Characterization of porcine ENO3: genomic and cDNA structure, polymorphism and expression

In this study, a full-length cDNA of the porcine ENO3 gene encoding a 434 amino acid protein was isolated. It contains 12 exons over approximately 5.4 kb. Differential splicing in the 5'-untranslated sequence generates two forms of mRNA that differ from each other in the presence or absence of a 142-nucleotide fragment. Expression analysis showed that transcript 1 of ENO3 is highly expressed in liver and lung, while transcript 2 is highly expressed in skeletal muscle and heart. We provide the first evidence that in skeletal muscle expression of ENO3 is different between Yorkshire and Meishan pig breeds. Furthermore, real-time polymerase chain reaction revealed that, in Yorkshire pigs, skeletal muscle expression of transcript 1 is identical at postnatal day-1 and at other stages while that of transcript 2 is higher. Moreover, expression of transcript 1 is lower in skeletal muscle and all other tissue samples than that of transcript 2, with the exception of liver and kidney. Statistical analysis showed the existence of a polymorphism in the ENO3 gene between Chinese indigenous and introduced commercial western pig breeds and that it is associated with fat percentage, average backfat thickness, meat marbling and intramuscular fat in two different populations

Nano-TiO2/PEEK bioactive composite as a bone substitute material: in vitro and in vivo studies

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Worm Monte Carlo study of the honeycomb-lattice loop model

We present a Markov-chain Monte Carlo algorithm of "worm"type that correctly simulates the O(n) loop model on any (finite and connected) bipartite cubic graph, for any real n>0, and any edge weight, including the fully-packed limit of infinite edge weight. Furthermore, we prove rigorously that the algorithm is ergodic and has the correct stationary distribution. We emphasize that by using known exact mappings when n=2, this algorithm can be used to simulate a number of zero-temperature Potts antiferromagnets for which the Wang-Swendsen-Kotecky cluster algorithm is non-ergodic, including the 3-state model on the kagome-lattice and the 4-state model on the triangular-lattice. We then use this worm algorithm to perform a systematic study of the honeycomb-lattice loop model as a function of n<2, on the critical line and in the densely-packed and fully-packed phases. By comparing our numerical results with Coulomb gas theory, we identify the exact scaling exponents governing some fundamental geometric and dynamic observables. In particular, we show that for all n<2, the scaling of a certain return time in the worm dynamics is governed by the magnetic dimension of the loop model, thus providing a concrete dynamical interpretation of this exponent. The case n>2 is also considered, and we confirm the existence of a phase transition in the 3-state Potts universality class that was recently observed via numerical transfer matrix calculations.Comment: 33 pages, 12 figure

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening

Pansharpening aims to enhance remote sensing image (RSI) quality by merging high-resolution panchromatic (PAN) with multispectral (MS) images. However, prior techniques struggled to optimally fuse PAN and MS images for enhanced spatial and spectral information, due to a lack of a systematic framework capable of effectively coordinating their individual strengths. In response, we present the Cross Modulation Transformer (CMT), a pioneering method that modifies the attention mechanism. This approach utilizes a robust modulation technique from signal processing, integrating it into the attention mechanism's calculations. It dynamically tunes the weights of the carrier's value (V) matrix according to the modulator's features, thus resolving historical challenges and achieving a seamless integration of spatial and spectral attributes. Furthermore, considering that RSI exhibits large-scale features and edge details along with local textures, we crafted a hybrid loss function that combines Fourier and wavelet transforms to effectively capture these characteristics, thereby enhancing both spatial and spectral accuracy in pansharpening. Extensive experiments demonstrate our framework's superior performance over existing state-of-the-art methods. The code will be publicly available to encourage further research

Megadalton-Node Assembly by Binding of Skb1 to the Membrane Anchor Slf1

The plasma membrane contains both dynamic and static microdomains. Given the growing appreciation of cortical microdomains in cell biology, it is important to determine the organizational principles that underlie assembly of compartmentalized structures at the plasma membrane. The fission yeast plasma membrane is highly compartmentalized by distinct sets of cortical nodes, which control signaling for cell cycle progression and cytokinesis. The mitotic inhibitor Skb1 localizes to a set of cortical nodes that provide spatial control over signaling for entry into mitosis. However, it has been unclear whether these nodes contain other proteins and how they might be organized and tethered to the plasma membrane. Here we show that Skb1 forms nodes by interacting with the novel protein Slf1, which is a limiting factor for node formation in cells. Using quantitative fluorescence microscopy and in vitro assays, we demonstrate that Skb1-Slf1 nodes are megadalton structures that are anchored to the membrane by a lipid-binding region in the Slf1 C-terminus. We propose a mechanism for higher-order node formation by Skb1 and Slf1, with implications for macromolecular assemblies in diverse cell types

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.Comment: Accepted by IEEE Transactions on Robotics(T-RO