopman operator of the system. In this way, the number of collocation points is reduced compared with using the tensor product rule. We demonstrate that SASK can be used to solve partial differential equations based-on their semi-discrete forms. Numerical experiments illustrate that SASK balances the accuracy with the computational cost, and hence accelerates ASK.Comment: 21 pages, 5 figure

Molecular cloning and expression of a novel trehalose synthase gene from Enterobacter hormaechei

<p>Abstract</p> <p>Background</p> <p>Trehalose synthase (TreS) which converts maltose to trehalose is considered to be a potential biocatalyst for trehalose production. This enzymatic process has the advantage of simple reaction and employs an inexpensive substrate. Therefore, new TreS producing bacteria with suitable enzyme properties are expected to be isolated from extreme environment.</p> <p>Results</p> <p>Six TreS producing strains were isolated from a specimen obtained from soil of the Tibetan Plateau using degenerate PCR. A novel <it>treS </it>gene from <it>Enterobacter hormaechei </it>was amplified using thermal asymmetric interlaced PCR. The gene contained a 1626 bp open reading frame encoding 541 amino acids. The gene was expressed in <it>Escherichia coli</it>, and the recombinant TreS was purified and characterized. The purified TreS had a molecular mass of 65 kDa and an activity of 18.5 U/mg. The optimum temperature and pH for the converting reaction were 37°C and 6, respectively. Hg²⁺, Zn²⁺, Cu²⁺and SDS inhibited the enzyme activity at different levels whereas Mn²⁺showed an enhancing effect by 10%.</p> <p>Conclusion</p> <p>In this study, several TreS producing strains were screened from a source of soil bacteria. The characterization of the recombinant TreS of <it>Enterobacter hormaechei </it>suggested its potential application. Consequently, a strategy for isolation of TreS producing strains and cloning of novel <it>treS </it>genes from natural sources was demonstrated.</p

An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems

In this paper we present an asymptotically compatible meshfree method for solving nonlocal equations with random coefficients, describing diffusion in heterogeneous media. In particular, the random diffusivity coefficient is described by a finite-dimensional random variable or a truncated combination of random variables with the Karhunen-Lo\`{e}ve decomposition, then a probabilistic collocation method (PCM) with sparse grids is employed to sample the stochastic process. On each sample, the deterministic nonlocal diffusion problem is discretized with an optimization-based meshfree quadrature rule. We present rigorous analysis for the proposed scheme and demonstrate convergence for a number of benchmark problems, showing that it sustains the asymptotic compatibility spatially and achieves an algebraic or sub-exponential convergence rate in the random coefficients space as the number of collocation points grows. Finally, to validate the applicability of this approach we consider a randomly heterogeneous nonlocal problem with a given spatial correlation structure, demonstrating that the proposed PCM approach achieves substantial speed-up compared to conventional Monte Carlo simulations

Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos

Generating text-editable and pose-controllable character videos have an imperious demand in creating various digital human. Nevertheless, this task has been restricted by the absence of a comprehensive dataset featuring paired video-pose captions and the generative prior models for videos. In this work, we design a novel two-stage training scheme that can utilize easily obtained datasets (i.e.,image pose pair and pose-free video) and the pre-trained text-to-image (T2I) model to obtain the pose-controllable character videos. Specifically, in the first stage, only the keypoint-image pairs are used only for a controllable text-to-image generation. We learn a zero-initialized convolu- tional encoder to encode the pose information. In the second stage, we finetune the motion of the above network via a pose-free video dataset by adding the learnable temporal self-attention and reformed cross-frame self-attention blocks. Powered by our new designs, our method successfully generates continuously pose-controllable character videos while keeps the editing and concept composition ability of the pre-trained T2I model. The code and models will be made publicly available.Comment: Project page: https://follow-your-pose.github.io/; Github repository: https://github.com/mayuelala/FollowYourPos

Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection

Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted significant attention due to the growing demand for video analysis. Recent approaches treat MR and HD as similar video grounding problems and address them together with transformer-based architecture. However, we observe that the emphasis of MR and HD differs, with one necessitating the perception of local relationships and the other prioritizing the understanding of global contexts. Consequently, the lack of task-specific design will inevitably lead to limitations in associating the intrinsic specialty of two tasks. To tackle the issue, we propose a Unified Video COMprehension framework (UVCOM) to bridge the gap and jointly solve MR and HD effectively. By performing progressive integration on intra and inter-modality across multi-granularity, UVCOM achieves the comprehensive understanding in processing a video. Moreover, we present multi-aspect contrastive learning to consolidate the local relation modeling and global knowledge accumulation via well aligned multi-modal space. Extensive experiments on QVHighlights, Charades-STA, TACoS , YouTube Highlights and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which outperforms the state-of-the-art methods by a remarkable margin

Inadequate gastric preparation and its associated factors for magnetically controlled capsule endoscopy

Goals: To explore factors associated with inadequate gastric preparation for MCE.Background: Factors associated with inadequate gastric preparation for magnetically controlled capsule endoscopy (MCE) remains unclear.Study: Data of patients who underwent MCE from June 2021 to July 2022 were prospectively collected. The gastric cleanliness score (GCS) of the six stomach regions (gastric cardia, fundus, body, angulus, antrum, and pylorus) was recorded. Patients with GCS score ≥18 were defined as the adequate preparation. Factors related to inadequate gastric preparation were analyzed using a logistic regression model with estimated odds ratios (OR).Results: The mean GCS score of 211 patients was 17.01 ± 2.82. In the multivariable analysis, proton pump inhibitor (PPI) use (OR 3.57; 95% CI 1.69–7.95; p &lt; 0.01) and premedication time after administering simethicone &lt;30 min (OR 2.86; 95% CI 1.10–7.39; p = 0.03) were independent risk factors for inadequate gastric preparation. Comparing the gastric cleanliness of different locations, the median GCS of the lower stomach [10.00, IQR (9.50, 11.00)] was significantly higher than that of the upper stomach [7.00, IQR (6.00, 8.00)] (p &lt;0.001).Conclusion: PPI use and inadequate premedication time (&lt;30 min) may reduce the quality of gastric preparation for MCE. The type, dose, duration of medication, and discontinuation time of PPIs was well worth further exploration. Appropriate control of the type and time of premedication may be the key to improving overall gastric cleanliness