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This note gives a classification for the spectral measures of CMV matrices, which are special unitary five-diagonal matrices related to orthogonal polynomials on the unit circle, with respect to packing measures. [ABSTRACT FROM AUTHOR]

Our previous studies showed that SYISL is a negative regulator of muscle growth and regeneration in mice, pigs and humans. SYISL knockout resulted in an increase in the density of muscle fibers and muscle growth. However, it is unclear whether there are natural mutations in pig SYNPO2 intron sense‐overlapping lncRNA (pSYISL) that affect the expression of pSYISL and muscle growth traits. In this study, three SNPs in exons and six SNPs within the promoter of pSYISL were identified. Association analysis showed that the two SNPs in exons are significantly associated with loin muscle area (p < 0.05); the six SNPs in the promoter that show complete linkage are significantly associated with live backfat thickness and live loin muscle area in American Large White pigs. Bioinformatics and luciferase reporter assays as well as in vitro binding experiments indicated that the mutation of SNP rs702045770 (g.539G>A) leads to the loss of YY1 binding to the promoter, thus affecting the expression level of pSYISL, and we found that Jiangshan Black pigs with genotype GG have a higher expression level of pSYISL than genotype AA individuals, but the muscle fiber density was significantly lower than in genotype AA individuals. Furthermore, the association analysis showed that the carcass backfat thickness of genotype GG of SNP rs702045770 was significantly higher than that of other genotypes in (Pietrain × Duroc) × (Landrace × Yorkshire) crossbred pigs (p < 0.05). The glycolytic potential of genotype GG was significantly higher than that of other genotypes (p < 0.05). These results provide novel insight into the identification of functional SNPs in non‐coding genomic regions. [ABSTRACT FROM AUTHOR]

Domain adaptation solves the challenge of inadequate labeled samples in the target domain by leveraging the knowledge learned from the labeled source domain. Most existing approaches aim to reduce the domain shift by performing some coarse alignments such as domain-wise alignment and class-wise alignment. To circumvent the limitation, we propose a coarse-to-fine unsupervised domain adaptation method based on metric learning, which can fully utilize more geometric structure and sample-wise information to obtain a finer alignment. The main advantages of our approach lie in four aspects: (1) it employs a structure-preserving algorithm to automatically select the optimal subspace dimension on the Grassmannian manifold; (2) based on coarse distribution alignment using maximum mean discrepancy, it utilizes the smooth triplet loss to leverage the supervision information of samples to improve the discrimination of data; (3) it introduces structure regularization to preserve the geometry of samples; (4) it designs a graph-based sample reweighting method to adjust the weight of each source domain sample in the cross-domain task. Extensive experiments on several public datasets demonstrate that our method achieves remarkable superiority over several competitive methods (more than 1.5% improvement of the average classification accuracy over the best baseline). [ABSTRACT FROM AUTHOR]

Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network‐based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non‐rigid cases and propose a network called the Scale Robust Point Matching (SRPM)‐Net for scale point matching. This robust structure‐preserving network is implemented by incorporating Lie group parametrisation. It is conducted by Lie group linearisation representation with the constraints of parameters under the corresponding basis of Lie algebra. SRPM‐Net preserves the structure of the solution and avoids degeneration. The contributions of this paper lie in two aspects: Most importantly, SRPM‐Net provides an extendable framework for handling complicated transformations. Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. Experimental results show that SRPM‐Net is more robust and accurate than existing traditional and recent deep learning methods under various situations. [ABSTRACT FROM AUTHOR]

KPNA7 is an essential nuclear protein for early embryogenesis and normal fertility. The aim of our study was to determine the associations between single nucleotide polymorphisms (SNPs) in the KPNA7 gene and the reproductive traits in France Large White pigs. Six new SNPs were identified by sequencing. Sows with CT genotype of rs81308652 had a significantly higher total number born than sows with CC and TT genotype in first parity. Litters from sows with TT genotype of rs81308652 had a significantly lower number of weak births than litters from sows with CC and CT genotype in multi parity. Individuals with GG genotype of rs327848277 showed higher litter weight at birth (LWB) and number of healthy births than individuals with genotype GT and TT in first parity, and individuals with genotype GG had lower LWB compared to other genotypes in multi parity. However, we obtained no statistically significant results for association between the SNPs in other four loci and reproductive traits in both primiparous and multiparous pigs. In conclusion, our results show that there are significant association of identified SNPs located in KPNA7 gene with pig reproductive traits, and provided a theoretical basis for genetic improvement of pig reproductive traits. [ABSTRACT FROM AUTHOR]

The leaderless consensus of fractional-order multi-agent systems (FOMASs) by intermittence sampled data control method is investigated in this brief, for which a distributed intermittence sampled data control protocol is presented to reduce the updating rate and working time of controllers. Subsequently, the Laplace transform and the stability theory are utilized to derive some necessary and sufficient consensus criteria that show the relations among the fractional order, sampling period, communication width, coupling strengths, and network topology. What is more, it can be discovered that both the communication width and sampling period have the lower and upper bounds for given networks to attain leaderless consensus. Finally, a numerical simulation example is presented to prove the theoretical results. [ABSTRACT FROM AUTHOR]

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods. [ABSTRACT FROM AUTHOR]

Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. [ABSTRACT FROM AUTHOR]

Solvent effect is one of the important factors in sample preparation which may affect matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra of synthetic polymers. MALDI imaging, a useful imaging tool for discovering biomarkers in tissues, is applied here for better comprehension of solvent effect in polymer analysis by MALDI-TOF mass spectrometry. Nylon-6 was chosen as a model polymer for the study of solvent effect. Its MALDI mass spectra in different solvents were performed. MALDI imaging analysis was performed for studying the incorporation of analytes into matrix crystals in different solvent combinations. Specifically, the colocalization of matrix and analyte was obtained through Pearson’s correlation (PC) coefficient analysis of their MALDI images. The results demonstrated that satisfactory spectra were obtained in higher PC value conditions. PC decreased along with an increase in the ratio of poor solvent, which suggested that we should minimize the poor solvent ratio to obtain better MALDI spectra. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an object(s)-of-interest (OOI) segmentation method for images with inhomogeneous intensities. First, we define a discrimination function for each pixel, labelling whether the pixel belongs to OOI based on the characteristics of OOI. This function is then integrated with image gradient to construct a stopping function in an energy functional. Finally, this energy functional is minimized by means of level set evolution, which guides the motion of the zero level set toward object boundaries. The results demonstrate that our model is effective. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor. [ABSTRACT FROM AUTHOR]

In this paper, numerical methods to compute the Karcher means on the n -order rotation group SO( n ) are considered. First, after recalling Karcher means as solutions of a kind of minimization problems on SO( n ), a super-linearly convergent numerical method, namely conjugate gradient method, has been used to deal with them. By the geometric structure of SO( n ), the proposed algorithm is structure preserving. Then, a variety of numerical experiments are presented to demonstrate the performance and efficiency of the proposed algorithm by comparing with a recent structure preserving method. [ABSTRACT FROM AUTHOR]

Quaternion singular value decomposition (QSVD) is a robust technique of digital watermarking that extracts high quality watermarks from watermarked images with low distortion. However, the existing QSVD-based watermarking schemes face the obstacle of "explosion of complexity" and have much room for improvement in terms of real-time, invisibility, and robustness. In this paper, we overcome such obstacle by introducing a new real structure-preserving QSVD algorithm and propose a novel QSVD-based watermarking scheme with high efficiency. Secret information is transmitted blindly by incorporating two new strategies: coefficient pair selection and adaptive embedding. The highly correlated coefficient pairs determined by the normalized cross-correlation method reduce the impact of embedding by reducing the maximum modification of the coefficient values, resulting in high fidelity of the watermarked image. Large-size 8-color binary watermark and QR code effectively verify that the proposed watermarking scheme can resist various image attacks in numerical experiments. Two keys designed by Logistic chaotic map ensure the security of the watermarking system. Under the premise of considering the correlation of color channels, the proposed watermarking scheme not only performs well in real-time and invisibility, but also has satisfactory advantages in robustness compared with the state-of-the-art methods.

Long noncoding RNAs (lncRNAs) play important roles in the spatial and temporal regulation of muscle development and regeneration. Nevertheless, the determination of their biological functions and mechanisms underlying muscle regeneration remains challenging. Here, we identified a lncRNA named lncMREF (lncRNA muscle regeneration enhancement factor) as a conserved positive regulator of muscle regeneration among mice, pigs and humans. Functional studies demonstrated that lncMREF, which is mainly expressed in differentiated muscle satellite cells, promotes myogenic differentiation and muscle regeneration. Mechanistically, lncMREF interacts with Smarca5 to promote chromatin accessibility when muscle satellite cells are activated and start to differentiate, thereby facilitating genomic binding of p300/CBP/H3K27ac to upregulate the expression of myogenic regulators, such as MyoD and cell differentiation. Our results unravel a novel temporal-specific epigenetic regulation during muscle regeneration and reveal that lncMREF/Smarca5-mediated epigenetic programming is responsible for muscle cell differentiation, which provides new insights into the regulatory mechanism of muscle regeneration., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Semantic segmentation has achieved great progress by effectively fusing features of contextual information. In this article, we propose an end-to-end semantic attention boosting (SAB) framework to adaptively fuse the contextual information iteratively across layers with semantic regularization. Specifically, we first propose a pixelwise semantic attention (SAP) block, with a semantic metric representing the pixelwise category relationship, to aggregate the nonlocal contextual information. In addition, we improve the computation complexity of SAP block from O(n²) to O(n) for images with size n. Second, we present a categorywise semantic attention (SAC) block to adaptively balance the nonlocal contextual dependencies and the local consistency with a categorywise weight, overcoming the contextual information confusion caused by the feature imbalance within intra-category. Furthermore, we propose the SAB module to refine the segmentation with SAC and SAP blocks. By applying the SAB module iteratively across layers, our model shrinks the semantic gap and enhances the structure reasoning by fully utilizing the coarse segmentation information. Extensive quantitative evaluations demonstrate that our method significantly improves the segmentation results and achieves superior performance on the PASCAL VOC 2012, Cityscapes, PASCAL Context, and ADE20K datasets.

lncMGPF is a novel positive regulator of myogenic differentiation, muscle growth and regeneration in mouse, pig, and human. But whether natural mutations within lncMGPF gene regulate animal meat production traits is unclear. In this study, ten single nucleotide polymorphisms (SNPs) of pig lncMGPF ( plncMGPF ) gene were identified among commercial pig breeds and Chinese local pig breeds. These SNPs are highly linked and constructed into multiple haplotypes, and haplotype ATTCATGTTC (H1) mainly exists in commercial pig breeds while haplotype GCCTGCACCT (H3) is more frequent in Chinese local pig breeds. Association analysis indicated that all SNPs are significantly associated with the backfat thickness and loin muscle area ( P < 0.05), respectively, and homologous H1 individuals have higher loin muscle area and lower backfat thickness than H3 pigs. Bioinformatics and functional analysis showed that haplotype H1 has a longer half-life and more stable RNA secondary structure than haplotype H3. plncMGPF haplotype H1 has stronger effects on pig primary myogenic progenitor cells differentiation and muscle growth than haplotype H3. Further experiments showed that two SNPs (rs81403974 and rs325492834) function together to confer plncMGPF stability and function. Our observation suggested that the SNPs in lncMGPF can change the RNA stabilities and lncMGPF function, thereby affecting the porcine meat production traits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Lv, Zhao, Hou, Tong, Peng, Li, Xu and Zuo.)

The brown planthopper (BPH), Nilaparvata lugens Stål, is one of the major pests of rice. It uses its stylet to penetrate rice phloem, feeding on rice sap and causing direct damage to rice or even plant death. During the feeding process, BPHs secrete saliva into plant tissues, which plays crucial roles in the plant-insect interactions. However, little is known about how the salivary proteins secreted by BPH affect feeding ability and how they induce plant immune responses. Here, we identified an N. lugens Salivary Protein 1 (NlSP1) by screening salivary proteome and characterized its functions in BPH and plants. NlSP1 induces cell death, H 2 O 2 accumulation, the expression of defense-related genes, and callose deposition in planta . The active region of NlSP1 that induces plant cell death is located in its N-terminal region. Inhibition of NlSP1 expression in BPHs reduced their feeding ability and had a lethal effect on them. Most importantly, we demonstrated that NlSP1 was able to be secreted into rice plant during feeding process and form a complex with certain interacting partner of rice. These results provide a detailed characterization of a salivary protein from BPHs and offers new insights into our understanding of rice-BPH interaction., (Copyright © 2020 Huang, Zhang, Shan, Peng, Guo, Zhou, Shi, Zheng, Wu, Guan, Yang, Du, Zhu, Yuan, He and Chen.)