Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization.

Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks' ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.

A road adhesion coefficient-tire cornering stiffness normalization method combining a fractional-order multi-variable gray model with a LSTM network and vehicle direct yaw-moment robust control.

A normalization method of road adhesion coefficient and tire cornering stiffness is proposed to provide the significant information for vehicle direct yaw-moment control (DYC) system design. This method is carried out based on a fractional-order multi-variable gray model (FOMVGM) and a long short-term memory (LSTM) network. A FOMVGM is used to generate training data and testing data for LSTM network, and LSTM network is employed to predict tire cornering stiffness with road adhesion coefficient. In addition to that, tire cornering stiffness represented by road adhesion coefficient can be used to built vehicle lateral dynamic model and participate in DYC robust controller design. Simulations under different driving cycles are carried out to demonstrate the feasibility and effectiveness of the proposed normalization method of road adhesion coefficient and tire cornering stiffness and vehicle DYC robust control system, respectively.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects.

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

Normalization of metabolic data to total thymine content and its application to determination of 2-hydroxyglutarate.

Our first objective was to develop an approach useful for reliable normalization of 2-hydroxyglutarate (2-HG) intracellular levels. The second objective was to use our data normalization strategy to verify previously published report on the higher d-2-HG level in tumors of colorectal cancer (CRC) patients than in normal colon fragments. We examined various methods of 2-HG level normalization in cell/tissue extracts (number of cells, mass of tissue, total protein). In order to solve the problems with reliable normalization of the 2-HG levels in colon fragments, we proposed a strategy based on relating the concentrations of 2-HG isomers to total thymine concentrations measured by ultra-performance liquid chromatography (UPLC) with UV detection in acid hydrolysates of the cell/tissue extracts. We used a common method of derivatization with diacetyl-l-tartaric anhydride (DATAN) to separate l- and d-2-HG enantiomers. DATAN-derivatized 2-HG was quantitated by UPLC with tandem mass spectrometry (MS/MS) in the selected reaction monitoring (SRM) mode. We observed a linear dependence of the total amount of thymine released from lymphocytes, HCT 116, K562, and PC-3 by acid hydrolysis on their number of cells. Our results showed a significantly higher level of l- and d-2-HG in cancer-free colon than in tumor.

Application of a 2D-QSAR with a sine normalization method for the biodegradation of fluoroquinolones to poison cyanobacteria.

Cyanobacteria are photosynthetic autotrophic aquatic prokaryotes. One of the methods for controlling cyanobacterial blooms is to destroy the phycobiliproteins required for photosynthesis. In this study, to improve the biodegradation of the fluoroquinolones through inhibit cyanobacteria, the molecular docking scores of 32 fluoroquinolones (FQs) with four categories of phycobiliproteins from cyanobacteria were calculated after sine normalization to characterize the binding ability between them. A two-dimensional quantitative structure-activity relationship (2D-QSAR) model was constructed based on the comprehensive scores. Danofloxacin (DAN) with the highest comprehensive score was chosen for molecular modification. When docking with four categories of phycobiliproteins from cyanobacteria, the docking values of DAN-11 and DAN-16 were increased up to 35.75%. Moreover, their functional characteristics and environmentally friendly predictive values were improved. When the DAN-11 and DAN-16 molecules docked with the other cyanobacterial phycobiliproteins, indicating that the designed DAN derivatives had general applicability to poison cyanobacteria, the weak interaction forces might increase the binding ability between the DAN derivatives and the receptor phycobiliprotein compared with the target molecule.

Theoretical Design of Biodegradable Phthalic Acid Ester Derivatives in Marine and Freshwater Environments.

The biodegradability of phtalic acid esters in marine and freshwater environments was characterized by their binding free energy with corresponding degrading enzymes. According to comprehensive biodegradation effects weights, the binding free energy values were converted into dimensionless efficacy coefficient using ratio normalization method. Then, considering comprehensive dual biodegradation effects value and the structural parameters of PAEs in both marine and freshwater environments, a 3D-QSAR pharmacophore model was constructed, five PAE derivatives (DBP-COOH, DBP-CHO, DBP-OH, DINP-NH2, and DINP-NO2) were screened out based on their environmental friendliness, functionality and stability. The prediction of biodegradation effects on five PAE derivatives by biodegradation models in marine and freshwater environment increased by 15.90 %, 15.84 %, 27.21 %, 12.33 %, and 8.32 %, and 21.57 %, 15.21 %, 20.99 %, 15.10 %, and 9.74 %, respectively. By simulating the photodegradation path of the PAE derivative molecular, it was found that DBP-OH can generate .OH and provides free radicals for the photodegradation of microplastics in the environment.

Enhanced Biodegradation/Photodegradation of Organophosphorus Fire Retardant Using an Integrated Method of Modified Pharmacophore Model with Molecular Dynamics and Polarizable Continuum Model.

A comprehensive 3D-quantitative structure-activity relationship (QSAR) pharmacophore model was constructed using the values of comprehensive biodegradation/photodegradation effects of 17 organophosphorus flame retardants (OPFRs) evaluated by a normalization method to modify OPFRs with high biodegradation/photodegradation, taking tris(chloro-isopropyl) phosphate (TCPP), tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) phosphate (TCIPP)-which occur frequently in the environment, and are the most difficult to degrade as target molecules. OPFR-derivative molecules TCPP-OH shows the highest improvement in biodegradation and photodegradation (55.48% and 46.37%, respectively). On simulating the biodegradation path and photodegradation path, it is found that the energy barrier of TCPP-OH for phosphate bond cleavage is reduced by 15.73% and 52.52% compared to TCPP after modification, respectively. Finally, in order to further significantly improve its biodegradability and photodegradation, the efficiency enhancement in the biodegradation and photodegradation of TCPP-OH are analyzed under the simulated environment by molecular dynamics and polarizable continuum model, respectively. The results of molecular dynamics show that the biodegradation efficiency of the TCPP-OH increased by 75.52% compared to TCPP. The UV spectral transition energy (4.07 eV) of TCPP-OH under the influence of hydrogen peroxide solvation effect is 44.23% lower than the actual transition energy (7.29 eV) of TCPP.

A Light-Weight Practical Framework for Feces Detection and Trait Recognition.

Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

Optimal inter-batch normalization method for GC/MS/MS-based targeted metabolomics with special attention to centrifugal concentration.

This study investigated the optimal inter-batch normalization method for gas chromatography/tandem mass spectrometry (GC/MS/MS)-based targeted metabolome analysis of rodent blood samples. The effect of centrifugal concentration on inter-batch variation was also investigated. Six serum samples prepared from a mouse and 2 quality control (QC) samples from pooled mouse serum were assigned to each batch, and the 3 batches were analyzed by GC/MS/MS at different days. The following inter-batch normalization methods were applied to metabolome data: QC-based methods with quadratic (QUAD)- or cubic spline (CS)-fitting, total signal intensity (TI)-based method, median signal intensity (MI)-based method, and isotope labeled internal standard (IS)-based method. We revealed that centrifugal concentration was a critical factor to cause inter-batch variation. Unexpectedly, neither the QC-based normalization methods nor the IS-based method was able to normalize inter-batch variation, though MI- or TI-based normalization methods were effective in normalizing inter-batch variation. For further validation, 6 disease model rat and 6 control rat plasma were evenly divided into 3 batches, and analyzed as different batches. Same as the results above, MI- or TI-based methods were able to normalize inter-batch variation. In particular, the data normalized by TI-based method showed similar metabolic profiles obtained from their intra-batch analysis. In conclusion, the TI-based normalization method is the most effective to normalize inter-batch variation for GC/MS/MS-based metabolome analysis. Graphical abstract.

Toxicity of Diclofenac: Cadmium Binary Mixtures to Algae Desmodesmus subspicatus Using Normalization Method.

Algal test using chlorococcal algae Desmodesmus subspicatus was used to determine single acute toxicity of either diclofenac or cadmium and to assess acute toxicity of their binary mixtures. The test confirmed significant acute toxicity of both diclofenac and cadmium; diclofenac with acute toxicity ErC50 60.44 ± 0.20 mg/L and cadmium with acute toxicity ErC50 2.14 ± 0.02 mg/L. This study of acute toxicity of binary cadmium-diclofenac mixtures confirmed their negative effects on aquatic producers and it also proved influence of the above substances on acute toxicity of their mixtures. Normalization method was applied to predict acute toxicity of binary mixtures composed of chemicals with significantly different acute toxicities. Normalization method used molar ratio (R) of chemicals in binary mixtures as their composition descriptor.

Optimization of Water Extraction Technology of Feng-Moutan Cortex by Orthogonal Test-Multiple-index Normalization Method / 中国药房

OBJECTIVE: To optimize water extraction technology of Feng-Moutan Cortex. METHODS: L9 (34) orthogonal design was used to optimize soaking time, solid-liquid ratio, extraction time and extraction times in water extraction technology of Feng-Moutan Cortex using comprehensive scores for the peak area percentage and extract yield of 8 ingredients in Feng-Moutan Cortex as gallic acid, paeoniflorin, catechin, paeoniflorin, benzoic acid, benzoyloxypaeoniflorin, benzoylpaeoniflorin and paeonol after normalization as indexes. Verification test was conducted. RESULTS: The optimal water extraction technology was that 20-fold water, soaking for 1 h, extracting twice, 2 h each time. Results of verification test showed that comprehensive scores of 3 times of tests were 65. 98, 68. 85 and 69. 25, respectively. RSDs of each index were lower than 5％ (n=3). CONCLUSIONS: Established comprehensive scoring method can reflect the relative contents of effective components in Feng-Moutan Cortex comprehen- sively, so that optimized extraction technology is reasonable and feasible.

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method.

INTRODUCTION: Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number. OBJECTIVES: We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles. METHODS: We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile. RESULTS: We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82-97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells. CONCLUSION: We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

Comparison of sympathetic nerve activity normalization procedures in conscious rabbits.

One of the main constraints associated with recording sympathetic nerve activity (SNA) in both humans and experimental animals is that microvolt values reflect characteristics of the recording conditions and limit comparisons between different experimental groups. The nasopharyngeal response has been validated for normalizing renal SNA (RSNA) in conscious rabbits, and in humans muscle SNA is normalized to the maximum burst in the resting period. We compared these two methods of normalization to determine whether either could detect elevated RSNA in hypertensive rabbits compared with normotensive controls. We also tested whether either method eliminated differences based only on different recording conditions by separating RSNA of control (sham) rabbits into two groups with low or high microvolts. Hypertension was induced by 5 wk of renal clipping (2K1C), 3 wk of high-fat diet (HFD), or 3 mo infusion of a low dose of angiotensin (ANG II). Normalization to the nasopharyngeal response revealed RSNA that was 88, 51, and 34% greater in 2K1C, HFD, and ANG II rabbits, respectively, than shams (P < 0.05), but normalization to the maximum burst showed no differences. The RSNA baroreflex followed a similar pattern whether RSNA was expressed in microvolts or normalized. Both methods abolished the difference between low and high microvolt RSNA. These results suggest that maximum burst amplitude is a useful technique for minimizing differences between recording conditions but is unable to detect real differences between groups. We conclude that the nasopharyngeal reflex is the superior method for normalizing sympathetic recordings in conscious rabbits.

HPLC determination of terbutaline sulfate impurity I / 中国药学杂志

OBJECTIVE: To establish a qualitative and quantitative HPLC method for the determination of impurity Iin tebutaline sulfate. METHODS: The Kromasil C18 column(4.6 mm×150 mm,5 μm) was used as the analysis column;buffer solution [dissolving 4.23 g of sodium hexanesulfonate in 770 mL of 0.050 mol·L-1 ammonium formate solution (pH 3)]-methanol (77:23) was used as the mobile phase. The flow rate was 1.0 mL·min-1, the column temperature was maitained at 30℃, the detection wavelength was set at 276 nm,and the injectiong volume was 20 μL. Area normalization method with correction factor was used for the quantitative analysis of the impurity I. RESULTS: Under the separation condition, the impurity I was completely separated from the principal components. The calibration curve showed good linearity in the concentration range of 0.10-579 μg·mL-1(r=1.000 0). The correction factor was 3.6. CONCLUSION: The area normalization method with correction factor developed in the paper can be used for the qualitative and quantitative analysis of the impurity Iin terbutaline sulfate, which can not only solve the problem of the availability of impurity reference standards, but also reflect the actual contents of impurity. The method provides an efficient and convenient method for quality control of terbutaline sulfate.

Making air quality indices comparable--assessment of 10 years of air pollutant levels in western Europe.

To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality.

In vivo metabolic pathway of liquiritin in rats / 中草药

Objective: To study the in vivo metabolic pathways of liquiritin (LQ) in rats. Methods: An HPLC-QTRAP-MS method was established and applied to identify the metabolites of LQ in bile, urine, feces, and plasma after ig administration of LQ (300 mg/kg) to rats. Results: A total of nine metabolites were found in rats. The major metabolic pathway of LQ was deglucosidation to liquiritigenin (LG) and dehydration, glucuronidation, and sulfation of LG. Conclusion: LQ undergoes extensive phases I and II metabolism in rats. The major metabolites of LQ are LG and its glucuronides and sulfates.