Semantic Segmentation Using Super Resolution Technique as Pre-Processing

Combining high-level and low-level visual tasks is a common technique in the field of computer vision. This work integrates the technique of image super resolution to semantic segmentation for document image binarization. It demonstrates that using image super-resolution as a preprocessing step can effectively enhance the results and performance of semantic segmentation

Characterization of TgPuf1, a member of the Puf family RNA-binding proteins from Toxoplasma gondii

BACKGROUND: Puf proteins act as translational regulators and affect many cellular processes in a wide range of eukaryotic organisms. Although Puf proteins have been well characterized in many model systems, little is known about the structural and functional characteristics of Puf proteins in the parasite Toxoplasma gondii. METHODS: Using a combination of conventional molecular approaches, we generated endogenous TgPuf1 tagged with hemagglutinin (HA) epitope and investigated the TgPuf1 expression levels and localization in the tachyzoites and bradyzoites. We used RNA Electrophoretic Mobility Shfit Assay (EMSA) to determine whether the recombination TgPuf1 has conserverd RNA binding activity and specificity. RESULTS: TgPuf1 was expressed at a significantly higher level in bradyzoites than in tachyzoites. TgPuf1 protein was predominantly localized within the cytoplasm and showed a much more granular cytoplasmic staining pattern in bradyzoites. The recombinant Puf domain of TgPuf1 showed strong binding affinity to two RNA fragments containing Puf-binding motifs from other organisms as artificial target sequences. However, two point mutations in the core Puf-binding motif resulted in a significant reduction in binding affinity, indicating that TgPuf1 also binds to conserved Puf-binding motif. CONCLUSIONS: TgPuf1 appears to exhibit different expression levels in the tachyzoites and bradyzoites, suggesting that TgPuf1 may function in regulating the proliferation or/and differentiation that are important in providing parasites with the ability to respond rapidly to changes in environmental conditions. This study provides a starting point for elucidating the function of TgPuf1 during parasite development

Pt nanoparticles decorated rose-like Bi2O2CO3 configurations for efficient photocatalytic removal of water organic pollutants

Pt nanoparticles decorated with rose-like Bi2O2CO3 configurations were synthesized via a simple photoreduction method at room temperature. The structure, morphology, optical and electronic properties, and photocatalytic performance of the as-prepared materials were characterized. Compared to pure Bi2O2CO3, the Pt/Bi2O2CO3 photocatalysts show better performance in decomposing RhB, BPA and OTC under visible light (? > 420 nm). The enhanced photocatalytic activity of Pt/Bi2O2CO3 could be attributed to the modification in light absorption (? > 420 nm) charge migration and the separation of photo-generated electrons (e?) and holes (h+). Free radical trapping experiments demonstrated that the main active species of the catalytic reaction are different in decomposing RhB and BPA.publishersversionPeer reviewe

Rice Calcineurin B-Like Protein-Interacting Protein Kinase 31 (OsCIPK31) Is Involved in the Development of Panicle Apical Spikelets

Panicle apical abortion (PAA) causes severe yield losses in rice production, but details about its development and molecular basis remain elusive. Herein, a PAA mutant, paa1019, was identified among the progeny of an elite indica maintainer rice line Yixiang 1B (YXB) mutagenized population obtained using ethyl methyl sulfonate. The abortion rate of spikelets in paa1019 was observed up to 60%. Genetic mapping combined with Mutmap analysis revealed that LOC_Os03g20380 harbored a single-bp substitution (C to T) that altered its transcript length. This gene encodes calcineurin B-like protein-interacting protein kinase 31 (OsCIPK31) localized into the cytoplasm, and is preferentially expressed in transport tissues of rice. Complementation of paa1019 by transferring the open reading frame of LOC_Os03g20380 from YXB reversed the mutant phenotype, and conversely, gene editing by knocking out of OsCIPK31 in YXB results in PAA phenotype. Our results support that OsCIPK31 plays an important role in panicle development. We found that dysregulation is caused by the disruption of OsCIPK31 function due to excessive accumulation of ROS, which ultimately leads to cell death in rice panicle. OsCIPK31 and MAPK pathway might have a synergistic effect to lead ROS accumulation in response to stresses. Meanwhile the PAA distribution is related to IAA hormone accumulation in the panicle. Our study provides an understanding of the role of OsCIPK31 in panicle development by responding to various stresses and phytohormones

Class label- versus sample label-based CCA

When correlating the samples with the corresponding class labels, canonical correlation analysis (CCA) can be used for supervised feature extraction and subsequent classification. Intuitively, different encoding modes for class label can result in different classification performances. However, actually, when the samples in each class share a common class label as in usual cases, a unified formulation of CCA is not only derived naturally, but also more importantly from it, we can get some insight into the shortcoming of the existing feature extraction using CCA for sequent classification: the existing encodings for class label fail to reflect the difference among the samples such as in central region of class and those in mixture overlapping region among classes, consequently resulting in its equivalence to the traditional linear discriminant analysis (LDA) for some commonly-used class-label encodings. To reflect such a difference between the samples, we elaborately design an independent soft label for each sample of each class rather than a common label for all the samples of the same class. A purpose of doing so is to try to promote CCA classification performance. The experiments show that this soft label based CCA is better than or comparable to the original CCA/LDA in terms of the recognition performance

Locality Preserving CCA with Applications to Data Visualization and Pose Estimation

Abstract- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality reduction and has been applied to image processing, pose estimation and other fields. However, it fails to discover or reveal the nonlinear correlation relationship between two sets of features. In contrast, its kernelized nonlinear version, KCCA, can overcome such a shortcoming, but the global kernelization of CCA restrains KCCA itself from effectively discovering the local structure of the data with complex and nonlinear characteristics. Recently, the locality methods, such as locally linear embedding (LLE) and locality preserving projections (LPP), are proposed to discover the low dimensional manifold embedded in the original high dimensional space. Compared to the subspace based methods, these locality methods take into account the local neighborhood structure of the data, and can discover the intrinsic structure of data to a better degree, which benefits to the subsequent computation. Inspired by the locality based methods, in this paper, we incorporate such an idea into CCA and propose locality preserving CCA (LPCCA) to discover the local manifold structure of the data and further apply it to data visualization and pose estimation. In addition, a fast algorithm of LPCCA is proposed for some special cases. The experiments show that LPCCA can both capture the intrinsic structure characteristic of the given data and achieve higher pose estimation accuracy than both CCA and KCCA

Class-Information-Incorporated Principal Component Analysis

Abstract: Principal component analysis (PCA) is one of the most popular feature extraction methods in pattern recognition and can obtain a set of so-needed projection directions or vectors by maximizing the projected variance of a given training data set in an unsupervised learning way, meaning that PCA does not sufficiently use the class label of given data in feature extraction. In this paper, a class-information-incorporated PCA (CIPCA) is presented with two objectives: one is to sufficiently utilize a given class label in feature extraction and the other is to still follow the same simple mathematical formulation as PCA. The experimental results on 13 benchmark datasets show its feasibility and effectiveness. Keywords: Principal component analysis (PCA); Class-information-incorporated PCA (CIPCA); Pattern recognition

Discriminative canonical correlation analysis with missing samples

Multimodal recognition emerges when the non-robustness of unimodal recognition is noticed in real applications. Canonical correlation analysis (CCA) is a powerful tool of feature fusion for multimodal recognition. However, in CCA, the samples must be pairwise, and this requirement may not easily be met due to various unexpected reasons. Additionally, the class information of the samples is not fully exploited in CCA. These disadvantages restrain CCA from extracting more discriminative features for recognition. To tackle these problems, in this paper, the class information is incorporated into the framework of CCA for recognition, and a novel method for multimodal recognition, called discriminative canonical correlation analysis with missing samples (DCCAM), is proposed. DCCAM can tolerate the missing of samples and need not artificially make up the missing samples so that its computation is timesaving and space-saving. The experimental results show that 1) DCCAM outperforms the related multimodal recognition methods; and 2) the recognition accuracy of DCCAM is relatively insensitive to the number of missing samples