•Flower-like In2O3 microspheres with high specific surface area was prepared.•The specific surface area is as high as 77.38 m2/g.•The unmodified pure In2O3 material can detect 5 ppb of isoprene at ...190 °C.•The sensor is expected to be used for rapid screening of chronic liver disease.
Isoprene is a typical biomarker for advanced fibrosis and can be used to screen and grade chronic liver disease (CLD). To detect trace isoprene, the high specific surface area (77.38 m2/g), porous flower-like In2O3 hierarchical microsphere material was prepared by simple hydrothermal method. In addition, the sensor based on synthetic flower-like In2O3 microspheres was prepared and gas sensing properties were investigated. The results showed that flower-like In2O3 nanomaterials had the highest response to isoprene at 190 °C. The response time was 53 s and repeatability was good. The relatively low operating temperature (190 °C) could extend the working life and also facilitate the portable application of the sensor. Meanwhile, the sensor exhibited selectivity over other typical biomarkers (ammonia, ethanol, hydrogen, and carbon monoxide). The unmodified pure flower-like In2O3 material could detect 5 ppb of isoprene at 190 °C and the logarithm of the response had good linear relationship with the logarithm of the concentration. Thus, this flower-like In2O3 material was promising to be developed into the portable breath isoprene detector that could be integrated into a micro system for noninvasive rapid screening and grading of CLD.
Isoprene is a typical biomarker for nonalcoholic fatty liver disease (NAFLD) and can be used for early screening in breath diagnosis. In this study, a simple hydrothermal synthesis of flower-like ...Cr2O3-doped In2O3 nanorods clusters materials for ultra-low isoprene detection was reported. This special flower-like nanorods clusters structure has the large specific surface area and the Cr2O3 doping improves in the distribution of different oxygen components, crystallite size, and carrier concentration, which synthetically contributes to the enhanced isoprene sensing performance. The results show that among all the sensors, the 1 wt% Cr2O3-doped In2O3 sensor exhibits reliable ppb-level detection ability with a concentration less than 5 ppb. Meanwhile, the sensor shows good long-term stability for 4 weeks and considerable selectivity over other common breathing gases (benzene, acetone, octane, pentane, ethanol, ammonia, and nitrogen dioxide). Furthermore, the sensor has good humidity resistance under different ambient humidity (0–92% RH). Thus, we believe the sensor based on the flower-like Cr2O3-doped In2O3 nanorods clusters has a potential application value for the detection of trace isoprene in breath analysis.
The flower-like Cr2O3-doped In2O3 nanorods clusters materials for ultra-low isoprene detection are synthesized via a facile hydrothermal method. Display omitted
Two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with ...one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H–13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H–13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.
RobOT Wang, Jingyi; Chen, Jialuo; Sun, Youcheng ...
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE),
05/2021
Conference Proceeding
Odprti dostop
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning ...testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.
Two-dimensional (2D)
H-
C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with ...one-dimensional (1D)
H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D
H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D
H-
C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ
) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ
for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D
H-
C HSQC spectra of 87 real extracts of 29 samples of five
species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.
•Some nutrients are able to induce the expression of endogenous defensins and cathelicidins.•The MAPK, NF-κB and HDAC signaling pathways play vital roles in the induction of defensin and cathelicidin ...expression.•Defensin and cathelicidin-inducing nutrients have potential applications in disease control and prevention.
Host defense peptides (HDPs) are crucial components of the body's first line of defense that protect organisms from infections and mediate immune responses. Defensins and cathelicidins are the two most important families of HDPs in mammals. In this review, we summarize the nutrients that are involved in inducible expression of endogenous defensins and cathelicidins. In addition, the mitogen-activated protein kinases (MAPK), nuclear factor kappa B (NF-κB) and histone deacetylase (HDAC) signaling pathways that play vital roles in the induction of defensin and cathelicidin expression are highlighted. Endogenous defensins and cathelicidins induced by nutrients may be potential alternatives to antibiotic treatments against infection and diseases. This review mainly focuses on the inducible expression and regulatory mechanisms of defensins and cathelicidins in multiple species by different nutrients and the potential applications of defensin- and cathelicidin-inducing nutrients.
•CNT-TFTs as sensors have been fabricated for gas detection at low ppm concentrations and the sensor responses have been obtained with good sensitivity and stability.•A convolution model has been ...developed, which can well capture the different sensing phases during each cycle of gas exposure using the time constants.•The proposed convolution model can be used for gas identification because of the uniqueness of time constants with respect to different gas types.•The sensing mechanism has been illustrated based on the interaction between gas molecules and different types of dwelling spots of a TFT.
Single-walled carbon nanotube (CNT) based gas sensors have enormous potential in pollution monitoring in low concentration level because of its high sensitivity, fast response, and physical/chemical stability. However, the lack of selectivity has been a major drawback for its wide range employment. In this work, we fabricate thin film transistors (TFTs) using randomly distributed CNTs and investigate them for ammonia and nitrogen dioxide detection in air at low ppm concentrations. A sensing mechanism is proposed based on the interaction between gas molecules and different types of dwelling spots inside the channel area of a TFT. We present double exponential-convolution model to decipher sensor response as well as to explore its application in gas identification. In this context, the consistency in time constants is recognized, which is independent of gas concentration. More importantly, the time constants vary with respect to different gas types and TFTs. The uniqueness of time constants can work as identity verification for different sensing gases, which demonstrates that the sensor response is a distinctive behavior determined by the unique channel structure of each TFT. This work provides us a general strategy for gas identification in ppm level and a practical path to realize the advantages of CNT gas sensors in air quality detection as well as the industrial emission control.
We presented a strategy utilizing 2D NMR‐based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of ...TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti‐non‐small cell lung cancer (anti‐NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti‐NSCLC activity. Cell proliferation assay was used to assess the anti‐NSCLC activity of four species of Scutellaria. 1H−13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS‐DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti‐NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti‐NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti‐NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR‐based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.
Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that ...is, given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Quality-oriented Testing). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties.
•DHM effectively restrained the cytotoxicity of DON in IPEC-J2 cells.•DHM relieves cell viability reduction, inflammatory response and oxidative stress reaction induced by DON.•DHM may weaken the ...toxic effect of DON by stabilizing glutamate metabolism, arachidonic metabolism and histidine metabolism.
In this study, we investigated the cytoprotective effects of dihydromyricetin (DHM) against deoxynivalenol (DON)-induced toxicity and accompanied metabolic pathway changes in porcine jejunum epithelial cells (IPEC-J2). The cells were incubated in 250 ng/ml DON cotreated with 40 µM DHM, followed by toxicity analysis, oxidative stress reaction analysis, inflammatory response analysis and metabolomic analysis. The results showed that DHM significantly increased the cell viability (P < 0.01), the intracellular GSH level (P < 0.01) and decreased the intracellular ROS level (P < 0.01), the secretion of TNF-α, IL-8 (P < 0.01) and the apoptotic cell percentages (P < 0.01) in IPEC-J2 cells compared to that in the DON group. Metabolomic analysis revealed that DHM recovered the disorder of metabolic pathways such as glutamate metabolism, arachidonic metabolism and histidine metabolism caused by DON. In summary, DHM alleviated cell injury induced by DON and it is possibly through its antioxidant activity, anti-inflammatory activity or ability to regulate metabolic pathways.
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