Antibacterial mechanism of inosine against Alicyclobacillus acidoterrestris.

Inosine could potentially become a novel antibacterial agent against Alicyclobacillus acidoterrestris as low doses of inosine can prevent its contamination. However, until now the antibacterial mechanism of inosine targeting A. acidoterrestris is still unknown. In this study, to unravel the mechanism of inosine against A. acidoterrestris puzzle, the effects of inosine on bacterial surface hydrophobicity, intracellular protein content, cell membrane damage extent, and permeability of the A. acidoterrestris were investigated. The results showed that inosine can effectively inhibit the growth and reproduction of A. acidoterrestris by destroying the integrity of cell membrane and increasing its permeability, causing the leakage of intracellular nutrients. Furthermore, the interaction networks of inosine target proteins were analyzed. The interaction networks further revealed that damage to bacterial cell membranes might be relevant to inosine's effect on bacterial DNA replication and cell energy metabolism through regulating nucleotide synthesis and metabolism and the activity of translation initiation factors. Finally, the antibacterial mechanism of inosine against A. acidoterrestris was proposed.

Architecture of the baculovirus nucleocapsid revealed by cryo-EM.

Baculovirus Autographa californica multiple nucleopolyhedrovirus (AcMNPV) has been widely used as a bioinsecticide and a protein expression vector. Despite their importance, very little is known about the structure of most baculovirus proteins. Here, we show a 3.2 Å resolution structure of helical cylindrical body of the AcMNPV nucleocapsid, composed of VP39, as well as 4.3 Å resolution structures of both the head and the base of the nucleocapsid composed of over 100 protein subunits. AcMNPV VP39 demonstrates some features of the HK97-like fold and utilizes disulfide-bonds and a set of interactions at its C-termini to mediate nucleocapsid assembly and stability. At both ends of the nucleocapsid, the VP39 cylinder is constricted by an outer shell ring composed of proteins AC104, AC142 and AC109. AC101(BV/ODV-C42) and AC144(ODV-EC27) form a C14 symmetric inner layer at both capsid head and base. In the base, these proteins interact with a 7-fold symmetric capsid plug, while a portal-like structure is seen in the central portion of head. Additionally, we propose an application of AlphaFold2 for model building in intermediate resolution density.

Discovering Mathematical Expressions Through DeepSymNet: A Classification-Based Symbolic Regression Framework.

Symbolic regression (SR) is the process of finding an unknown mathematical expression given the input and output and has important applications in interpretable machine learning and knowledge discovery. The major difficulty of SR is that finding the expression structure is an NP-hard problem, which makes the entire process time-consuming. In this study, the solution of expression structures was regarded as a classification problem and solved by supervised learning such that SR can be solved quickly by using the solving experience. Techniques for classification tasks, such as equivalent label merging and sample balance, were used to enhance the robustness of the algorithm. We proposed a symbolic network called DeepSymNet to represent symbolic expressions to improve the performance of the algorithm. DeepSymNet has been proven to have a strong representation ability with a shorter label compared to the current popular representation methods, reducing the search space when predicting. Moreover, DeepSymNet conveniently decomposes SR into two smaller subproblems, which makes solving the problem easier. The proposed algorithm was tested on artificially generated expressions and public datasets and compared with other algorithms. The results demonstrate the effectiveness of the proposed algorithm.

Recent progress in the development of ursolic acid derivatives as anti-diabetes and anti-cardiovascular agents.

Ursolic acid (UA) is a pentacyclic triterpenoid, which exhibits many biological activities, particularly in anti-cardiovascular and anti-diabetes. The further application of UA is greatly limited due to its low bioavailability and poor water solubility. Up to date, various UA derivatives have been designed to overcome these shortcomings. In this paper, the authors reviewed the development of UA derivatives as the anti-diabetes anti-cardiovascular reagents.

SNR: Symbolic network-based rectifiable learning framework for symbolic regression.

Symbolic regression (SR) can be utilized to unveil the underlying mathematical expressions that describe a given set of observed data. At present, SR can be categorized into two methods: learning-from-scratch and learning-with-experience. Compared to learning-from-scratch, learning-with-experience yields results that are comparable to those of several benchmarks and incurs significantly lower time costs for obtaining expressions. However, the learning-with-experience model performs poorly in terms of unseen data distributions and lacks a rectification tool, apart from constant optimization, which exhibits limited performance. In this study, we propose a Symbolic Network-based Rectifiable Learning Framework (SNR) that possesses the ability to correct errors. SNR adopts Symbolic Network (SymNet) to represent an expression, and the encoding of SymNet is designed to provide supervised information, with numerous self-generated expressions, to train a policy net (PolicyNet). The training of PolicyNet can offer prior knowledge to guide effective searches. Subsequently, the incorrectly predicted expressions are revised via a rectification mechanism. This rectification mechanism endows SNR with broader applicability. Experimental results demonstrate that our proposed method achieves the highest averaged coefficient of determination on self-generated datasets when compared with other state-of-the-art methods and yields more accurate results in public datasets.

Multiplex single-cell droplet PCR with machine learning for detection of high-risk human papillomaviruses.

High-risk human papillomavirus (HPV) testing can significantly decline the incidence and mortality of cervical cancer. Microfluidic technology provides an effective method for accurate detection of high-risk HPV by utilizing multiplex single-cell droplet polymerase chain reaction (PCR). However, current strategies are limited by low-integration microfluidic chip, complex reagent system, expensive detection equipment and time-consuming droplet identification. Here, we developed a novel multiplex droplet PCR method that directly detected high-risk HPV sequences in single cells. A multiplex microfluidic chip integrating four flow-focusing structures was designed for one-step and parallel droplet preparation. Using single-cell droplet PCR, multi-target sequences were detected simultaneously based on a monochromatic fluorescence signal. We applied machine learning to automatically identify the large populations of single-cell droplets with 97% accuracy. HPV16, 18 and 45 sequences were sensitively detected without cross-contamination in mixed CaSki and Hela cells. The approach enables rapid and reliable detection of multi-target sequences in single cells, making it powerful for investigating cellular heterogeneity related to cancer diagnosis and treatment.

Biofilm Formation of Listeria monocytogenes and Pseudomonas aeruginosa in a Simulated Chicken Processing Environment.

This study aims to investigate the mono- and dual-species biofilm formation of Listeria monocytogenes and Pseudomonas aeruginosa incubated in different culture mediums, inoculum ratios, and incubation time. The planktonic cell population and motility were examined to understand the correlation with biofilm formation. The results showed that chicken juice significantly inhibited the biofilm formation of L. monocytogenes (p < 0.05). Pseudomonas aeruginosa was the dominant bacteria in the dual-species biofilm formation in the trypticase soy broth medium. The dynamic changes in biofilm formation were not consistent with the different culture conditions. The growth of planktonic L. monocytogenes and P. aeruginosa in the suspension was inconsistent with their growth in the biofilms. There was no significant correlation between motility and biofilm formation of L. monocytogenes and P. aeruginosa. Moreover, scanning electron microscopy (SEM) results revealed that the biofilm structure of L. monocytogenes was loose. At the same time, P. aeruginosa formed a relatively dense network in mono-species biofilms in an initial adhesion stage (24 h). SEM results also showed that P. aeruginosa was dominant in the dual-species biofilms. Overall, these results could provide a theoretical reference for preventing and controlling the biofilm formation of L. monocytogenes and P. aeruginosa in the food processing environment in the future.

Adhesion and kinetics of biofilm formation and related gene expression of Listeria monocytogenes in response to nutritional stress.

Listeria monocytogenes is a gram-positive pathogen, that usually adheres to stainless steel (SS), and other abiotic surfaces in food processing that undergo repeated cleaning and cause the spread of Listeria. Through the enumeration of biofilm cells, extracellular polymeric substance (EPS) component and the scanning electron microscopy (SEM) analysis of biofilms, it was found that the ratio of cells and extracellular matrix is affected by nutrition status. Regardless of the temperature, all strains exhibited a higher adhesion ability when exposed to 10-fold diluted TSB-YE (DTSB-YE, nutrition deficiency). Three hour initial adhesion was significantly positively correlated with biofilm formation (p＜0.01). DTSB-YE enhances initial attachment and subsequently promotes biofilm formation. The SEM analysis also showed that in DTSB-YE the adhesion and covered area of the attached cells were higher than those in TSB-YE (rich media). The amount of both extracellular polysaccharides and proteins was significantly higher when incubated in DTSB-YE than TSB-YE. The highest biofilm formation of Lm83 was observed in DTSBYE independent of temperature. The effects of nutrition deficiency on the expression of critical biofilm-associated genes of Lm 83 planktonic and biofilm cells were measured. The gene expression levels of inlA and sigB in biofilm cells in TSB-YE and DTSB-YE were approximately 95.7% and 88.0% and 42.2% and 45.7% lower than those in planktonic cells, respectively. However, the expression of inlA in DTSB-YE was significantly higher (p＜0.05) than that in TSB-YE for the same cell state. Interestingly, the gene expression of motB was considerably higher in DTSB-YE than in TSBYE, regardless of the state. These results indicate that better cell motility in nutrient deficiencies might facilitate the cell aggression to promote biofilm formation.

Technology investigation on time series classification and prediction.

Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data. In recent years, many scholars have achieved fruitful results in the study of time series. A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction. Therefore, in this study, we focus on analyzing the technical development routes of time series classification and prediction algorithms. 87 literatures with high relevance and high citation are selected for analysis, aiming to provide a more comprehensive reference base for interested researchers. For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks. For time series prediction, from classical statistical methods, to neural network methods, and then to fuzzy modeling and transfer learning methods, the performance and applications of these different methods are discussed. We hope this article can help aid the understanding of the current development status and discover possible future research directions, such as exploring interpretability of time series analysis and online learning modeling.

Encoder-X: Solving Unknown Coefficients Automatically in Polynomial Fitting by Using an Autoencoder.

Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.

Multi-angle head pose classification with masks based on color texture analysis and stack generalization.

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Inactivation and Subsequent Growth Kinetics of Listeria monocytogenes After Various Mild Bactericidal Treatments.

This study was carried out to investigate the effects of mild heat, lactic acid, benzalkonium chloride and nisin treatments on the inactivation, sublethal injury, and subsequent growth of Listeria monocytogenes. Results showed that the Bigelow model successfully described the thermal inactivation kinetics, while the Log-linear model with tail consistently offered the most accurate fit to LA, BC, and nisin inactivation curves of cells. Differential plating indicated that percentage of sublethal injury for nisin treated cells was significantly higher than that for the other three treatments. Compared to non-treated cells, significant extension of lag time was observed for all treated cells. The longer exposures to heat treatment contributed to the extended lag time of the survivors. While for LA, BC and nisin treated cells, the longest lag time was not observed at the most severe treatment conditions. The correlation analysis of sublethal injury percentage on the duration of lag time revealed that only heat treatment showed the significant correlation. Overall, the lag time analysis could evaluate a wide range of bacterial injury. Lag time of treated cells was significantly influenced by stress treatments and temperatures of recovery, however, there were not any significant changes in the maximum specific growth rate between treated and non-treated cells under isothermal recovery conditions. The information generated from this study is valuable for utilizing intervention strategies in the elimination or growth inhibition of L. monocytogenes.

Identification of Listeria monocytogenes Contamination in a Ready-to-Eat Meat Processing Plant in China.

Listeria monocytogenes is the etiologic agent of listeriosis, which remains a significant public health concern in many countries due to its high case-fatality rate. The constant risk of L. monocytogenes transmission to consumers remains a central challenge in the food production industry. At present, there is very little known about L. monocytogenes contamination in ready-to-eat (RTE) processing plants in China. In this study, L. monocytogenes in an RTE meat processing plant in Shanghai municipality was characterized using pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS). Furthermore, the biofilm formation ability of the pathogen was also tested. Results revealed that L. monocytogenes isolates were present in 12 samples out of the 48 samples investigated. Most of them (66.7%, 8/12) were identified from the processing facilities irrespective of observed hygiene levels of aerobic plate count (APC) and coliforms. Coliforms were present in only one processing area. ST5 (1/2b) isolates were predominant (83.3%, 10/12) and were identified in two dominant pulsotypes (PTs) (three in PT3 and seven in PT4, respectively). Results of the core-genome multi-locus sequence typing (cgMLST) showed that ST5 in three PTs (PT1, PT3, and PT4) had 0-8 alleles, which confirmed that clonal transmission occurred in the RTE meat processing facilities. In addition, the biofilm formation test confirmed that the isolates from the processing facilities could form biofilms, which helped them colonize and facilitate persistence in the environment. These results indicated that common sanitation procedures regularly applied in the processing environment were efficient but not sufficient to remove L. monocytogenes isolates, especially biofilm of L. monocytogenes. Furthermore, the ST5 isolates in this study exhibited 12 alleles with one ST5 clinical isolate, which contributes to the understanding of the potential pathogenic risk that L. monocytogenes in RTE meat processing equipment posed to consumers. Therefore, strong hygienic measures, especially sanitation procedures for biofilms eradication, should be implemented to ensure the safety of raw materials. Meanwhile, continuous surveillance might be vital for the prevention and control of listeriosis caused by L. monocytogenes.

Icariin Stimulates hFOB 1.19 Osteoblast Proliferation and Differentiation via OPG/RANKL Mediated by the Estrogen Receptor.

BACKGROUND: Icariin (ICA), one of the main effective components isolated from the traditional Chinese herb Epimedium brevicornu Maxim., has been reported to possess extensive pharmacological actions, including enhanced sexual function, immune regulation, anti-inflammation, and antiosteoporosis. METHODS: Our study was designed to investigate the effect of ICA on cell proliferation and differentiation and the molecular mechanism of OPG/RANKL mediated by the Estrogen Receptor (ER) in hFOB1.19 human osteoblast cells. RESULTS: The experimental results show that ICA can stimulate cell proliferation and increase the activity of Alkaline Phosphatase (ALP), Osteocalcin (BGP) and I Collagen (Col I) and a number of calcified nodules. Furthermore, the mRNA and protein expression of OPG and RANKL and the OPG/ RANKL mRNA and protein expression ratios were upregulated by ICA. The above-mentioned results indicated that the optimal concentration of ICA for stimulating osteogenesis was 50ng/mL. Subsequent mechanistic studies comparing 50ng/mL ICA with an estrogen receptor antagonist demonstrated that the effect of the upregulated expression is connected with the estrogen receptor. In conclusion, ICA can regulate bone formation by promoting cell proliferation and differentiation and upregulating the OPG/RANKL expression ratio by the ER in hFOB1.19 human osteoblast cells.