Macrophage migration inhibitory factor (MIF) family in arthropods : Cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain

Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Peer reviewedPostprin

Strong attractors for the nonclassical diffusion equation with fading memory in time-dependent spaces

In this paper, we discuss the long-time behavior of solutions to the nonclassical diffusion equation with fading memory when the nonlinear term $f$ fulfills the polynomial growth of arbitrary order and the external force $g(x)\in L^{2}(\Omega)$. In the framework of time-dependent spaces, we verify the existence and uniqueness of strong solutions by the Galerkin method, then we obtain the existence of the time-dependent global attractor $\mathscr{A}=\{A_t\}_{t\in \mathbb{R}}$ in $\mathcal{M}_t^1$

Regularity of pullback attractors for nonclassical diffusion equations with delay

In this paper, we mainly study the regularity of pullback $\mathcal{D}$-attractors for a nonautonomous nonclassical diffusion equation with delay term $b(t,u_t)$ which contains some hereditary characteristics. Under a critical nonlinearity $f$, a time-dependent force $g(t,x)$ with exponential growth and a delayed force term $b(t,u_t)$, we prove that there exists a pullback $\mathcal{D}$-attractor $\mathcal{A}=\{A(t):t \in \mathbb{R}\}$ in $\mathbb{K}^1=H_0^1(\Omega) \times L^2((-h,0);L^2(\Omega))$ to problem \eqref{ine01} and for each $t \in \mathbb{R}$, $A(t)$ is bounded in $\mathbb{K}^2=H^2(\Omega) \cap H_0^1(\Omega) \times L^2((-h,0);L^2(\Omega))$

Strong global attractors for a three dimensional nonclassical diffusion equation with memory

In this paper, we study the strong global attractors for a three dimensional nonclassical diffusion equation with memory. First, we prove the existence and uniqueness of strong solutions for the equations by the Galerkin method. Then we prove the existence of global attractors for the equations in $H^2(\Omega)\cap H^1_0(\Omega)\times L^2_\mu(\mathbb{R}^+;H^2(\Omega)\cap H^1_0(\Omega))$ by the condition (C).Comment: 17 page

Manganese coordination chemistry of bis(imino)phenoxide derived [2 + 2] Schiff-base macrocyclic ligands

The [2 + 2] Schiff base macrocycles [2,2'-(CH₂CH₂)(C₆H₄N)₂-2,6-(4-RC₆H₃OH)]₂ (IʳH₂), upon reaction with MnCl₂ (two equivalents) afforded the bimetallic complex [Cl₃Mn(NCMe)][MnCl(IᵗᵇᵘH₂)] (2). Under similar conditions, use of the related [2 + 2] oxy-bridged macrocycle [2,2'-O(C₆H₄N=CH)₂4-RC₆H₃OH] (IIʳH₂), afforded the bimetallic complexes [(MnCl)₂IIʳ] (R = Me 3, tBu 4), whilst the macrocycle derived from 1,2-diaminobenzene and 5,5'-di-tert-butyl-2,2'-dihydroxy-3,3'-methylenedibenzaldehyde (IIIH₄) afforded the complex [(MnCl)₂(III)]·2MeCN (5·2MeCN). For comparative studies, the salt complexes [2,6-(ArNHCH)₂-4-MeC₆H₂O][MnCl₃(NCMe)] (Ar = 2,4-Me₂C₆H₃, 6) and {[2,6-(ArNHCH)₂-4-MeC₆H₂O][MnCl}₂[MnCl₄]·8CH₂Cl₂ (Ar = 4-MeC₆H₄, 7·8CH₂Cl₂) were prepared. The crystal structures of 1 - 7 are reported (synchrotron radiation was necessary for complexes 1, 3 and 5). Complexes 1 - 7 (not 5) were screened for their potential to act as pre-catalysts for the ring opening polymerization (ROP) of ε-caprolactone; 3, 4 and 6, 7 were inactive, whilst 1 and 2 exhibited only poor activity low conversion (<15 %) at temperatures above 60 °C

Superresolution Reconstruction of Single Image for Latent features

In recent years, Deep Learning has shown good results in the Single Image Superresolution Reconstruction (SISR) task, thus becoming the most widely used methods in this field. The SISR task is a typical task to solve an uncertainty problem. Therefore, it is often challenging to meet the requirements of High-quality sampling, fast Sampling, and diversity of details and texture after Sampling simultaneously in a SISR task.It leads to model collapse, lack of details and texture features after Sampling, and too long Sampling time in High Resolution (HR) image reconstruction methods. This paper proposes a Diffusion Probability model for Latent features (LDDPM) to solve these problems. Firstly, a Conditional Encoder is designed to effectively encode Low-Resolution (LR) images, thereby reducing the solution space of reconstructed images to improve the performance of reconstructed images. Then, the Normalized Flow and Multi-modal adversarial training are used to model the denoising distribution with complex Multi-modal distribution so that the Generative Modeling ability of the model can be improved with a small number of Sampling steps. Experimental results on mainstream datasets demonstrate that our proposed model reconstructs more realistic HR images and obtains better PSNR and SSIM performance compared to existing SISR tasks, thus providing a new idea for SISR tasks