Characterization and application of Bacillus velezensis D6 co-producing α-amylase and protease.

BACKGROUND: Research on the co-production of multiple enzymes by Bacillus velezensis as a novel species is still a topic that needs to be studied. This study aimed to investigate the fermentation characteristics of B. velezensis D6 co-producing α-amylase and protease and to explore their enzymatic properties and applications in fermentation. RESULTS: The maximum co-production of α-amylase and protease reached 13.13 ± 0.72 and 2106.63 ± 64.42 U mL-1, respectively, under the optimal fermented conditions (nutrients: 20.0 g L-1 urea, 20.0 g L-1 glucose, 0.7 g L-1 MnCl2; incubation conditions: initial pH 7.0, temperature 41 °C, 8% inoculation size and 30% working volume). Moreover, the genetic co-expression of α-amylase and protease increased from 0 to 24 h and then decreased after 36 h at the transcriptional level, which coincided with the growth trend of B. velezensis D6. The optimal reaction temperature of α-amylase was 55-60 °C, while that of protease was 35-40 °C. The activities of α-amylase and protease were retained by over 80% after thermal treatment (90 °C, 1 h), which indicated that two enzymes co-produced by B. velezensis D6 demonstrated excellent thermal stability. Moreover, the two enzymes were stable over a wide pH range (pH 4.0-8.0 for α-amylase; pH 4.0-9.0 for protease). Finally, the degrees of hydrolysis of corn, rice, sorghum and soybeans by α-amylase from B. velezensis D6 reached 44.95 ± 2.95%, 57.16 ± 2.75%, 52.53 ± 4.01% and 20.53 ± 2.42%, respectively, suggesting an excellent hydrolysis effect on starchy raw materials. The hydrolysis degrees of mackerel heads and soybeans by protease were 43.93 ± 2.19% and 26.38 ± 1.72%, respectively, which suggested that the protease from B. velezensis D6 preferentially hydrolyzed animal-based protein. CONCLUSION: This is a systematic study on the co-production of α-amylase and protease by B. velezensis D6, which is crucial in widening the understanding of this species co-producing multi-enzymes and in exploring its potential application. © 2024 Society of Chemical Industry.

An effective feature extraction method based on GDS for atrial fibrillation detection.

Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection.

Microbiota stratification and succession of amylase-producing Bacillus in traditional Chinese Jiuqu (fermentation starters).

BACKGROUND: Jiuqu are vital saccharifying and fermenting agents for Chinese fermented foods. Natural ventilation during Jiuqu fermentation causes changes in temperature, oxygen and moisture content, resulting in mass and heat gradients from the outer to inner areas of Jiuqu blocks. In the present study, microbiota stratification in Jiuqu was investigated by single molecule real-time sequencing and culture isolation. The contributors of Bacillus to amylase activity of Jiuqu and the dynamics of their biomass during Jiuqu fermentation were also analyzed. RESULTS: The dominant orders, genera and species between the inner and outer layers of Huangjiu qu (HJQ) were similar, although they displayed greater variance in two layers of Baijiu qu (BJQ). Bacillus possessed the highest diversity (including 27 species) in Jiuqu. Bacillus licheniformis, Bacillus altitudinis, Bacillus subtilis, Bacillus amyloliquefaciens and Bacillus megaterium were most prevalent in HJQ, whereas B. licheniformis, B. amyloliquefaciens and Bacillus cereus were dominant in BJQ. Isolates of B. amyloliquefaciens, B. subtilis and B. cereus exhibited high activities of amylase and glucoamylase. Quantification of Bacillus members possessing genes of α-amylase revealed that B. cereus and B. licheniformis were the most dominant microbes to secret α-amylase in Jiuqu and their biomass were increasing during Jiuqu fermentation. CONCLUSION: The present study demonstrates the microbial distribution in different layers of Jiuqu and clarifies the Bacillus species processing the activity of α-amylase. These results will help industries control the quality of Jiuqu by rationally selecting starters and optimizing their microbiota. © 2020 Society of Chemical Industry.

Metagenomics reveals flavour metabolic network of cereal vinegar microbiota.

Multispecies microbial community formed through centuries of repeated batch acetic acid fermentation (AAF) is crucial for the flavour quality of traditional vinegar produced from cereals. However, the metabolism to generate and/or formulate the essential flavours by the multispecies microbial community is hardly understood. Here we used metagenomic approach to clarify in situ metabolic network of key microbes responsible for flavour synthesis of a typical cereal vinegar, Zhenjiang aromatic vinegar, produced by solid-state fermentation. First, we identified 3 organic acids, 7 amino acids, and 20 volatiles as dominant vinegar metabolites. Second, we revealed taxonomic and functional composition of the microbiota by metagenomic shotgun sequencing. A total of 86 201 predicted protein-coding genes from 35 phyla (951 genera) were involved in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of Metabolism (42.3%), Genetic Information Processing (28.3%), and Environmental Information Processing (10.1%). Furthermore, a metabolic network for substrate breakdown and dominant flavour formation in vinegar microbiota was constructed, and microbial distribution discrepancy in different metabolic pathways was charted. This study helps elucidating different metabolic roles of microbes during flavour formation in vinegar microbiota.

Batch-to-batch uniformity of bacterial community succession and flavor formation in the fermentation of Zhenjiang aromatic vinegar.

Solid-state fermentation of traditional Chinese vinegar is a mixed-culture refreshment process that proceeds for many centuries without spoilage. Here, we investigated bacterial community succession and flavor formation in three batches of Zhenjiang aromatic vinegar using pyrosequencing and metabolomics approaches. Temporal patterns of bacterial succession in the Pei (solid-state vinegar culture) showed no significant difference (P > 0.05) among three batches of fermentation. In all the batches investigated, the average number of community operational taxonomic units (OTUs) decreased dramatically from 119 ± 11 on day 1 to 48 ± 16 on day 3, and then maintained in the range of 61 ± 9 from day 5 to the end of fermentation. We confirmed that, within a batch of fermentation process, the patterns of bacterial diversity between the starter (took from the last batch of vinegar culture on day 7) and the Pei on day 7 were similar (90%). The relative abundance dynamics of two dominant members, Lactobacillus and Acetobacter, showed high correlation (coefficient as 0.90 and 0.98 respectively) among different batches. Furthermore, statistical analysis revealed dynamics of 16 main flavor metabolites were stable among different batches. The findings validate the batch-to-batch uniformity of bacterial community succession and flavor formation accounts for the quality of Zhenjiang aromatic vinegar. Based on our understanding, this is the first study helps to explain the rationality of age-old artistry from a scientific perspective.

Influence of 3-chloropropyl) triethoxysilane and pH on the properties of modified guar gum film.

Guar gum (GG) as a polymer biopolymer is widely used in the field of bio-based packaging. However, its poor mechanical properties, barrier properties and high viscosity greatly hinder its use as an effective packaging material. Therefore, this study introduced CPTES to improve the mechanical (16.58-27.39 MPa) and tensile properties (26.80 %-30.67 %). The FTIR and XRD results indicated a strong interaction between the biofilm fractions modified by CPTES, CPTES bound to the hydroxyl groups on GG and formed a dense polysiloxane network through adsorption and grafting. OM and AFM reflect a denser and flatter film structure on the surface of the G30 film, which has the best film formation. Based on this, the pH of the solution was further adjusted to reach an alkaline environment, disrupting the intermolecular binding through electrostatic repulsion. The rheological behavior indicates that the viscosity and viscoelasticity of film solution gradually decrease with the increase in pH. OM and AFM results show that the G30/8 film has the best compact properties, while the nonporous compact film structure further improves the mechanical, barrierand and thermodynamic properties of the film. Accordingly, the findings of this study had a certain value for regulating the low viscoelasticity of GG emulsion and enhancing the stability of film formation.

Investigation of bioactive compounds and their correlation with the antioxidant capacity in different functional vinegars.

There are complex and diverse substances in traditional vinegars, some of which have been identified as biologically active factors, but the variety of functional compounds is currently restricted. In this study, it was aimed to determine the bioactive compounds in 10 typical functional vinegars. The findings shown that total flavonoids (0.21-7.19 mg rutin equivalent/mL), total phenolics (0.36-3.20 mg gallic acid equivalent/mL), and antioxidant activities (DPPH: 3.17-47.63 mmol trolox equivalent/L, ABTS: 6.85-178.29 mmol trolox equivalent/L) varied among different functional vinegars. In addition, the concentrations of the polysaccharides (1.17-44.87 mg glucose equivalent/mL) and total saponins (0.67-12.46 mg oleanic acid equivalent/mL) were determined, which might play key role for the function of tested vinegars. A total of 8 organic acids, 7 polyphenol compounds and 124 volatile compounds were measured and tentatively identified. The protocatechuic acid (4.81-485.72 mg/L), chlorogenic acid (2.69-7.52 mg/L), and epicatechin (1.18-97.42 mg/L) were important polyphenol compounds in the functional vinegars. Redundancy analysis indicated that tartaric acid, oxalic acid and chlorogenic acid were significantly positively correlated with antioxidant capacity. Various physiologically active ingredients including cyclo (Pro-Leu), cyclo (Phe-Pro), cyclo (Phe-Val), cyclo (Pro-Val), 1-monopalmitin and 1-eicosanol were firstly detected in functional vinegars. Principle component analysis revealed that volatiles profile of bergamot Monascus aromatic vinegar and Hengshun honey vinegar exhibited distinctive differences from other eight vinegar samples. Moreover, the partial least squares regression analysis demonstrated that 11 volatile compounds were positively correlated with the antioxidant activity of vinegars, which suggested these compounds might be important functional substances in tested vinegars. This study explored several new functionally active compounds in different functional vinegars, which could widen the knowledge of bioactive factor in vinegars and provide new ideas for further development of functional vinegar beverages.

Adversarial Spatiotemporal Contrastive Learning for Electrocardiogram Signals.

Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a challenge for deep neural networks (DNNs). Contrastive learning is a promising method for unsupervised learning. However, it should improve its robustness to noise and learn the spatiotemporal and semantic representations of categories, just like cardiologists. This article proposes a patient-level adversarial spatiotemporal contrastive learning (ASTCL) framework, which includes ECG augmentations, an adversarial module, and a spatiotemporal contrastive module. Based on the ECG noise attributes, two distinct but effective ECG augmentations, ECG noise enhancement, and ECG noise denoising, are introduced. These methods are beneficial for ASTCL to enhance the robustness of the DNN to noise. This article proposes a self-supervised task to increase the antiperturbation ability. This task is represented as a game between the discriminator and encoder in the adversarial module, which pulls the extracted representations into the shared distribution between the positive pairs to discard the perturbation representations and learn the invariant representations. The spatiotemporal contrastive module combines spatiotemporal prediction and patient discrimination to learn the spatiotemporal and semantic representations of categories. To learn category representations effectively, this article only uses patient-level positive pairs and alternately uses the predictor and the stop-gradient to avoid model collapse. To verify the effectiveness of the proposed method, various groups of experiments are conducted on four ECG benchmark datasets and one clinical dataset compared with the state-of-the-art methods. Experimental results showed that the proposed method outperforms the state-of-the-art methods.

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis.

In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment.

PET and MRI image fusion based on a dense convolutional network with dual attention.

The fusion techniques of different modalities in medical images, e.g., Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), are increasingly significant in many clinical applications by integrating the complementary information from different medical images. In this paper, we propose a novel fusion model based on a dense convolutional network with dual attention (CSpA-DN) for PET and MRI images. In our framework, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is employed to generate the fused image from these features. Simultaneously, a dual-attention module is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. In the dual-attention module, a spatial attention block is leveraged to extract features of each point from encoder network by a weighted sum of feature information at all positions. Meanwhile, the interdependent correlation of all image features is aggregated via a module of channel attention. In addition, we design a specific loss function including image loss, structural loss, gradient loss and perception loss to preserve more structural and detail information and sharpen the edges of targets. Our approach facilitates the fused images to not only preserve abundant functional information from PET images but also retain rich detail structures of MRI images. Experimental results on publicly available datasets illustrate the superiorities of CSpA-DN model compared with state-of-the-art methods according to both qualitative observation and objective assessment.

Ultrasonic treatment of Dendrobium officinale polysaccharide enhances antioxidant and anti-inflammatory activity in a mouse D-galactose-induced aging model.

Utilization of the biological macromolecule Dendrobium officinale polysaccharide (DOP) as a functional ingredient is limited by its high intrinsic viscosity and molecular weight. The goal of the present study was to improve rheological properties of DOP by ultrasonic treatment. Such a treatment resulted in the degradation of DOP and consequent reduction of rheological properties. Among DOP samples treated with ultrasonication at low (L), medium (M), and high (H) power intensities (25, 50, 75 w/cm2), M-DOP displayed the highest reactive oxygen species (ROS) and reactive nitrogen species (RNS) radical scavenging activity in vitro. In a mouse D-galactose (D-Gal)-induced aging model, M-DOP significantly increased activities of antioxidant enzymes and reduced levels of pro-inflammatory cytokines in liver. Real-time polymerase chain reaction (RT-PCR) analysis indicated that M-DOP upregulated messenger RNA (mRNA) expression of anti-inflammatory/antioxidant proteins such as Nrf2 (nuclear factor erythroid 2-related factor), hemeoxygenase-1 (HO-1), and NAD(P)H:quinone oxidoreductase (NQO1) in liver. In summary, M-DOP displayed a strong radical scavenging activity in vitro, and ameliorated liver injury in the mouse aging model through the promotion of Nrf2/HO-1/NQO1 signaling pathway.

[Expression of c-kit and Cx43 in neonates with spontaneous gastric perforation].

OBJECTIVE: To study the clinical significance of interstitial cell of Cajal (ICC) in spontaneous neonatal gastric perforation by examining the expression of c-kit and Cx43 in neonates with this disorder. METHODS: The gastric specimens of 19 cases of neonatal gastric perforation from 2001 to 2010 and 8 cases of accidental death without digestive tract malformations (control) were collected. Immunohistochemical staining was employed to examine the expression of c-kit and Cx43 (immunomarkers of ICCs) in gastric tissues. RESULTS: The muscular layer of the stomach wall became thinner or deficient in the gastric perforation group. C-kit and Cx43 positive cells in gastric tissues decreased significantly in the gastric perforation group compared with those in the control group (P＜0.01). CONCLUSIONS: The development of spontaneous neonatal gastric perforation is associated with the decreased quantity of ICCs and damaged gap junction structure of the stomach wall.

TP-CNN: A Detection Method for atrial fibrillation based on transposed projection signals with compressed sensed ECG.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is the most prevalent arrhythmia, which increases the mortality of several complications. The use of wearable devices to detect atrial fibrillation is currently attracting a great deal of attention. Patients use wearable devices to continuously collect individual ECG signals and transmit them to the cloud for diagnosis. However, the ECG acquisition and transmission of wearable devices consumes a lot of energy. In order to solve this problem, some scholars have skipped the complex reconstruction process of compressed ECG signals and directly classified the compressed ECG signals, but the AF recognition rate is not high by this method. There is no explanation as to why the compressed ECG signals can be used for AF detection. METHODS: Firstly, a simple deterministic measurement matrix (SDMM) is used to perform random projection operation on the ECG signals to complete the compression. Then, we use the transpose of the SDMM to perform transpose projection operation on the compressed signals in the cloud to obtain the approximate signals. We verify the similarity between the approximate ECG signal and the original ECG signal to explain why the compressed ECG signals are effective in AF detection. Finally, the Transposed Projection - Convolutional Neural Network (TP-CNN) is used to effectively detect AF on the obtained approximate ECG signals. Our proposed method is validated in the MIT-BIH AFDB. RESULTS: The experimental results show that when compression ratios (CRs) are from 2 to 10, the average Pearson correlation coefficients between the approximate signals and the original signals are from 0.9867 to 0.8326, the average cosine similarities between the four frequency domain-based HRV features (including mean RR, RMSSD, SDNN and R density) are from 1.00 to 0.9958, from 1.00 to 0.9959, from 0.9978 to 0.8619 and from 0.9982 to 0.8707, respectively. Furthermore, when CR=10 (ECG was compressed to 1/10 of the original signal), the accuracy, specificity, f1 score and matthews correlation coefficient for AF detection of approximate signals were 99.32%, 99.43%, 99.14% and 98.57%, respectively. CONCLUSION: Our proposed method illustrates the approximate signals have significant characteristics of the original signals and they are valid to classify the approximate signals. Meanwhile, comparing with the state-of-the-art methods, TP-CNN exceeded the results of the method for compressed signals and were also competitive compared with the classification results of the original signals, and is a promising method for AF detection in wearable application scenarios.

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm.

Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.

Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning.

Remote ECG diagnosis has been widely used in the clinical ECG workflow. Especially for patients with pacemaker, in the limited information of patient's medical history, doctors need to determine whether the patient is wearing a pacemaker and also diagnose other abnormalities. An automatic detection pacing ECG method can help cardiologists reduce the workload and the rates of misdiagnosis. In this paper, we propose a novel autoencoder framework that can detect the pacing ECG from the remote ECG. First, we design a memory module in the traditional autoencoder. The memory module is to record and query the typical features of the training pacing ECG type. The framework does not directly feed features of the encoder into the decoder but uses the features to retrieve the most relevant items in the memory module. In the training process, the memory items are updated to represent the latent features of the input pacing ECG. In the detection process, the reconstruction data of the decoder is obtained by the fusion features in the memory module. Therefore, the reconstructed data of the decoder tends to be close to the pacing ECG. Meanwhile, we introduce an objective function based on the idea of metric learning. In the context of pacing ECG detection, comparing the error of objective function of the input data and reconstructed data can be used as an indicator of detection. According to the objective function, if the input data does not belong to pacing ECG, the objective function may get a large error. Furthermore, we introduce a new database named the pacing ECG database including 800 patients with a total of 8,000 heartbeats. Experimental results demonstrate that our method achieves an average F1-score of 0.918. To further validate the generalization of the proposed method, we also experiment on a widely used MIT-BIH arrhythmia database.

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia.

Arrhythmia is a cardiovascular disease that seriously affects human health. The identification and diagnosis of arrhythmia is an effective means of preventing most heart diseases. In this paper, a BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. Firstly, the discrete wavelet transform is used to denoise the ECG signal, based on which we performed heartbeat segmentation and preserved the timing relationship between heartbeats. Then, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. Finally, the tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. And the interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. This method divides the heartbeat into five categories (nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fused heartbeats (F), and unknown heartbeats (Q)) and is validated on the MIT-BIH arrhythmia database. The results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper not only improves the classification accuracy and obtains higher sensitivity and positive predictive value but also has higher interpretability.

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification.

Automatic classification of ECG is very important for early prevention and auxiliary diagnosis of cardiovascular disease patients. In recent years, many studies based on ECG have achieved good results, most of which are based on single-label problems; one record corresponds to one label. However, in actual clinical applications, an ECG record may contain multiple diseases at the same time. Therefore, it is very important to study the multilabel ECG classification. In this paper, a multiscale residual deep neural network CSA-MResNet model based on the channel spatial attention mechanism is proposed. Firstly, the residual network is integrated into a multiscale manner to obtain the characteristics of ECG data at different scales and then increase the channel spatial attention mechanism to better focus on more important channels and more important ECG data fragments. Finally, the model is used to classify multilabel in large databases. The experimental results on the multilabel CCDD show that the CSA-MResNet model has an average F1 score of 88.2% when the multilabel classification of 9 ECGs is performed. Compared with the benchmark model, the F1 score of CSA-MResNet in the multilabel ECG classification increased by up to 1.7%. And, in the model verification on another database HF-challenge, the final average F1 score is 85.8%. Compared with the state-of-the-art methods, CSA-MResNet can help cardiologists perform early-stage rapid screening of ECG and has a certain generalization performance, providing a feasible analysis method for multilabel ECG classification.

Baijiu vinasse as a new source of bioactive peptides with antioxidant and anti-inflammatory activity.

During production in Chinese baijiu fermentation process, huge amounts of the by-product vinasse are generated and generally utilized as low-value animal feed. We applied alkaline extraction in combination with ultrasonication to recover vinasse proteins, which were then hydrolyzed by complex protease Corolase PP for 8 h to obtain peptide fractions (VPH-1, -2, -3) displaying high DPPH radical scavenging activity. VPH-3 (<3 kDa) separated by ultrafiltration had EC50 values lower than those of VPH-1 and -2 for reactive oxygen species (ROS) and reactive nitrogen species (RNS) radicals, and significantly inhibited production of NO and pro-inflammatory cytokines in LPS-stimulated RAW264.7 macrophage cells. Active peptides and their amino acid sequences were identified by LC-MS/MS analysis, and five synthesized peptides (particularly KLPDHPKLPK and VDVPVKVPYS) displayed strong anti-inflammatory activity at concentration 0.25 mg/mL. These findings will be useful in future commercial development of baijiu vinasse, including application as a new source of bioactive peptides.

An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.

Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. The analysis results are output in the form of reports. In this system, by comparing and analyzing the classification accuracy of different feature sets and classifiers, the optimal classification algorithm is obtained and applied to the system. The algorithm accuracy of the system is tested based on the MIT-BIH data set. The result shows that AdaBoost + Random Forest achieved 99.11% accuracy with optimal feature sets. The intelligent heartbeat classification system based on this algorithm has also achieved good results on clinical data.

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features.

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.