Monitoring Longitudinal Trends and Assessment of the Health Risk of Shigella flexneri Antimicrobial Resistance.

Shigella flexneri infection is the main cause of diarrhea in humans worldwide. The emergence of antimicrobial resistance (AMR) of S. flexneri is a growing public health threat worldwide, while large-scale studies monitoring the longitudinal AMR trends of isolates remain scarce. Here, the AMR gene (ARG) profiles of 717 S. flexneri isolates from 1920 to 2020 worldwide were determined. The results showed that the average number of ARGs in isolates has increased significantly, from 19.2 ± 2.4 before 1970 to 29.6 ± 5.3 after 2010. In addition, mobile genetic elements were important contributors to ARGs in S. flexneri isolates. The results of the structural equation model showed that the human development index drove the consumption of antibiotics and indirectly promoted the antibiotic resistance. Finally, a machine learning algorithm was used to predict the antibiotic resistance risk of global terrestrial S. flexneri isolates and successfully map the antibiotic resistance threats in global land habitats with over 80% accuracy. Collectively, this study monitored the longitudinal AMR trends, quantitatively surveilled the health risk of S. flexneri AMR, and provided a theoretical basis for mitigating the threat of antibiotic resistance.

Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals.

Gesture recognition based on wearable devices is one of the vital components of human-computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models' test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.

Monitoring longitudinal antimicrobial resistance trends of Staphylococcus aureus strains worldwide over the past 100 years to decipher its evolution and transmission.

Staphylococcus aureus inhabits diverse habitats including food waste and wastewater treatment plants. Cases of S. aureus-induced infection are commonly reported worldwide. The emergence of antimicrobial resistance (AMR) of S. aureus is a growing public health threat worldwide. Here, we longitudinally monitored global trends in antibiotic resistance genes (ARGs) of 586 S. aureus strains, isolated between 1884 and 2022. The ARGs in S. aureus exhibited a significant increase over time (P < 0.0001). Mobile genetic elements play a crucial role in the transfer of ARGs in S. aureus strains. The structural equation model results revealed a significant correlation between the human development index and rising antibiotic consumption, which subsequently leads to an indirect escalation of AMR in S. aureus strains. Lastly, a machine learning algorithm successfully predicted the AMR risk of global terrestrial S. aureus with over 70% accuracy. Overall, these findings provided valuable insights for managing AMR in S. aureus.

Horizontal gene transfer in activated sludge enhances microbial antimicrobial resistance and virulence.

Activated sludge (AS) plays a vital role in removing organic pollutants and nutrients from wastewater. However, the risks posed by horizontal gene transfer (HGT) between bacteria in AS are still unclear. Here, a total of 478 high-quality non-redundant metagenome-assembled genomes (MAGs) were obtained. >50 % and 5 % of MAGs were involved in at least one HGT and recent HGT, respectively. Most of the transfers (82.4 %) of antimicrobial resistance genes (ARGs) occurred among the classes of Alphaproteobacteria and Gammaproteobacteria. The bacteria involved in the transfers of virulence factor genes (VFGs) mainly include Alphaproteobacteria (42.3 %), Bacteroidia (19.2 %), and Gammaproteobacteria (11.5 %). Moreover, the number of ARGs and VFGs in the classes of Alphaproteobacteria and Gammaproteobacteria was higher than that in other bacteria (P < 0.001). Mobile genetic elements were important contributors to ARGs and VFGs in AS bacteria. These results have implications for the management of antimicrobial resistance and virulence in activated sludge microorganisms.

Molecular basis of phenotypic plasticity in a marine ciliate.

Phenotypic plasticity, which involves phenotypic transformation in the absence of genetic change, may serve as a strategy for organisms to survive in complex and highly fluctuating environments. However, its reaction norm, molecular basis, and evolution remain unclear in most organisms, especially microbial eukaryotes. In this study, we explored these questions by investigating the reaction norm, regulation, and evolution of phenotypic plasticity in the cosmopolitan marine free-living ciliates Glauconema spp., which undergo significant phenotypic changes in response to food shortages. This study led to the de novo assembly of macronuclear genomes using long-read sequencing, identified hundreds of differentially expressed genes associated with phenotypic plasticity in different life stages, validated the function of two of these genes, and revealed that the reaction norm of body shape in response to food density follows a power-law distribution. Purifying selection may be the dominant evolutionary force acting on the genes associated with phenotypic plasticity, and the overall data support the hypothesis that phenotypic plasticity is a trait maintained by natural selection. This study provides novel insight into the developmental genetics of phenotypic plasticity in non-model unicellular eukaryotes and sheds light on the complexity and long evolutionary history of this important survival strategy.

Dynamic changes of quality and flavor characterization of Zhejiang rosy vinegar during fermentation and aging based on untargeted metabolomics.

Zhejiang Rosy Vinegar (ZRV) is a traditional condiment in Southeast China. This study aimed to track the physicochemical, microbiological, sensory changes, and metabolomic profiles of ZRV during fermentation and aging. The increase of acidity and decrease of reducing sugar were associated with the dominant growth of Lactobacillus and Acetobacter. The total 35 volatile compounds were identified in ZRV, mainly containing alcohols, esters, acids, aldehydes, ketones acids, phenols and nitrogen-containing. Compared to phenethyl acetate with sweet aroma in fresh vinegar, the compound with high odor activity values was isoamyl acetate with fruity aromas in aged vinegar. Furthermore, 1309 types of non-volatile components were identified, and histidine metabolism and arginine biosynthesis were revealed as main pathways during fermenting and aging. Concurrently, various bioactive substances in ZRV were identified. This study enriched the knowledge on the components and flavor of ZRV, and assist to improve the production quality of vinegar.

Distinct increase in antimicrobial resistance genes among Vibrio parahaemolyticus in recent decades worldwide.

Vibrio parahaemolyticus is a common pathogen, and has emerged with multiple antimicrobial resistance (AMR). However, few studies have conducted large-scale investigations of AMR and virulence trends of V. parahaemolyticus worldwide. This study longitudinally monitored antibiotic resistance genes (ARGs) and virulence factor genes (VFGs) trends of 1540 V. parahaemolyticus isolates isolated from 1951 to 2021. The number of ARGs in V. parahaemolyticus isolates distinctly increased over the years (P = 5.9e-10), while the number of VFGs decreased significantly (P < 2.2e-16). However, the number of VFGs of isolates isolated from humans has not changed significantly over the years (R = 0.013, P = 0.74), suggesting that the pathogenic risk to humans has not been reduced. Besides, mobile genetic elements are important contributors to ARGs in V. parahaemolyticus (R = 0.34, P < 2.2e-16), but have no promoting effect on VFGs (P = 0.50). The structural equation model illustrated that the human development index promoted the consumption of antibiotics, thereby indirectly promoting an increase in the AMR of the V. parahaemolyticus isolates. Finally, the random forest was performed to predict the ARG and VFG risks of global terrestrial V. parahaemolyticus isolates, and successfully map these threats with over 80% accuracy. This study aimed to evaluate the global risks posed by AMR and virulence, which helps to develop methods specifically targeting V. parahaemolyticus to mitigate these threats.

[Clinical significance and risk factors of redundant nerve root in patients with lumbar spinal stenosis].

OBJECTIVE: To investigate the clinical significance and screen the risk factors of redundant nerve roots(RNRs) in patients with lumbar spinal stenosis. METHODS: The clinical data of 196 patients with lumbar spinal stenosis in the department of Spinal Surgery, Yijishan Hospital, Wannan Medical College from April 1, 2015 to November 30, 2020 were retrospectively analyzed. All patients were divided into RNRs positive group and RNRs negative group according to the presence of RNRs. The differences in general clinical data, imaging parameters, visual analogue scale(VAS), Oswestry disability index(ODI), and other indicators between the two groups were compared. The risk factors which are highly correlated with RNRs were screened by binary Logistic regression analysis. RESULTS: There were 59 cases in the RNRs positive group, with an occurrence rate of 29.95% (59/137), and 137 cases in the RNRs negative group. The incidence rate of RNRs in 196 patients with lumbar spinal stenosis was 30.10% (59/196). VAS and ODI scores of patients in the two groups were statistically significant (P<0.05), and clinical symptoms of patients in the RNRs positive group were more severe than those in the RNRs negative group. There were significant differences in age, number of stenosis segments, average area of lumbar dural sac, area of the narrowest segment and the narrowest segment(P<0.05). Binary logistic regression analysis showed that the number of stenosis segments, the average median sagittal diameter of spinal canal, and the average area of dural sac in lumbar intervertebral space were correlated with the generation of RNRs (P<0.05). The regression coefficient of the number of stenosis segments was -1.115, the regression coefficient of the median sagittal diameter of the spinal canal was -1.707, and the regression coefficient of the mean dural sac area of the lumbar intervertebral space was 7.556. CONCLUSION: The clinical symptoms of patients with lumbar spinal stenosis accompanied by RNRs are more severe than those without them. The number of narrow segments, median sagittal diameter of the spinal canal, and the area of the lumbar intervertebral dural sac are the high-risk factors for RNRs, with the area of the lumbar intervertebral dural sac has the highest correlation.

Attribute analysis and modeling of color harmony based on multi-color feature extraction in real-life scenes.

Color harmony is the focus of many researchers in the field of art and design, and its research results have been widely used in artistic creation and design activities. With the development of signal processing and artificial intelligence technology, new ideas and methods are provided for color harmony theory and color harmony calculation. In this article, psychological experimental methods and information technology are combined to design and quantify the 16-dimensional physical features of multiple colors, including multi-color statistical features and multi-color contrast features. Eighty-four subjects are invited to give a 5-level score on the degree of color harmony for 164 multi-color materials selected from the screenshots of film and television scenes. Based on the multi-color physical features and the subjective evaluation experiment, the correlation analysis is firstly carried out, which shows that the overall lightness, difference of the color tones, number of multiple colors, lightness contrast, color tone contrast, and cool/warm contrast are significantly correlated with color harmony. On the other hand, the regression prediction model and classification prediction model of color harmony are constructed based on machine learning algorithms. In terms of regression prediction model, the prediction accuracy of linear models is higher than that of nonlinear models, with 63.9% as the highest, indicating that the multi-color physical features can explain color harmony well. In terms of classification prediction model, the Random Forest (RF) has the best prediction performance, with an accuracy of 80.2%.

Comparison and Analysis of Timbre Fusion for Chinese and Western Musical Instruments.

Timbre fusion is the theoretical basis of instrument acoustics and Chinese and Western orchestral acoustics. Currently, studies on timbre fusion are mainly focused on Western instruments, but there are some studies on the timbre fusion of Chinese instruments. In this paper, the characteristics of timbre fusion for Chinese and Western instruments are explored, focusing on the subjective attributes and objective acoustic parameters, and a series of experiments is carried out. First, a database containing 518 mixed timbre stimuli of Chinese and Western instruments was constructed to provide basic data that are necessary for the subjective and objective analyses of timbre fusion. We designed and conducted a subjective evaluation experiment of timbre perception attributes based on the method of successive categories. The experimental data were processed using statistical approaches, such as variance analysis, multidimensional preference analysis, and correlation analysis, and we studied the influence of the temporal envelopes and instrument types on fusion, segregation, roughness, and pleasantness. In addition, the differences between Chinese and Western instruments were compared based on these four perception attributes. The results show that fusion and segregation are the most important attributes for Chinese instrument timbre, while roughness is the most important attribute for Western instrument timbre. In addition, multiple linear regression, random forest, and multilayer perceptron were used to construct a set of timbre fusion models for Chinese and Western instruments. The results show that these models can better predict the timbre fusion attributes. It was also found that there are some differences between the timbre fusion models for Chinese and Western instruments, which is consistent with the analysis results of subjective experimental data. The contribution of acoustic objective parameters to the fusion model is also discussed.

Deciphering the succession patterns of bacterial community and their correlations with environmental factors and flavor compounds during the fermentation of Zhejiang rosy vinegar.

Zhejiang Rosy Vinegar (ZRV) is a traditional condiment in Southeast China, produced using semi-solid-state fermentation techniques under an open environment, yet little is known about the functional microbiota involved in the flavor formation of ZRV. In this study, 43 kinds of volatile flavor substances were identified by HS-SPME/GC-MS, mainly including ethyl acetate (relative content at the end of fermentation: 1104.1 mg/L), phenylethyl alcohol (417.6 mg/L) and acetoin (605.2 mg/L). The most abundant organic acid was acetic acid (59.6 g/L), which kept rising during the fermentation, followed by lactic acid (7.0 g/L), which showed a continuously downward trend. Amplicon sequencing analysis revealed that the richness and diversity of bacterial community were the highest at the beginning and then maintained decreasing during the fermentation. The predominant bacteria were scattered in Acetobacter (average relative abundance: 63.7%) and Lactobacillus (19.8%). Both sequencing and culture-dependent analysis showed Lactobacillus dominated the early stage (day 10 to 30), and Acetobacter kept highly abundant from day 40 to the end. Spearman correlation analysis displayed that the potential major groups involved in the formation of flavor compounds were Acetobacter and Lactobacillus, which were also showed strong relationships with other bacteria through co-occurrence network analysis (edges attached to Acetobacter: 61.7%; Lactobacillus: 14.0%). Moreover, structural equation model showed that the contents of ethanol, titratable acid and reducing sugar were the major environmental factors playing essential roles in influencing the succession of bacterial community and their metabolism during the fermentation. Overall, these findings illuminated the dynamic profiles of bacterial community and flavor compounds and the potential functional microbes, which were expected to help us understand the formation of flavor substances in ZRV.

Optimisation of lactic acid fermentation for improved vinegar flavour during rosy vinegar brewing.

BACKGROUND: Rosy vinegar is a well-known traditional Chinese product whose flavour is affected by its lactic acid content. In this study, Lactobacillus bacteria were employed to increase the content of lactic acid during the ethanol fermentation stage. RESULTS: The optimised fermentation parameters were determined as an inoculation amount of 3% (v/v), a temperature of 30 degrees C and an initial pH value of 4.0. Fermentation under these optimal conditions resulted in an alcohol degree of 6.2% (v/v), a total acidity of 49.5 g L(-1) and a lactic acid content of 4.14 g L(-1). The content of lactic acid (4.14 g L(-1)), which approached the level achieved by solid state fermentation, was 3.56-fold higher than that in vinegar fermented without lactic acid bacteria (1.16 g L(-1)). CONCLUSION: The results indicate that mixed fermentation with Lactobacillus plantarum and Saccharomyces cerevisiae strains greatly increases the lactic acid content and improves the flavour of rosy vinegar.