Non-IRC Mechanism of Bimolecular Reactions with Submerged Barriers: A Case Study of Si+ + H2O Reaction.

Chemical reactions with submerged barriers may feature interesting dynamic behaviors that are distinct from those with substantial barriers or those entirely dominated by capture. The Si+ + H2O reaction is a prototypical example, involving even two submerged saddle points along the reaction path: one for the direct dissociation of H (H-dissociation SP) and another for H migration from the O-side to the Si-side (H-migration SP). We investigated the intricacies of this process by employing quasi-classical trajectory calculations on an accurate, full-dimensional ab initio potential energy surface. Through careful trajectory analysis, an interesting nonintrinsic reaction coordinate mechanism was found to play an important role in producing SiOH+ and H. This new pathway is featured as that the H atoms do not form HSiOH+ complexes along the minimum-energy path but directly dissociate into the products after passing through the H-migration SP. Furthermore, based on artificially scaled potential energy surfaces (PESs), the impact of barrier height on the reaction is also explored. This work provides new insights into the dynamics of the Si+ + H2O reaction and enriches our understanding of reactions with submerged barriers.

Successful surgical treatment of impending paradoxical embolism with pulmonary embolism and myocardial infarction.

BACKGROUND: Paradoxical embolism is a rare cause of acute arterial occlusion. This phenomenon arises when embolic material travels from the venous system crosses an abnormal shunt such as patent foramen ovale, atrial septal defects, ventricular septal defects, or pulmonary arteriovenous malformations, into the arterial system. Impending paradoxical embolism refers to the presence of an entrapped thrombus in the patent foramen ovale. CASE PRESENTATION: We report a case of a 68-year-old female patient who presented with an impending paradoxical embolism, alongside both concomitant pulmonary embolism and myocardial infarction with ST-segment elevation. Swiftly addressed through emergency cardiac surgery and systemic anticoagulation, the patient's condition was effectively treated. CONCLUSIONS: While the ideal treatment strategy for impending paradoxical embolism remains a topic of debate due to limited and inconclusive evidence, emergent open surgery should be contemplated in patients as it signifies a critical clinical emergency.

Mulberry leaf and its effects against obesity: A systematic review of phytochemistry, molecular mechanisms and applications.

BACKGROUND: Obesity and hyperlipidemia can induce a variety of diseases, and have become major health problems worldwide. How to effectively prevent and control obesity has become one of the hot-spots of contemporary research. Mulberry leaf is the dried leaf of Morus alba L., which is approved by the Ministry of Health as a "homology of medicine and food", rich in diverse active constituents and with a variety of health effects including anti-obesity and anti-hyperlipidemia activities. PURPOSE: The review attempts to summarize and provide the molecular basis, mechanism, safety and products for further exploration and application of mulberry leaf on the treatment on the control of weight gain and obesity. METHODS: This review is conducted by using ScienceDirect, PubMed, CNKI and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). RESULTS: Based on the research progress of domestic and foreign scholars, the effective phytochemicals, molecular mechanisms and product applications of mulberry leaf in the prevention and treatment of obesity and related metabolic diseases were summarized. CONCLUSION: Mulberry leaf has excellent medicinal and health care value in obesity treatment. However, its pharmacodynamic substance basis and molecular mechanisms need to be further studied.

SPARK: A High-efficiency Black-box Domain Adaptation Framework for Source Privacy-preserving Drowsiness Detection.

Developing an effective and efficient electroencephalography (EEG)-based drowsiness monitoring system is crucial for enhancing road safety and reducing the risk of accidents. For general usage, cross-subject evaluation is indispensable. Despite progress in unsupervised domain adaptation (UDA) and source-free domain adaptation (SFDA) methods, these often rely on the availability of labeled source data or white-box source models, posing potential privacy risks. This study explores a more challenging setting of UDA for EEG-based drowsiness detection, termed black-box domain adaptation (BBDA). In BBDA, adaptation in the target domain relies solely on a black-box source model, without access to the source data or parameters of the source model. To address these privacy concerns, we propose a framework called Self-distillation and Pseudo-labelling for Ensemble Deep Random Vector Functional Link (edRVFL)-based Black-box Knowledge Adaptation (SPARK). SPARK employs entropy-based selection of high-confidence samples, which are then pseudo-labeled to train a student edRVFL network. Subsequently, ensemble self-distillation is performed to extract knowledge by training the edRVFL using refined labels introduced by ensemble learning. This process further improves the robustness of the student edRVFL network. The use of edRVFL as the student network offers advantages such as a closed-form solution, fast computation, and ease of implementation. These features are beneficial for improving the computational efficiency of the framework, making it more suitable for tasks involving small datasets. The proposed SPARK framework is evaluated on two publicly available driver drowsiness datasets. Experimental results demonstrate its superior performance over strong baselines, while significantly reducing training time. These findings underscore the potential for practical integration of the proposed framework into drowsiness monitoring systems, thereby contributing substantially to the privacy preservation of source subjects.

Discovery of cyperenoic acid as a potent and novel entry inhibitor of influenza A virus.

Influenza therapeutics with new targets and modes of action are urgently needed due to the frequent emergence of mutants resistant to currently available anti-influenza drugs. Here we report the in vitro and in vivo anti-influenza A virus activities of cyperenoic acid, a natural compound, which was isolated from a Chinese medicine Croton crassifolius Geise. Cyperenoic acid could potently suppress H1N1, H3N2 and H9N2 virus replication with IC50 values ranging from 0.12 to 15.13 µM, and showed a low cytotoxicity against MDCK cells (CC50 = 939.2 ± 60.0 µM), with selectivity index (SI) values ranging from 62 to 7823. Oral or intraperitoneal treatment of cyperenoic acid effectively protected mice against a lethal influenza virus challenge, comparable to the efficacy of Tamiflu. Additionally, cyperenoic acid also significantly reduced lung virus titers and alleviated influenza-induced acute lung injury in infected mice. Mechanism-of-action studies revealed that cyperenoic acid exhibited its anti-influenza activity during the entry stage of viral replication by inhibiting HA-mediated viral fusion. Simulation docking analyses of cyperenoic acid with the HA structures implied that cyperenoic acid binds to the stalk domain of HA in a cavity near the fusion peptide. Collectively, these results demonstrate that cyperenoic acid is a promising lead compound for the anti-influenza drug development and this research provides a useful small-molecule probe for studying the HA-mediated viral entry process.

Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment.

The prevalence of antibiotic resistance genes (ARGs) in the environment is a quintessential One Health issue that threats both human and ecosystem health; however, the source and transmission of ARGs, especially clinically important ARGs (CLIARGs), in the environment have not yet been well studied. In the present study, shotgun metagenomic approaches were used to characterize the microbiome, resistome, and mobilome composition in human feces and six different environment sample types in South China. Overall, the resistome harbored 157 CLIARGs, with specific ARG hotspots (e.g., human feces, wastewater treatment plants, livestock manure and wastewater) excreting significantly higher abundance of CLIARGs compared with the natural environment. A redundancy analysis (RDA) was performed and revealed that the bacterial community compositions and mobile genetic elements (MGEs) explained 55.08% and 34.68% of the variations in ARG abundance, respectively, indicating that both bacterial community and MGEs are key contributors to the maintenance and dissemination of CLIARGs in the environment. The network analysis revealed non-random co-occurrence patterns between 200 bacterial genera and 147 CLIARGs, as well as between 135 MGEs and 123 CLIARGs. In addition to numerous co-shared CLIARGs among different sample types, the source tracking program based on the FEAST probabilistic model was used to estimate the relative contributions of the CLIARGs from potential sources to the natural environment. The source tracking analysis results delineated that mobilome, more than microbiome, contributed CLIARG transmission from those ARG hotspots into natural environment, and the MGEs in WWTPs seem to play the most significant role in the spread of CLIARGs to the natural environment (average contribution 32.9%-46.4%). Overall, this study demonstrated the distribution and dissemination of CLIARGs in the environment, and aimed to better inform strategies to control the spread of CLIARGs into the natural environment.

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.

Introduction: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. Methods: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. Results: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Discussion: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.

The application of HER2 and CD47 CAR-macrophage in ovarian cancer.

BACKGROUND: The chimeric antigen receptor (CAR)-T therapy has a limited therapeutic effect on solid tumors owing to the limited CAR-T cell infiltration into solid tumors and the inactivation of CAR-T cells by the immunosuppressive tumor microenvironment. Macrophage is an important component of the innate and adaptive immunity, and its unique phagocytic function has been explored to construct CAR macrophages (CAR-Ms) against solid tumors. This study aimed to investigate the therapeutic application of CAR-Ms in ovarian cancer. METHODS: In this study, we constructed novel CAR structures, which consisted of humanized anti-HER2 or CD47 scFv, CD8 hinge region and transmembrane domains, as well as the 4-1BB and CD3ζ intracellular domains. We examined the phagocytosis of HER2 CAR-M and CD47 CAR-M on ovarian cancer cells and the promotion of adaptive immunity. Two syngeneic tumor models were used to estimate the in vivo antitumor activity of HER2 CAR-M and CD47 CAR-M. RESULTS: We constructed CAR-Ms targeting HER2 and CD47 and verified their phagocytic ability to ovarian cancer cells in vivo and in vitro. The constructed CAR-Ms showed antigen-specific phagocytosis of ovarian cancer cells in vitro and could activate CD8+ cytotoxic T lymphocyte (CTL) to secrete various anti-tumor factors. For the in vivo model, mice with human-like immune systems were used. We found that CAR-Ms enhanced CD8+ T cell activation, affected tumor-associated macrophage (TAM) phenotype, and led to tumor regression. CONCLUSIONS: We demonstrated the inhibition effect of our constructed novel HER2 CAR-M and CD47 CAR-M on target antigen-positive ovarian cancer in vitro and in vivo, and preliminarily verified that this inhibitory effect is due to phagocytosis, promotion of adaptive immunity and effect on tumor microenvironment.

Construction of the Gal-NH2/mulberry leaf polysaccharides-lysozyme/luteolin nanoparticles and the amelioration effects on lipid accumulation.

Luteolin is a kind of natural flavonoid with great potential for lipid accumulation intervention. However, the poor water solubility and non-targeted release greatly diminish its efficiency. In this study, 4-aminophenyl ß-D-galactopyranoside (Gal-NH2)/mulberry leaf polysaccharides- lysozyme/luteolin nanoparticles (Gal-MPL/Lut) were fabricated via amide reaction, self-assembly process and electrostatic interaction. The nanoparticles could hepatic-target of Lut and enhance action on liver tissue by specific recognition of asialoglycoprotein receptor (ASGPR). Physicochemical characterization of the nanoparticles showed a spherical shape with a uniform particle size distribution (77.8 ± 2.6 nm) with a polydispersity index (PDI) of 0.22 ± 0.06. Subsequently, in HepG2 cells model, administration with hepatic-targeted Gal-MPL/Lut nanoparticles promoted the cellular uptake of Lut, and regulated lipid metabolism manifested by remarkably inhibiting total cholesterol (TC) and triglyceride (TG) expression levels through the modulation of PI3K/SIRT-1/FAS/CEBP-α signaling pathway. This study provides a promising strategy for a highly hepatic-targeted therapy to ameliorate lipid accumulation using natural medicines facilitated by nano-technology.

Online dynamic ensemble deep random vector functional link neural network for forecasting.

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL's representation ability. Each hidden layer's representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL's output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers' outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series.

Modulation of the catalytic activity and thermostability of a thermostable GH7 endoglucanase by engineering the key loop B3.

The loop B3 of glycoside hydrolase family 7 (GH7) endoglucanases is confined into long and short types. TtCel7 is a thermophilic GH7 endoglucanase from Thermothelomyces thermophilus ATCC 42464 with a long loop B3. TtCel7 was distinct for the excellent thermostability (>30 % residual activity after 1-h incubation at 90 °C). The catalytic efficiency was reduced by removing the disulfide bond in loop B3 (C220A) and truncated the loop B3 (B3cut). However, B3cut exhibited improved thermostability, the remaining enzyme activity increased by 39 %-171 % compared toTtCel7 when treated at 70-90 °C for 1-h. Based on the analysis of molecular dynamics simulation, both loops B1 and A3 of B3cut swing toward the catalytic center, which contributed to the reduced cleft-space and increased structure-rigidity. Conversely, the deletion of disulfide bond resulted in a reduction of structural rigidity in C220A. Through structure-directed enzyme modulation, this study has identified two structural elements that are related to the catalysis and thermostability of TtCel7. The loop B3 of TtCel7 possibly stretches the catalytic pocket, thereby increases the openness of the catalytic tunnel and enhancing flexibility for efficient catalysis. Additionally, the disulfide bond within loop B3 serves to enhance structural stability and maintain a heightened level of activity.

CCR2 antagonist represses fibroblast-like synoviocyte-mediated inflammation in patients with rheumatoid arthritis.

Rheumatoid arthritis (RA) is a common autoimmune disease with a global incidence of approximately 1%. Its complex pathogenesis makes the development of RA-related therapeutics very difficult. Existing drugs for RA have many side effects and are prone to drug resistance. One potential target for RA drugs includes C-Cchemokinereceptortype2 (CCR2), which belongs to the G protein-coupled receptor family. A series of RA drugs targeting CCR2 have been developed; however, the pre-clinical and clinical research results for CCR2 antagonists are inconsistent. We found that CCR2 was also expressed in primary Fibroblast-like synoviocyte (FLS) from patients with RA. CCR2 antagonists can inhibit inflammatory cytokines and matrix metalloproteinases released by RA-FLS but do not affect the proliferation and migration ability of RA-FLS. In addition, CCR2 antagonist-treated RA-FLS indirectly repressed macrophage-mediated inflammation and rescued the viability of chondrocytes. Finally, a CCR2 antagonist ameliorated the collagen-induced arthritic (CIA). CCR2 antagonists may exert anti-inflammatory effects on RA-FLS by inhibiting the JAK-STAT pathway. In summary, a CCR2 antagonist can exert anti-inflammatory effects by acting on RA-FLS. This study provides a new experimental basis for the use of CCR2 antagonists in the development of RA drugs.

Effect of Anionic Polyacrylamide Polymer on Frost Heave Mitigation and Its Implication for Frost-Susceptible Soil.

Seasonally frozen ground regions occupy approximately 55% of the exposed land surface in the Northern Hemisphere, and frost heave is the common global problem in seasonally frozen soil areas. Frost heave induces uneven deformation of ground and damages railways, road paving, and buildings. How to mitigate frost heave is the most important technical issue in this field that has provoked great interest. Here, using freezing experiments, we investigate the effect of anionic polyacrylamide (APAM) polymer on frost susceptible soil. The results demonstrate a so-far undocumented inhibition of frost heave by APAM in freezing soil, namely APAM (tested at concentrations from 0.0 wt% to 0.60 wt%) slows down the frost heave by a factor of up to 2.1 (since 0.60 wt% APAM can decrease frost heave from 8.56 mm to 4.14 mm in comparison to the control experiment). Moreover, it can be observed that the maximum water content near the frozen fringe decreased from 53.4% to 31.4% as the APAM content increased from 0.0 wt% to 0.60 wt%, implying a mitigated ice lens growth. Hydrogen bonding between APAM and soil particles triggers an adsorption mechanism that accumulates soil particles, and thus can potentially inhibit the separation and growth of the ice lens. Moreover, the residue of APAM due to hydrogen bonding-induced adsorption in the pores of granular media may narrow seepage channels (capillary barriers) and provide an unfavourable condition for water migration. The use of APAM can also increase the viscosity of the solution, which causes a greater water migration resistance. This research provides new insights into APAM-influenced frost heave (introducing APAM into the soil can induce bridging adsorption between APAM polymer segments and a particle surface), can enable engineers and researchers to utilise chemical improvement design and to consider suitable actions (e.g., by injecting APAM solution into a frost susceptible soil or using APAM-modified soil to replace the frost susceptible soil) to prevent frost heave from having a negative impact on traffic roads and buildings in cold regions.

A frequentist approach to dynamic borrowing.

There has been growing interest in leveraging external control data to augment a randomized control group data in clinical trials and enable more informative decision making. In recent years, the quality and availability of real-world data have improved steadily as external controls. However, information borrowing by directly pooling such external controls with randomized controls may lead to biased estimates of the treatment effect. Dynamic borrowing methods under the Bayesian framework have been proposed to better control the false positive error. However, the numerical computation and, especially, parameter tuning, of those Bayesian dynamic borrowing methods remain a challenge in practice. In this paper, we present a frequentist interpretation of a Bayesian commensurate prior borrowing approach and describe intrinsic challenges associated with this method from the perspective of optimization. Motivated by this observation, we propose a new dynamic borrowing approach using adaptive lasso. The treatment effect estimate derived from this method follows a known asymptotic distribution, which can be used to construct confidence intervals and conduct hypothesis tests. The finite sample performance of the method is evaluated through extensive Monte Carlo simulations under different settings. We observed highly competitive performance of adaptive lasso compared to Bayesian approaches. Methods for selecting tuning parameters are also thoroughly discussed based on results from numerical studies and an illustration example.

Placental Mesenchymal Stem Cells Alleviate Podocyte Injury in Diabetic Kidney Disease by Modulating Mitophagy via the SIRT1-PGC-1alpha-TFAM Pathway.

The use of mesenchymal stem cells (MSCs) has become a new strategy for treating diabetic kidney disease (DKD). However, the role of placenta derived mesenchymal stem cells (P-MSCs) in DKD remains unclear. This study aims to investigate the therapeutic application and molecular mechanism of P-MSCs on DKD from the perspective of podocyte injury and PINK1/Parkin-mediated mitophagy at the animal, cellular, and molecular levels. Western blotting, reverse transcription polymerase chain reaction, immunofluorescence, and immunohistochemistry were used to detect the expression of podocyte injury-related markers and mitophagy-related markers, SIRT1, PGC-1α, and TFAM. Knockdown, overexpression, and rescue experiments were performed to verify the underlying mechanism of P-MSCs in DKD. Mitochondrial function was detected by flow cytometry. The structure of autophagosomes and mitochondria were observed by electron microscopy. Furthermore, we constructed a streptozotocin-induced DKD rat model and injected P-MSCs into DKD rats. Results showed that as compared with the control group, exposing podocytes to high-glucose conditions aggravated podocyte injury, represented by a decreased expression of Podocin along with increased expression of Desmin, and inhibited PINK1/Parkin-mediated mitophagy, manifested as a decreased expression of Beclin1, the LC3II/LC3I ratio, Parkin, and PINK1 associated with an increased expression of P62. Importantly, these indicators were reversed by P-MSCs. In addition, P-MSCs protected the structure and function of autophagosomes and mitochondria. P-MSCs increased mitochondrial membrane potential and ATP content and decreased the accumulation of reactive oxygen species. Mechanistically, P-MSCs alleviated podocyte injury and mitophagy inhibition by enhancing the expression of the SIRT1-PGC-1α-TFAM pathway. Finally, we injected P-MSCs into streptozotocin-induced DKD rats. The results revealed that the application of P-MSCs largely reversed the markers related to podocyte injury and mitophagy and significantly increased the expression of SIRT1, PGC-1α, and TFAM compared with the DKD group. In conclusion, P-MSCs ameliorated podocyte injury and PINK1/Parkin-mediated mitophagy inhibition in DKD by activating the SIRT1-PGC-1α-TFAM pathway.

Cache-Based Privacy Protection Scheme for Continuous Location Query.

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.

Effects of Carbon Tax on Urban Carbon Emission Reduction: Evidence in China Environmental Governance.

Carbon tax is an important economic instrument in achieving the goal of carbon emission reduction and sustainable development. This paper investigates the effects of carbon tax on carbon emission reduction in China. First, a non-competitive input-output table for Carbon Emissions of 28 sectors in China after Carbon Tax was established, based on the "2018 China Non-competitive Input-Output Table (42 Sectors)" and the carbon emission data of sectors provided by China Carbon Emission Accounts and Datasets (CEADs). Then, an input-output price model was established to study the changes on product price, GDP, employment, and carbon dioxide emissions of 28 sectors after carbon taxing ranged from 10 to 200. When the carbon tax rate reaches 200 yuan/ton, the inflation rate will be 5.907%, the total GDP will be decreased to 1.910%, the total labor force will be decreased to 1.744%, and the total carbon emission reduction rate will be increased to 8.171%. Results showed that with the increases in carbon tax, the inflation rate was increased, the rate of carbon emission reduction was increased, and the negative effects on GDP and employment were also increased. Suggestions on policy making, such as combination of carbon taxing and carbon trading, dynamic adjustment mechanism, tax neutrality, and forcing active carbon reduction, were proposed to minimize the adverse effect of levying carbon tax. The results from this paper would provide a reference for the policy making on carbon management.

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA.

Preventive and synbiotic effects of the soluble dietary fiber obtained from Lentinula edodes byproducts and Lactobacillus plantarum LP90 against dextran sulfate sodium-induced colitis in mice.

BACKGROUND: Soluble dietary fiber (SDF) obtained from Lentinula edodes byproducts has beneficial effects on human intestinal health. This study aimed to examine the combined preventive and ameliorative effects of a kind of synbiotic (SDF with a molecular weight of 1.58 × 102 kDa and Lactobacillus plantarum LP90 (LP) at 1 × 109 CFU kg-1 ) on dextran sulfate sodium-induced colitis mice. RESULTS: The results demonstrated that synbiotic treatment could alleviate weight loss, decrease the disease activity index level and cause histological amelioration. Synbiotic treatment also promoted the production of goblet cells, increased the expression of tight junction proteins, and adjusted the production of myeloperoxidase, malondialdehyde and superoxide dismutase to repair intestinal epithelial injury. Clinical symptoms were alleviated by maintaining Th17/Treg balance, increasing interleukin 10 and immunoglobulin A levels, reducing interleukin 17a and tumor necrosis factor α production, and promoting mRNA to highly express of Foxp3 and vitamin D receptors. Moreover, synbiotic treatment could upregulate butyric acid production (4.71 ± 0.46 mol g-1 feces, P < 0.05) and diversity of intestinal microbial to maintain intestinal homeostasis. CONCLUSION: This study suggested that the combination of LP and SDF as a synbiotic has the potential for use as a nutritional supplement to alleviate colitis. © 2022 Society of Chemical Industry.