Near-infrared fluorophores methylene blue for targeted imaging of the stomach in intraoperative navigation

Near-infrared (NIR) fluorescence imaging-guided surgery is increasingly concerned in gastrointestinal surgery because it can potentially improve clinical outcomes. This new technique can provide intraoperative image guidance for surgical margin evaluation and help surgeons examine residual lesions and small tumors during surgery. NIR fluorophores methylene blue (MB) is a promising fluorescent probe because of its safety and intraoperative imaging in the clinic. However, whether MB possesses the potential to perform intraoperative navigation of the stomach and gastric tumors needs to be further explored. Therefore, the current study mainly validated MB’s usefulness in animal models’ intraoperative imaging of stomach and gastric tumors. NIR fluorophores MB can exhibit specific uptake by the gastric epithelial cells and cancer cells. It is primarily found that MB can directly target the stomach in mice. Interestingly, MB was applied for the NIR imaging of gastric cancer cell xenografts, suggesting that MB cannot specifically target subcutaneous and orthotopic gastric tumors in xenograft models. Thus, it can be concluded that MB has no inherent specificity for gastric tumors but specificity for gastric tissues. Apparently, MB-positive and negative NIR imaging are meaningful in targeting gastric tissues and tumors. MB is expected to represent a helpful NIR agent to secure precise resection margins during the gastrectomy and resection of gastric tumors

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

Sparse tensor canonical correlation analysis for micro-expression recognition

A micro-expression is considered a fast facial movement that indicates genuine emotions and thus provides a cue for deception detection. Due to its promising applications in various fields, psychologists and computer scientists, particularly those focus on computer vision and pattern recognition, have shown interest and conducted research on this topic. However, micro-expression recognition accuracy is still low. To improve the accuracy of such recognition, in this study, micro-expression data and their corresponding Local Binary Pattern (LBP) (Ojala et al., 2002) [1] code data are fused by correlation analysis. Here, we propose Sparse Tensor Canonical Correlation Analysis (STCCA) for micro-expression characteristics. A sparse solution is obtained by the regularized low rank matrix approximation. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution. The experimental results also show that STCCA performs better than three-order Discriminant Tensor Subspace Analysis (DTSA3) with discriminant information, smaller projected dimensions and a larger training set sample size. The experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvectors are discarded, and those eigenvectors include brief and subtle motion information. (C) 2016 Elsevier B.V. All rights reserved

Plasmonic Coupling of Au Nanoclusters on a Flexible MXene/Graphene Oxide Fiber for Ultrasensitive SERS Sensing

High sensitivity, good signal repeatability, and facile fabrication of flexible surface enhanced Raman scattering (SERS) substrates are common pursuits of researchers for the detection of probe molecules in a complex environment. However, fragile adhesion between the noble-metal nanoparticles and substrate material, low selectivity, and complex fabrication process on a large scale limit SERS technology for wide-ranging applications. Herein, we propose a scalable and cost-effective strategy to a fabricate sensitive and mechanically stable flexible Ti3C2Tx MXene@graphene oxide/Au nanoclusters (MG/AuNCs) fiber SERS substrate from wet spinning and subsequent in situ reduction processes. The use of MG fiber provides good flexibility (114 MPa) and charge transfer enhancement (chemical mechanism, CM) for a SERS sensor and allows further in situ growth of AuNCs on its surface to build highly sensitive hot spots (electromagnetic mechanism, EM), promoting the durability and SERS performance of the substrate in complex environments. Therefore, the formed flexible MG/AuNCs-1 fiber exhibits a low detection limit of 1 × 10-11 M with a 2.01 × 109 enhancement factor (EFexp), signal repeatability (RSD = 9.80%), and time retention (remains 75% after 90 days of storage) for R6G molecules. Furthermore, the l-cysteine-modified MG/AuNCs-1 fiber realized the trace and selective detection of trinitrotoluene (TNT) molecules (0.1 μM) via Meisenheimer complex formation, even by sampling the TNT molecules at a fingerprint or sample bag. These findings fill the gap in the large-scale fabrication of high-performance 2D materials/precious-metal particle composite SERS substrates, with the expectation of pushing flexible SERS sensors toward wider applications

The Methylation Patterns and Transcriptional Responses to Chilling Stress at the Seedling Stage in Rice

Chilling stress is considered the major abiotic stress affecting the growth, development, and yield of rice. To understand the transcriptomic responses and methylation regulation of rice in response to chilling stress, we analyzed a cold-tolerant variety of rice (Oryza sativa L. cv. P427). The physiological properties, transcriptome, and methylation of cold-tolerant P427 seedlings under low-temperature stress (2–3 °C) were investigated. We found that P427 exhibited enhanced tolerance to low temperature, likely via increasing antioxidant enzyme activity and promoting the accumulation of abscisic acid (ABA). The Methylated DNA Immunoprecipitation Sequencing (MeDIP-seq) data showed that the number of methylation-altered genes was highest in P427 (5496) and slightly lower in Nipponbare (Nip) and 9311 (4528 and 3341, respectively), and only 2.7% (292) of methylation genes were detected as common differentially methylated genes (DMGs) related to cold tolerance in the three varieties. Transcriptome analyses revealed that 1654 genes had specifically altered expression in P427 under cold stress. These genes mainly belonged to transcription factor families, such as Myeloblastosis (MYB), APETALA2/ethylene-responsive element binding proteins (AP2-EREBP), NAM-ATAF-CUC (NAC) and WRKY. Fifty-one genes showed simultaneous methylation and expression level changes. Quantitative RT-PCR (qRT-PCR) results showed that genes involved in the ICE (inducer of CBF expression)-CBF (C-repeat binding factor)—COR (cold-regulated) pathway were highly expressed under cold stress, including the WRKY genes. The homologous gene Os03g0610900 of the open stomatal 1 (OST1) in rice was obtained by evolutionary tree analysis. Methylation in Os03g0610900 gene promoter region decreased, and the expression level of Os03g0610900 increased, suggesting that cold stress may lead to demethylation and increased gene expression of Os03g0610900. The ICE-CBF-COR pathway plays a vital role in the cold tolerance of the rice cultivar P427. Overall, this study demonstrates the differences in methylation and gene expression levels of P427 in response to low-temperature stress, providing a foundation for further investigations of the relationship between environmental stress, DNA methylation, and gene expression in rice