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Human gut Bacteroides and Parabacteroides species play crucial roles in human health and are known for their capacity to utilize diverse polysaccharides. Understanding how these bacteria utilize medicinal polysaccharides is foundational for developing polysaccharides-based prebiotics and drugs. Here, we systematically mapped the utilization profiles of 20 different medicinal polysaccharides by 28 human gut Bacteroides and Parabacteroides species. The growth profiles exhibited substantial variation across different bacterial species and medicinal polysaccharides. Ginseng polysaccharides promoted the growth of multiple Bacteroides and Parabacteroides species; in contrast, Dendrobium polysaccharides selectively promoted the growth of Bacteroides uniformis . This distinct utilization profile was associated with genomic variation in carbohydrate-active enzymes, rather than monosaccharides composition variation among medicinal polysaccharides. Through comparative transcriptomics and genetical manipulation, we validated that the polysaccharide utilization locus PUL34_ Bu enabled Bacteroides uniformis to utilize Dendrobium polysaccharides (i.e. glucomannan). In addition, we found that the GH26 enzyme in PUL34_ Bu allowed Bacteroides uniformis to utilize multiple plant-derived mannan. Overall, our results revealed the selective utilization of medicinal polysaccharide by Bacteroides and Parabacteroides species and provided insights into the use of polysaccharides in engineering the human gut microbiome. Here, the authors characterize the utilization of 20 medicinal polysaccharides by 28 human gut Bacteroides and Parabacteroides species, revealing substantial variability in bacterial growth responses, which they link to genomic differences in carbohydrate-active enzymes.

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual’s response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.

Abstract Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual’s response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin compared to the non-fermentable fiber cellulose. We found that inulin produced generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the timeseries data, which revealed a group of bacteria whose growth significantly increases in response to inulin. and whose baseline abundance and interspecies competition explains the baseline-dependence of microbiome density and community composition dynamics. Fecal levels of of SCFAs, such as propionate, is associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impedes the prediction of SCFAs by machine learning models. Finally, we showed that our methods and major findings are generalizable to dietary resistant starch. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions. Competing Interest Statement The authors have declared no competing interest.

As the operational status of aircraft engines evolves, their fault modes also undergo changes. In response to the operational degradation trend of aircraft engines, this paper proposes an aircraft engine fault diagnosis model based on 1DCNN-BiLSTM with CBAM. The model can be directly applied to raw monitoring data without the need for additional algorithms to extract fault degradation features. It fully leverages the advantages of 1DCNN in extracting local features along the spatial dimension and incorporates CBAM, a channel and spatial attention mechanism. CBAM could assign higher weights to features relevant to fault categories and make the model pay more attention to them. Subsequently, it utilizes BiLSTM to handle nonlinear time feature sequences and bidirectional contextual feature information. Finally, experimental validation is conducted on the publicly available CMAPSS dataset from NASA, categorizing fault modes into three types: faultless, HPC fault (the single fault), and HPC&Fan fault (the mixed fault). Comparative analysis with other models reveals that the proposed model has a higher classification accuracy, which is of practical significance in improving the reliability of aircraft engine operations and for Remaining Useful Life (RUL) prediction.

A complex operating environment and high operating temperature lead to the uneven temperature field distribution of key components of the molten salt Linear Fresnel collector in a way that compromises the collector’s safety and stability. To investigate the influence of different working conditions on the temperature field of the molten salt Linear Fresnel collector under multi-physical field conditions, this study develops a three-dimensional numerical model based on ANSYS that integrates the loading of solar radiation and thermal–fluid coupling, compares and verifies the accuracy of the model through the collector field data of the actual operation, and systematically analyzes the distribution characteristics of the receiver tube and outlet temperature field and its rule of change. The results show that temperatures of the receiver tube and exit during operation exhibit pronounced non-uniform distribution characteristics, in which the inlet flow rate of the molten salt and intensity of solar irradiation have the most critical influence on the temperature distribution throughout the receiver tube and its exit, and the heat transfer temperature difference between the molten salt and heat conduit wall is reduced as the inlet temperature raises, which makes the receiver tube and molten salt outlet temperature gradient slightly reduced. This study not only supplements and improves the numerical simulation study of the molten salt Linear Fresnel collector under complex working conditions but also reveals the distribution law of the temperature field between the receiver tube and the outlet, which provides adequate numerical support for the safe and stable operation of the collector.

Performance prediction based on candidates and screening based on predicted performance value are the core of product development. For example, the performance prediction and screening of equipment components and parts are an important guarantee for the reliability of equipment products. The prediction and screening of drug bioactivity value and performance are the keys to pharmaceutical product development. The main reasons for the failure of pharmaceutical discovery are the low bioactivity of the candidate compounds and the deficiencies in their efficacy and safety, which are related to the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the compounds. Therefore, it is very necessary to quickly and effectively perform systematic bioactivity value prediction and ADMET property evaluation for candidate compounds in the early stage of drug discovery. In this paper, a data-driven pharmaceutical products screening prediction model is proposed to screen drug candidates with higher bioactivity value and better ADMET properties. First, a quantitative prediction method for bioactivity value is proposed using the fusion regression of LGBM and neural network based on backpropagation (BP-NN). Then, the ADMET properties prediction method is proposed using XGBoost. According to the predicted bioactivity value and ADMET properties, the BVAP method is defined to screen the drug candidates. And the screening model is validated on the dataset of antagonized Erα active compounds, in which the mean square error (MSE) of fusion regression is 1.1496, the XGBoost prediction accuracy of ADMET properties are 94.0% for Caco-2, 95.7% for CYP3A4, 89.4% for HERG, 88.6% for hob, and 96.2% for Mn. Compared with the commonly used methods for ADMET properties such as SVM, RF, KNN, LDA, and NB, the XGBoost in this paper has the highest prediction accuracy and AUC value, which has better guiding significance and can help screen pharmaceutical product candidates with good bioactivity, pharmacokinetic properties, and safety.

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand–supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy’s current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.

Background The incidence rate of stroke or cerebrovascular accidents ranks first in China. More than 85% of stroke patients have residual upper limb motor dysfunction, especially hand dysfunction. Normalizing the rehabilitation evaluation process and standard quantitative evaluation method is a complex and key point in rehabilitation therapy. The study aimed to establish a function model based on the Bayes discriminant by measuring the thenar stiffness with shear wave elastography (SWE) to quantitatively evaluate the hand motor function of hemiplegic patients after stroke. Methods This study collected 60 patients diagnosed with hemiplegia after stroke from October 2021 to October 2022. Therapists used the Brunnstrom assessment (BA)scale to divide the patients into the stage. All the patients underwent the measurement of SWE examination of abductor pollicis brevis (APB), opponens pollicis (OP), flexor pollicis long tendon (FPLT), and flexor pollicis brevis (FPB) by two sonographers. The SWE change rate of four parts of the thenar area was calculated prospectively with the non-hemiplegic side as the reference, the function equation was established by the Bayes discriminant method, and the evaluation model was fitted according to the acquired training set data. Lastly, the model was verified by self-validation, cross-validation, and external data validation methods. The classification performance was evaluated regarding the area under the ROC curve (AUC), sensitivity, and specificity. Results The median SWE values of the hemiplegic side of patients were lower than those of the non-hemiplegic side. According to the BA stage and SWE R of APB, OP, FPLT, and FPB, our study established the Bayes discriminative model and validated it via self-validation and cross-validation methods. Then, the discriminant equation was used to validate 18 patients prospectively, the diagnostic coincidence rate was about 78.8%, and the misjudgment rate was approximately 21.2%. The AUC of the discriminant model for diagnosing BA stage I-VI was 0.928(95% CI: 0.839-1.0),0.858(95% CI: 0.748–0.969),1.0(95% CI: 1.0–1.0), 0.777(95% CI: 0.599–0.954),0.785(95% CI: 0.593–0.977) and 0.985(95% CI: 0.959-1.0), respectively. Conclusion This Bayes discriminant model built by measuring thenar stiffness was of diagnostic value and can provide an objective basis for evaluating clinical rehabilitation.

Background Carotid vulnerable plaque is an important risk factor for stroke occurrence and recurrence. However, the relationship between risk parameters related to carotid vulnerable plaque (plaque size, echogenicity, intraplaque neovascularization, and plaque stiffness) and neurological outcome after ischemic stroke or TIA is unclear. This study investigates the value of multimodal ultrasound-based carotid plaque risk biomarkers to predict poor short-term functional outcome after ischemic stroke or TIA. Methods This study was a single-center, prospective, continuous, cohort study to observe the occurrence of adverse functional outcomes (mRS 2–6/3–6) 90 days after ischemic stroke or TIA in patients, where the exposure factors in this study were carotid plaque ultrasound risk biomarkers and the risk factors were sex, age, disease history, and medication history. Patients with ischemic stroke or TIA (mRS ≤3) whose ipsilateral internal carotid artery stenosis was ≥50% within 30 days were included. All patients underwent multimodal ultrasound at baseline, including conventional ultrasound, superb microvascular imaging (SMI), and shear wave elastography (SWE). Continuous variables were divided into four groups at interquartile spacing for inclusion in univariate and multifactorial analyses. After completion of a baseline ultrasound, all patients were followed up at 90 days after ultrasound, and patient modified neurological function scores (mRSs) were recorded. Multivariate Cox regression and ROC curves were used to assess the risk factors and predictive power for predicting poor neurological function. Results SMI revealed that 20 (30.8%) patients showed extensive neovascularization in the carotid plaque, and 45 (69.2%) patients showed limited neovascularization in the carotid plaque. SWE imaging showed that the mean carotid plaque stiffness was 51.49 ± 18.34 kPa (23.19–111.39 kPa). After a mean follow-up of 90 ± 14 days, a total of 21 (32.3%) patients had a mRS of 2–6, and a total of 10 (15.4%) patients had a mRS of 3–6. Cox regression analysis showed that the level of intraplaque neovascularization and plaque stiffness were independent risk factors for a mRS of 2–6, and the level of intraplaque neovascularization was an independent risk factor for a mRS of 3–6. After correcting for confounders, the HR of intraplaque neovascularization level and plaque stiffness predicting a mRS 2–6 was 3.06 (95% CI 1.05–12.59, P  = 0.041) and 0.51 (95% CI 0.31–0.83, P  = 0.007), respectively; the HR of intraplaque neovascularization level predicting a mRS 3–6 was 6.11 (95% CI 1.19–31.45, P  = 0.031). For ROC curve analysis, the mRSs for intraplaque neovascularization level, plaque stiffness, and combined application to predict 90-day neurological outcome ranged from 2 to 6, with AUCs of 0.73 (95% CI 0.59–0.87), 0.76 (95% CI 0.64–0.89) and 0.85 (95% CI 0.76–0.95), respectively. The mRSs for the intraplaque neovascularization level to predict 90-day neurological outcome ranged from 3 to 6, with AUCs of 0.79 (95% CI 0.63–0.95). Conclusion Intraplaque neovascularization level and plaque stiffness may be associated with an increased risk of poor short-term functional outcome after stroke in patients with recent anterior circulation ischemic stroke due to carotid atherosclerosis. The combined application of multiple parameters has efficacy in predicting poor short-term functional outcome after stroke.

Background The aim of this study is to propose a new TI-RADS and compare it with the American College of Radiology (2017 ACR)-TI-RADS and the 2020 Chinese (2020 C)-TI-RADS. Methods A retrospective analysis of 749 thyroid nodules was performed. Based on the calculated odds ratio of ultrasonic signs between benign and malignant nodules, a new thyroid nodule score and malignancy rate were calculated. A receiver operating characteristic curve was drawn to analyze the new system’s effectiveness in the differential diagnosis of benign and malignant thyroid nodules and was compared with the 2020 C-TI-RADS and 2017 ACR-TI-RADS. Five ultrasound physicians with different qualifications graded another 123 thyroid nodules according to the 2017ACR-TI-RADS, 2020 C-TI-RADS, and the newly proposed TI-RADS. Intergroup and intragroup consistency was evaluated using the Kappa test and intraclass correlation coefficient (ICC) test. Results 1) The new thyroid nodule score was divided into 0, 1, 2, 3, 4, and 5 points, with malignancy rates of 1.52%, 7.69%, 38.24%, 76.00%, 90.75%, and 93.75%, respectively. Using 3 points as the cutoff value to diagnose benign and malignant thyroid nodules, the sensitivity and specificity were 94.03% and 67.39%, respectively, which were higher than those of the 2017 ACR-TI-RADS and 2020 C-TI-RADS. The simplified TI-RADS, namely, sTI-RADS, was established as follows: sTI-RADS 3 (0 points), malignancy rate < 2%; sTI-RADS 4a (1 point), malignancy rate 2–10%; sTI-RADS 4b (2 points), malignancy rate 10–50%; sTI-RADS 4 (3 points), malignancy rate 50–90%; and sTI-RADS 5 (4 and 5 points), malignancy rate > 90%. 2) Five ultrasound doctors graded thyroid nodules by the 2017 ACR-TI-RADS, 2020C-TI-RADS and sTI-RADS. Intragroup consistency was good among all tests; ICC were 0.86 (0.82–0.90), 0.84 (0.78–0.88), and 0.88 (0.84–0.91), respectively, while only sTI-RADS had good intergroup consistency. Conclusion In summary, we proposed a new TI-RADS, namely, sTI-RADS, which was obtained using a simple assignment method with higher specificity, accuracy, positive predictive value, and Youden index than the 2017 ACR-TI-RADS and 2020 C-TI-RADS.