Association of Polymorphisms of the Matrix Metalloproteinase 9 Gene with Ischaemic Stroke in a Southern Chinese Population

Background/Aims: Matrix metalloproteinase 9 (MMP9), a potent endopeptidase degrading extracellular matrix, plays a pivotal role in the pathogenesis of ischaemic stroke (IS). The present study was undertaken to determine the association of MMP9 gene polymorphisms and the risk of IS in a southern Chinese population. Methods: A cohort of 1274 patients and 1258 age-matched healthy controls were genotyped to detect the four MMP9 polymorphisms (rs17156, rs3787268, rs3918241 and rs3918242) using SNaPshot. Results: Our study demonstrated a significant difference in the genotype and allele frequencies of the MMP9 rs3918242 polymorphism between the IS patients and the controls (P = 0.012 for the genotype and P = 0.0092 for the allele). Stratification by smoking status showed statistically significant differences in the frequency and allele of the rs3918242 polymorphism between IS patients and the controls (P = 0.0052 for the genotype and P = 0.0019 for the allele). Further stratification by IS subtypes revealed that the presence of the T allele of the MMP9 rs3918242 polymorphism confers a higher risk of the large artery atherosclerosis subtype of IS (P = 0.017). Moreover, IS patients with the rs3918242 T allele of MMP9 presented with increased serum MMP9 production, and this increase was more significant in smokers with IS (P = 0.022). Patients carrying the variant T allele of the MMP9 rs3918242 polymorphism exhibited significantly higher infarct volumes than those with the major CC genotype (P = 0.036). Conclusion: Our study provides preliminary evidence that the MMP9 rs3918242 polymorphism is linked to a higher risk of IS, confirming the role of MMP9 in the pathophysiology of IS, with potentially important therapeutic implications

A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery

Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual abnormal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications

Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD)

Genome-wide analysis of the switchgrass YABBY family and functional characterization of PvYABBY14 in response to ABA and GA stress in Arabidopsis

Abstract Background The small YABBY plant-specific transcription factor has a prominent role in regulating plant growth progress and responding to abiotic stress. Results Here, a total of 16 PvYABBYs from switchgrass (Panicum virgatum L.) were identified and classified into four distinct subgroups. Proteins within the same subgroup exhibited similar conserved motifs and gene structures. Synteny analyses indicated that segmental duplication contributed to the expansion of the YABBY gene family in switchgrass and that complex duplication events occurred in rice, maize, soybean, and sorghum. Promoter regions of PvYABBY genes contained numerous cis-elements related to stress responsiveness and plant hormones. Expression profile analysis indicated higher expression levels of many PvYABBY genes during inflorescence development and seed maturation, with lower expression levels during root growth. Real-time quantitative PCR analysis demonstrated the sensitivity of multiple YABBY genes to PEG, NaCl, ABA, and GA treatments. The overexpression of PvYABBY14 in Arabidopsis resulted in increased root length after treatment with GA and ABA compared to wild-type plants. Conclusions Taken together, our study provides the first genome-wide overview of the YABBY transcription factor family, laying the groundwork for understanding the molecular basis and regulatory mechanisms of PvYABBY14 in response to ABA and GA responses in switchgrass

Facile Method to Enhance the Adhesion of TiO₂ Nanotube Arrays to Ti Substrate

The weak adhesion of anodic TiO₂ nanotube arrays (TNTAs) to the underlying Ti substrate compromises many promising applications. In this work, a compact oxide layer between TNTAs and Ti substrate is introduced by employing an additional anodization in a fluoride-free electrolyte. The additional anodization results in an about 200 nm thick compact layer near the nanotube bottoms. Scratch test demonstrates that the critical load of TNTAs with the compact oxide layer is a more than threefold increase in comparison with those without the compact layer. Moreover, this facile method can also improve the photoactivity and supercapacitor performances of TNTAs markedly