Similar Fault Isolation of Discrete-Time Nonlinear Uncertain Systems: An Adaptive Threshold Based Approach

In this paper, a new concept of “similar fault” is introduced to the field of fault isolation (FI) of discrete-time nonlinear uncertain systems, which defines a new and important class of faults that have small mutual differences in fault magnitude and fault-induced system trajectories. Effective isolation of such similar faults is rather challenging as their small mutual differences could be easily concealed by other system uncertainties (e.g., modeling uncertainty/disturbances). To this end, a novel similar fault isolation (sFI) scheme is proposed based on an adaptive threshold mechanism. Specifically, an adaptive dynamics learning approach based on the deterministic learning theory is first introduced to locally accurately learn/identify the uncertain system dynamics under each faulty mode using radial basis function neural networks (RBF NNs). Based on this, a bank of sFI estimators are then developed using a novel mechanism of absolute measurement of fault dynamics differences. The resulting residual signals can be used to effectively capture the small mutual differences of similar faults and distinguish them from other system uncertainties. Finally, an adaptive threshold is designed for real-time sFI decision making. One important feature of the proposed sFI scheme is that: it is capable of not only isolating similar faults that belong to a pre-defined fault set (used in the training/learning process), but also identifying new faults that do not match any pre-defined faults. Rigorous analysis on isolatability conditions and isolation time is conducted to characterize the performance of the proposed sFI scheme. Simulation results on a practical application example of a single-link flexible joint robot arm are used to show the effectiveness and advantages of the proposed scheme over existing approaches

Effects of APOE Genotype on Brain Proteomic Network and Cell Type Changes in Alzheimer's Disease

Polymorphic alleles in the apolipoprotein E (APOE) gene are the main genetic determinants of late-onset Alzheimer's disease (AD) risk. Individuals carrying the APOE E4 allele are at increased risk to develop AD compared to those carrying the more common E3 allele, whereas those carrying the E2 allele are at decreased risk for developing AD. How ApoE isoforms influence risk for AD remains unclear. To help fill this gap in knowledge, we performed a comparative unbiased mass spectrometry-based proteomic analysis of post-mortem brain cortical tissues from pathologically-defined AD or control cases of different APOE genotypes. Control cases (n = 10) were homozygous for the common E3 allele, whereas AD cases (n = 24) were equally distributed among E2/3, E3/3, and E4/4 genotypes. We used differential protein expression and co-expression analytical approaches to assess how changes in the brain proteome are related to APOE genotype. We observed similar levels of amyloid-β, but reduced levels of neurofibrillary tau, in E2/3 brains compared to E3/3 and E4/4 AD brains. Weighted co-expression network analysis revealed 33 modules of co-expressed proteins, 12 of which were significantly different by APOE genotype in AD. The modules that were significantly different by APOE genotype were associated with synaptic transmission and inflammation, among other biological processes. Deconvolution and analysis of brain cell type changes revealed that the E2 allele suppressed homeostatic and disease-associated cell type changes in astrocytes, microglia, oligodendroglia, and endothelia. The E2 allele-specific effect on brain cell type changes was validated in a separate cohort of 130 brains. Our systems-level proteomic analyses of AD brain reveal alterations in the brain proteome and brain cell types associated with allelic variants in APOE, and suggest further areas for investigation into the upstream mechanisms that drive ApoE-associated risk for AD

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Advances in tumour markers for diagnosis of hepatocellular carcinoma

According to the data from the Chinese Centre for Disease Control and Prevention, the liver cancer ranks second among malignant tumour deaths in China, with a high mortality rate. Liver cancer is characterized by difficult of early diagnosis (about 50% of patients missed), high malignant level, strong heterogeneity, and rapid progression. Early diagnosis can help patients seize the best chance for treatment, reduce the damage to the body, improve the treatment effect, and prolong survival. The main pathological type of liver cancer is hepatocellular carcinoma(HCC). Commonly used clinical tumour markers for HCC include α-fetoprotein (AFP), protein induced by vitamin K deficiency or antagonist-Ⅱ (PIVKA-Ⅱ), a-L-fucosidase (AFU), etc., which are simple and efficient. However, due to the heterogeneity of liver cancer, the marker levels in some patients were not abnormal, and 52% of HCC patients with small tumours (<3 cm) were AFP-negative, which affected the diagnostic accuracy of HCC. Therefore, some novel tumour markers have been discovered, including circulating tumour cells (CTCs), circulating cell-free nucleic acids [including circulating cell-free DNA (cfDNA) and microRNAs (miRNAs)], and exosomes. It revealed that 90.81% of CTC positive HCC patients (including early disease patients) can detect very small HCC nodules after 3-5 months of follow-up, indicating a high correlation between CTC and HCC characteristics. Postoperative monitoring of CTC levels can predict HCC recurrence before clinical detection of recurrent nodules; cfDNA can serve as an effective tool for early diagnosis of HCC, and detecting mutations in ctDNA can guide targeted therapy; miRNA can serve as a biomarker for diagnosing diseases and monitoring disease progression and prognosis; The joint detection of AFP and lncRNAs panel (including three circulating exosome sources of long chain non coding RNAs: ENSG00000248932.1, ENST000000440688.1, ENST000000457302.2) showed higher sensitivity and specificity than the single detection of AFP (AUC: 0.910 and 0.408), which can predict the occurrence of HCC and dynamically monitor HCC metastasis.However, these new tumour markers still have some limitations such as high false-negative rate at low levels, and limitation in stability due to the lack of standardized pre-analytical variables and analytical variables. These tumour markers are still not recommended to be used independently for early screening, monitoring or large-scale clinical application of HCC, and can only be used as a supplement to traditional diagnostic methods. This article reviewed the research progress of tumour markers in the diagnosis of HCC in recent years, summarized the efficacy of traditional tumour markers (AFP, PIVKA-Ⅱ. and AFU, etc.), introduced the research progress and clinical application of new tumour markers (CTC, cfDNA, ctDNA, miRNA and exosomes, etc.), and looked forward to improving the accuracy of HCC diagnosis in the future

Correction: LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

[This corrects the article DOI: 10.1371/journal.pone.0163491.]

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines

<div><p>Background</p><p>The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions.</p><p>Method</p><p>In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine.</p><p>Results</p><p>We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers.</p><p>Conclusion</p><p>Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.</p></div

The summary of the overlap between the predicted enhancers and the FANTOM5 enhancers.

<p>The summary of the overlap between the predicted enhancers and the FANTOM5 enhancers.</p

MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data

Abstract Background DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed. Results We developed “MeDEStrand,” a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods “MEDIPS,” “BayMeth,” and “QSEA” on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs. Conclusions The MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git