No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Detection of 5-HMF in apple juice with artificial sensing systems

5-HMF (5-hydroxymethyl-furfural) is a product of thermal treatment and is increasingly considered a food contaminant. Here, different concentrations of 5-HMF were measured in apple juice to evaluate the performance of the electronic nose (EN) and electronic tongue (ET) as rapid detection techniques for 5-HMF when coupled with chemometric analysis. Principal component analysis (PCA) and linear discriminant analysis (LDA) evaluated the discrimination capacity of EN and ET for 5-HMF. Loading analysis examined the discrimination contribution of the EN sensors. Partial least square (PLS) regression analysis established a quantitative prediction model for different concentrations of 5-HMF based on EN and ET data set. The optimal models had a coefficient of determination (R²) of 0.926 and a root-mean-square error of prediction (RMSEP) of 0.4168 in EN; there were R² of 0.914 and RMSEP of 0.5836 in ET. These results demonstrate that EN and ET coupled with chemometric analysis are two promising approaches for the rapid and online detection of 5-HMF in apple juice.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

食醋总酸近红外快速检测方法的研究

利用傅立叶变换近红外光谱技术结合偏最小二乘法（PLS）对食醋的总酸进行快速检测分析。随机采集106个不同酸度食醋的实验数据、扫描近红外光谱图，选择最优的光谱预处理方法优化、建立模型。利用模型对15个未参与建模的食醋样品总酸的含量进行预测，外部验证模型的准确性。结果表明，食醋总酸实测值与对应采集的近红外漫反射光谱相关联建立的快速检测模型，其近红外食醋总酸模型的交叉验证决定系数（R2）为0.972 3，交叉验证均方差（RMSECV）为0.062 1。经外部验证后，该模型食醋总酸预测值和实测值的绝对偏差平均值为0.035，最大相对误差为1.383%，两者间相关性系数为0.995。该方法可以快速、高效、简便地用于食醋总酸的快速检测。     

The Impact of the ‘Mis-Peptidome’ on HLA Class I-Mediated Diseases: Contribution of ERAP1 and ERAP2 and Effects on the Immune Response

The strong association with the Major Histocompatibility Complex (MHC) class I genes represents a shared trait for a group of autoimmune/autoinflammatory disorders having in common immunopathogenetic basis as well as clinical features. Accordingly, the main risk factors for Ankylosing Spondylitis (AS), prototype of the Spondyloarthropathies (SpA), the Behçet’s disease (BD), the Psoriasis (Ps) and the Birdshot Chorioretinopathy (BSCR) are HLA-B*27, HLA-B*51, HLA-C*06:02 and HLA-A*29:02, respectively. Despite the strength of the association, the HLA pathogenetic role in these diseases is far from being thoroughly understood. Furthermore, Genome-Wide Association Studies (GWAS) have highlighted other important susceptibility factors such as Endoplasmic Reticulum Aminopeptidase (ERAP) 1 and, less frequently, ERAP2 that refine the peptidome presented by HLA class I molecules to CD8⁺ T cells. Mass spectrometry analysis provided considerable knowledge of HLA-B*27, HLA-B*51, HLA-C*06:02 and HLA-A*29:02 immunopeptidome. However, the combined effect of several ERAP1 and ERAP2 allelic variants could generate an altered pool of peptides accounting for the “mis-immunopeptidome” that ranges from suboptimal to pathogenetic/harmful peptides able to induce non-canonical or autoreactive CD8⁺ T responses, activation of NK cells and/or garbling the classical functions of the HLA class I molecules. This review will focus on this class of epitopes as possible elicitors of atypical/harmful immune responses which can contribute to the pathogenesis of chronic inflammatory diseases.     

基于深度卷积神经网络的视觉里程计研究

视觉里程计利用视频信息来估计相机运动的位姿参数，实现对智能体的定位。传统视觉里程计方法需要特征提取、特征匹配/跟踪、外点剔除、运动估计、优化等流程，解算非常复杂，因此，提出了基于卷积神经网络的方法来实现端对端的单目视觉里程计。借助卷积神经网络对彩色图片自动学习提取图像帧间变化的全局特征，将用于分类的卷积神经网络转化为帧间时序特征网络，通过三层全连接层输出相机的帧间相对位姿参数。在KITTI数据集上的实验结果表明，提出的Deep-CNN-VO模型可以较准确地估计车辆的运动轨迹，证明了方法的可行性。在简化了复杂模型的基础上，与传统的视觉里程计系统相比，该模型的精度也有所提高。     

乙酰丙酮-目视判读法快速测定白酒中甲醇的含量

目的建立了乙酰丙酮-目视判读法快速测定白酒中甲醇的方法。方法采用乙酰丙酮法,白酒中甲醇经高锰酸钾氧化,草酸还原过量高猛酸钾后,在沸水浴中与乙酰丙酮发生反应,并对分析条件进行了探讨优化,对酒精度影响和干扰因素进行了深入研究并提出了解决办法。结果对优化后的分析条件进行了多个实验室间验证。本快速检验方法灵敏度为100%,特异性为≥94%,假阴性为0%,假阳性率为≤6.0%,相对准确度≥98.5%,满足要求。结论该方法灵敏、准确、快速,适用于白酒中甲醇的含量的现场快速检测。