A Gram-stain-negative and facultatively anaerobic bacterial strain, designated GUO
, was isolated from surface water collected from the South China Sea. Cells were non-flagellate, yellow, ...non-spore-forming and rod-shaped. The 16S rRNA gene sequence comparisons with species in the genus
showed that strain GUO
shares the highest similarity of 97.5 % with
and
. Average nucleotide identity and digital DNA-DNA hybridization values between strain GUO
and its related type strains were 77.1-78.4% and 20.8-26.2 % respectively. Growth of strain GUO
occurred at 15-50°C (optimum, 20-25°C), pH 5-7.5 (pH 6) and in media containing 0-7 % NaCl (optimum, 0-1 %). Cells contained methanol-soluble yellow-coloured pigments but flexirubin-type pigments were absent. The major fatty acids (>5 %) were iso-C
3-OH, iso-C
, anteiso-C
, C
, summed feature 3, iso-C
G and iso-C
3-OH. The dominant polar lipids comprised phosphatidylethanolamine and some unidentified polar lipids. The main respiratory quinone was menaquinone-6. The DNA G+C content of strain GUO
was 40.1 %. Based on the presented data, we consider strain GUO
to represent a novel species of the genus
, for which the name
sp. nov. is proposed. The type strain is GUO
(=KCTC 62629
=MCCC 1K03559
).
Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the ...needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.
Machine learning algorithms are susceptible to cyberattacks, posing security problems in computer vision, speech recognition, and recommendation systems. So far, researchers have made great strides ...in adopting adversarial training as a defensive strategy. Single-step adversarial training methods have been proposed as viable solutions for improving model generality and resilience. However, there has been little study to address this issue in the context of ownership-based recommendations, which may fail to capture stable results. In this work, we adapt the single-step adversarial training for ownership recommendation systems. Our main technical contributions are as follows: (1) We propose Adversarial Consumption and Production Relationship (ACPR), a model that combines factorization machine and single-step adversarial training for ownership recommendations. It enables us to take advantage of modeling consumption-production interactions with a factorization machine instead of the conventional matrix factorization method for ownership recommendations. (2) We enrich the ACPR technique with directional adversarial training and call our technique Adversarial Consumption and Production Relationship-Aware Directional Adversarial Model (ACPR-ADAM). The idea behind our ACPR-ADAM is that instead of the worst perturbation direction, the perturbation direction in the embedding space is restricted to other examples in the current embedding space, allowing us to incorporate the collaborative signal into the training process. Lastly, through extensive evaluations on Reddit and Pinterest, we demonstrate that our proposed method outperforms state-of-the-art methods. Compared with CPR and ACPR on Reddit and Pinterest datasets, our proposed ACPR-ADAM achieves 93%, 88%, and 72%, 69% enhancement in terms of AUC and HR, respectively.
Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study ...aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics.
The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs).
A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90;
< 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13;
< 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates.
Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).
The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of ...vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.
Probiotic fermented fruit extract is a new kind of food with potential health care efficacy. In this study, transformation of bioactive substances in Lactic acid bacteria (LAB) and yeast fermented ...kiwifruit extract were researched. The highest levels of total polyphenol, superoxide dismutase (SOD) were found in Lactobacillus paracasei LG0260 fermented kiwifruit extract with 2.31 ± 0.06 mgGAE/g, 369 ± 12.73 U.mL−1, respectively. LAB fermented kiwifruit extract dramatically had a highest vitamin C (VitC) concentration during the fermentation, while yeast and natural fermented samples were decline steadily. Another interesting founding, a highest level of γ-aminobutyric acid (GABA) was found in Saccharomyces cerevisiae J2861 fermented kiwifruit extract with 24.132 ± 1.01 μg/mL. Furthermore, the main organic acids in fresh kiwifruit and yeast fermented kiwifruit extract were citric acid and malic acid. However, lactic acid and citric acid were the main organic acids in LAB fermented kiwifruit extract. A total of 43 kinds of flavor compounds in fresh kiwifruit and 88 kinds of flavor compounds in fermented kiwifruit extract were identified. Esters and alcohols in fermented kiwifruit system increased by the fermentation of selected bacteria which improved the taste and flavor.
•Probiotic fermented kiwifruit extract is a new kind of health care efficacy food.•The evolution of biochemical components in long-term fermented system was proved.•The change of organic acids in fermented kiwifruit extract was revealed.•The flavor compounds of different fermented kiwifruit extracts were compared.
Triangular grid reinforced by carbon fiber/epoxy (CF/EP) was designed and manufactured. The sandwich structure was prepared by gluing the core and composite skins. The mechanical properties of the ...sandwich structure were investigated by the finite element analysis (FEA) and three-point bending methods. The calculated bending stiffness and core shear stress were compared to the characteristics of a honeycomb sandwich structure. The results indicated that the triangular core ultimately failed under a bending load of 11000 N; the principal stress concentration was located at the loading region; and the cracks occurred on the interface top skin and triangular core. In addition, the ultimate stress bearing of the sandwich structure was 8828 N. The experimental results showed that the carbon fiber reinforced triangular grid was much stiffer and stronger than the honeycomb structure.
Sourdough plays an important role in improving product quality. In this study, single-strain and mixed fermentation of Lactobacillus paracasei LG0260, Lactobacillus plantarum LG1034, Lactococcus ...lactis LG0827, Saccharomyces cerevisiae J2815 and Saccharomyces cerevisiae J8202 were used to prepare type II sourdough, which was used to evaluate the ability of mixed fermentation to improve the quality of sourdough and increase volatile flavor compounds. In sourdough, mixed fermentation reduced the number of lactic acid bacteria (LAB) and yeasts colonies, accompanied by a decrease in acidity. However, the organic acid analysis found that mixed fermentation increased the content of acetic acid and reduced the content of lactic acid. Mixed fermentation enhanced the degradation of phytic acid. It was found that sourdough MY1L3 degrades up to 96.6% of phytic acid. In addition, the total polyphenol content of mixed fermentation was decreased, but the DPPH free radical scavenging ability was enhanced. It was found by HS-SPME-GC-MS that mixed-fermented sourdough had more complex flavor compounds, such as acids, aldehydes, and esters. The differences in the metabolic properties of different starter cultures in sourdough will provide a theoretical reference for the improvement of sourdough quality.
•Microbial colonies in mixed fermentation sourdough reduced, accompanied by a decrease in acidity.•Mixed fermentation increased acetic acid content and reduced lactic acid content in sourdough.•Mixed fermentation showed the strong ability to degrade phytic acid.•Mixed-fermented sourdough had more complex flavor compounds.
•ECG episodes are repeated and resampled for augmenting small datasets.•The data augmentation balances samples among different categories.•The augmentation enhances deep neural networks in atrial ...fibrillation detection.
Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains challenging in real clinical settings. Deep neural networks (DNN) emerge as a promising tool for the task of AF detection. However, the success of DNN for AF detection is hampered by limited size and imbalanced number of samples in datasets. We propose a novel data augmentation strategy based on duplication, concatenation and resampling of ECG episodes to balance the number of samples among different categories as well as to increase the diversity of samples. The performance of the data augmentation method was examined on an AF database from Computing in Cardiology (CinC) challenge 2017. A 2-layer long short-term memory (LSTM) network was trained with the augmented dataset. Its ability of AF detection was evaluated using a 10-fold cross validation approach. And F1 score was adopted as the metrics. The AF detection results show that the proposed method was superior to two conventional data augmentation methods: window slicing and permutation. The network was also submitted to the evaluation system of the CinC challenge 2017. The F1 score obtained by the network using the proposed data augmentation method was close to the winner (0.82 vs. 0.83). In summary, the proposed data augmentation method provides an effective solution to enhance the dataset for improving the performance of DNN in ECG analysis. Such a method promotes the application of deep learning in the analysis of ECG, particularly when the dataset is small and imbalanced.
Na7PW11O39 (PW11) immobilized on quanternary ammonium functionalized chloromethylated polystyrene (CMPS) was synthesized in a facile method. PW11-DMA16/CMPS exhibited a considerable catalytic ...activity in selective oxidation of cyclohexanol with 30% H2O2, which is similar to that of homogeneous PW11. Moreover, PW11-DMA16/CMPS also showed excellent catalytic activity in oxidation of various alcohols.
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•Na7PW11O39 was successfully immobilized and finely dispersed on DMA16/CMPS.•PW11-DMA16/CMPS exhibited high activity and stability for alcohol oxidation.•A promising method for the construction of POMs and mesoporous-macropores hybrid.
Na7PW11O39 (PW11) was immobilized on quanternary ammonium functionalized chloromethylated polystyrene (DMA16/CMPS) in a facile method and used as efficient recyclable catalyst in oxidation of alcohol. Commercially available mesopores and macropores CMPS was quaternized by N, N- dimethylhexadecylamine (DMA16) followed by immobilization of PW11 onto the pore of CMPS by electrostatic interaction. The synthesized catalyst was characterized by Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (XRD), thermo gravimetric analyzer (TGA), ultraviolet–visible (UV–vis), N2 adsorption–desorption, X-ray photoelectron spectroscopy (XPS), zeta potential, contact angle, 31P nuclear magnetic resonance (31P NMR), scanning electron microscope (SEM), transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS). The results indicated that Na7PW11O39 was successfully immobilized by electrostatic interaction and finely dispersed on quaternary ammonium functionalized CMPS. PW11-DMA16/CMPS showed a similar catalytic activity to that of homogeneous PW11, which was attributed to the hydrophobic nature of the PW11-DMA16/CMPS and facile diffusion of reactant to the active sites provided by widely opened mesopores and macropores. The present approach provides a promising and universal strategy for the construction of hybrid catalysts based on POMs and mesopores and macropores materials.
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