Validation Study of a Novel Detection Kit for Rapid Detection and Quantification of Listeria Spp. in Food Samples

A single tube detection kit was designed as a rapid, easy-to use and reliable test to detect Listeria spp.. Various food samples (vegetables and raw catfish fillets) were used in order to validate the performance of the detection kit. L. grayi was detected in one ready-to-eat (RTE) vegetables with the detection kit while no Listeria spp. was detected using the modified FDA-BAM method. In addition, both the detection kit and modified FDA-BAM method indicated that twelve catfish fillets were Listeria positive. The detection kit had 100% sensitivity and specificity in less detection time (24 h) than the modified FDA-BAM method (60% specificity, \u3e72 h). There was no difference (P\u3c0.05) between the kit and the modified FDA-BAM method on MPN for Listeria spp

Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer

PKM2 Is Required to Activate Myeloid Dendritic Cells from Patients with Severe Aplastic Anemia

Severe aplastic anemia (SAA) is an autoimmune disease in which bone marrow failure is mediated by activated myeloid dendritic cells (mDCs) and T lymphocytes. Recent research has identified a strong immunomodulatory effect of pyruvate kinase M2 (PKM2) on dendritic cells in immune-mediated diseases. In this study, we aimed to explore the role of PKM2 in the activation of mDCs in SAA. We observed conspicuously higher levels of PKM2 in mDCs from SAA patients compared to normal controls at both the gene and protein levels. Concurrently, we unexpectedly discovered that after the mDC-specific downregulation of PKM2, mDCs from patients with SAA exhibited weakened phagocytic activity and significantly decreased and shortened dendrites relative to their counterparts from normal controls. The expression levels of the costimulatory molecules CD86 and CD80 were also reduced on mDCs. Our results also suggested that PKM2 knockdown in mDCs reduced the abilities of these cells to promote the activation of CD8+ T cells (CTLs), leading to the decreased secretion of cytotoxic factors by the latter cell type. These findings demonstrate that mDC activation requires an elevated intrinsic PKM2 level and that PKM2 improves the immune status of patients with SAA by enhancing the functions of mDCs and, consequently, CTLs