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Listeria monocytogenes (L. monocytogenes) is a foodborne pathogen that causes listeriosis in humans and other animals. Surface proteins with the LPXTG motif have important roles in the virulence of L. monocytogenes. Lmo0159 is one such protein, but little is known about its role in L. monocytogenes virulence, motility, and biofilm formation. Here, we constructed and characterized a deletion mutant of lmo0159 (∆lmo0159). We analyzed not only the capacity of biofilm formation, motility, attachment, and intracellular growth in different cell types but also LD50; bacterial load in mice’s liver, spleen, and brain; expression of virulence genes; and survival time of mice after challenge. The results showed that the cross-linking density of the biofilm of ∆lmo0159 strain was lower than that of WT by microscopic examination. The expression of biofilm-formation and virulence genes also decreased in the biofilm state. Subsequently, the growth and motility of ∆lmo0159 in the culture medium were enhanced. Conversely, the growth and motility of L. monocytogenes were attenuated by ∆lmo0159 at both the cellular and mouse levels. At the cellular level, ∆lmo0159 reduced plaque size; accelerated scratch healing; and attenuated the efficiency of adhesion, invasion, and intracellular proliferation in swine intestinal epithelial cells (SIEC), RAW264.7, mouse-brain microvascular endothelial cells (mBMEC), and human-brain microvascular endothelial cells (hCMEC/D3). The expression of virulence genes was also inhibited. At the mouse level, the LD50 of the ∆lmo0159 strain was 100.97 times higher than that of the WT strain. The bacterial load of the ∆lmo0159 strain in the liver and spleen was lower than that of the WT strain. In a mouse model of intraperitoneal infection, the deletion of the lmo0159 gene significantly prolonged the survival time of the mice, suggesting that the lmo0159 deletion mutant also exhibited reduced virulence. Thus, our study identified lmo0159 as a novel virulence factor among L. monocytogenes LPXTG proteins.

A bullet point cloud registration algorithm with a low overlap rate based on line feature detection was proposed to solve the problem of the difficulty and low efficiency of point cloud registration due to the low overlap rate among point clouds sampled by the bullet model. In this paper, voxel downsampling is used to remove some noise points and outliers from the bullet point cloud and applied to the specified resolution to reduce the calculation cost. The bullet point cloud is transformed to a better initial position by fitting the central axis with the geometrical features of the bullet. Then, the direction vector of the bullet linear features is obtained by using an icosahedral fitting discrete Hough transform to simplify the parameter space of the search transformation. Finally, the optimal rotation angle is searched for in the parameter space by using the improved Cuckoo algorithm to realize the registration of the bullet point cloud with a low overlap rate. Simulation and experimental results show that the proposed registration method can accurately register bullet point clouds of different densities with a low overlap rate. Compared with the commonly used ICP, GICP, and TRICP algorithms, the registration error of the proposed algorithm is reduced by 92.68% on average when the overlap rate is 52.85%. The registration error is reduced by 98.87% in the case of a 41.36% overlap rate, by 99.52% in the case of a 33.02% overlap rate, and by 98.89% in the case of a 22.75% overlap rate.

Metabolic syndrome (MS) has emerged as one of the major global health concerns, accompanied by a series of related complications, such as obesity and type-2 diabetes. The gut-liver axis (GLA) is a bidirectional communication between the gut and the liver. The GLA alterations have been revealed to be closely associated with the development of MS. Probiotics within Lactobacillus and Bifidobacterium confer beneficial effects on improving MS symptoms. WHHPRO™ is a mixture of four probiotic strains, with potential MS-improving abilities. This study aimed to investigate the effects of WHHPRO™ on MS symptoms using a high-fat diet (HFD) rat model. Oral administration of WHHPRO™ for 12 weeks improved glucose tolerance, blood lipid, body weight, and liver index in HFD rats. WHHPRO™ shaped the gut microbiome composition by increasing the abundance of Lactobacillus and Akkermansia and normalized the reduced SCFA levels in HFD rats. Besides, WHHPRO™ modulated the fecal bile acids (BAs) profile, with decreased levels of T-b-MCA and 12-KDCA and increased levels of LCA and ILCA. Meanwhile, WHHPRO™ increased total unconjugated BAs in feces and liver and reduced the accumulation of total hepatic BA pool size in HFD rats. Moreover, WHHPRO™ reversed the expression of genes associated with impaired BA metabolism signaling in the ileum and liver. Our findings suggest that WHHPRO™ exerted beneficial effects on improving MS symptoms, involving the modulation of the gut microbiome composition, SCFAs, and the FXR-FGF15 signaling along the GLA. Supplementation of WHHPRO™ may serve as a novel strategy for improving MS symptoms.

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

In Phi29-α–hemolysin (α-HL) nanopore sequencing systems, a strong electrochemical signal is dependent on a high concentration of salt. However, high salt concentrations adversely affect polymerase activity. Sequencing by synthesis (SBS) requires the use of phi29 polymerase without exonuclease activity to prevent the degradation of modified nucleotide tags; however, the lack of exonuclease activity also affects polymerase processivity. This study aimed to optimize phi29 polymerase for improved salt tolerance and processivity while maintaining its lack of exonuclease activity to meet the requirements of nanopore sequencing. Using salt tolerance compartmentalized self-replication (stCSR) and a microfluidic platform, we obtained 11 mutant sites with enhanced salt tolerance attributes. Sequencing and biochemical analyses revealed that the substitution of conserved amino acids such as G197D, Y369E, T372N, and I378R plays a critical role in maintaining the processivity of exonuclease-deficient phi29 polymerase under high salt conditions. Furthermore, Y369E and T372N have been identified as important determinants of DNA polymerase binding affinity. This study provides insights into optimizing polymerase processability under high-salt conditions for real-time polymerase nanopore sequencing, paving the way for improved performance and applications in nanopore sequencing technologies.

Sensors are widely used to acquire biological and environmental information for medical diagnosis, and health and environmental monitoring. Graphene is a promising new sensor material that has been widely used in sensor fabrication in recent years. Compared with many other existing graphene preparation methods, laser-scribed graphene (LSG) is simple, low-cost, environmentally friendly, and has good conductivity and high thermal stability, making it widely used in the sensor field. This paper summarizes existing LSG methods for sensor fabrication. Primary LSG preparation methods and their variants are introduced first, followed by a summary of LSG modification methods designed explicitly for sensor fabrication. Subsequently, the applications of LSG in stress, bio, gas, temperature, and humidity sensors are summarized with a particular focus on multifunctional integrated sensors. Finally, the current challenges and prospects of LSG-based sensors are discussed.

Multi-exciton generation by multi-photon absorption under low-energy photons can be thought a reasonable method to reduce the risk of optical damage, especially in photoelectric quantum dot (QD) devices. The lifetime of the multi-exciton state plays a key role in the utilization of photon-induced carriers, which depends on the dynamics of the exciton generation process in materials. In this paper, the exciton generation dynamics of the photon absorption under low-frequency light in CdSe QDs are successfully detected and studied by the temporal resolution transient absorption (TA) spectroscopy method. Since the cooling time of hot excitons extends while the rate of auger recombination is accelerated when incident energy is increased, the filling time of defect states is irregular, and exciton generation experiences a transition from single-photon absorption to multi-photon absorption. This result shows how to change the excitation. Optical parameters can prolong the lifetime of excitons, thus fully extracting excitons and improving the photoelectric conversion efficiency of QD optoelectronic devices, which provides theoretical and experimental support for the development of QD optoelectronic devices.

The prediction of mechanical properties of cold-rolled steel is very important for the quality control, process optimization, and cost control of cold-rolled steel, but it is still a challenging task to predict accurately. For the existing graph structure of graph attention networks, it is difficult to effectively establish the complex coupling relationship and nonlinear causal relationship between variables. At the same time, it is considered that the process of cold-rolled steel has typical full-flow process characteristics and the graph attention network makes it difficult to extract the path information between the central node and its higher-order neighborhood. The neural Granger causality algorithm is used to extract the latent relationship between variables, and the basic graph structure of mechanical property prediction data is constructed. Secondly, the node embedding layer is added before the graph attention network, which leverages the symmetry nature of Node2vec method by incorporating both breadth-first and depth-first exploration strategies. This ensures a balanced exploration of diverse paths in the graph, capturing not only local structures but also higher-order relationships. The combined graph attention networks are then able to effectively capture the symmetry path information between nodes and dependencies between variables. The accuracy and superiority of this method are verified by experiments in real cold-rolled steel production cases.

One of the most effective therapeutic proposals for type 2 diabetes mellitus (T2DM) is regulation of postprandial blood glucose (PBG) level by inhibiting α-glucosidases. In this study, we firstly investigated the influence of ginsenoside Rg1 (Rg1) on activity of α-glucosidase in vitro. The results revealed that Rg1 had a reversible inhibition on α-glucosidase activity in an uncompetitive manner. Fluorescence spectrum and circular dichroism (CD) analysis indicated that the stable complex between Rg1 and α-glucosidase was mainly formed by hydrogen bonds and hydrophobic forces. In addition, the anti-hyperglycemic effect of Rg1 was confirmed by the sucrose tolerance test in normal and db/db mice. Caco-2 monolayer cell model further emphasized that decreasing effect of Rg1 on PBG may be achieved by reducing glucose uptake and transport which is accounted for inhibition of α-glucosidase. Taken together, those results suggested that Rg1 may be used as a promising α-glucosidase inhibitor to decrease PBG.

To assess the probiotic potential of strain 68-1 with rutin conversion capabilities, isolated from Chinese traditional Jiangshui, a complete genomic analysis and in vitro tests were conducted. The Oxford Nanopore Technologies (ONT, Oxford, UK)–Illumina (San Diego, CA, USA) hybrid sequencing platform was used for whole genome sequencing and the results showed that strain 68-1 had a chromosome sequence of 3,482,151 bp, with 46.53% GC content and five plasmids with a sequence length ranging from 2009 bp to 48,711 bp. Strain 68-1 was identified as Lactiplantibacillus pentosus based on the whole genome sequence. A total of 133 genes encoding for carbohydrate-active enzymes (CAZymes) were identified and genes that may be involved in rutin conversion were found in the L. pentosus 68-1 genome. L. pentosus 68-1 showed excellent tolerance to gastrointestinal juice and adhesion properties, and corresponding genes were identified. In addition, L. pentosus 68-1 exhibited strong antibacterial and antifungal activity, where organic acids may play a crucial role in its antagonistic ability. Moreover, the gene cluster for plantaricin_EF production was detected. No high virulence factor was found in the L. pentosus 68-1 genome and no hemolytic effect was observed. In addition, L. pentosus 68-1 showed resistance to ampicillin, gentamycin, and kanamycin, and the genomic analysis indicated that horizontal ARG transfer should not be possible. The results show that L. pentosus 68-1 could be developed as a novel probiotic candidate to improve rutin bioavailability in the food and feed industry.

Ginsenoside CK, a kind of rare ginsenoside transformed from protopanaxadiol saponins extracted from the genus Panax, has been proven to possess favorable bioactivities such as anti-inflammatory, anti-cancer, anti-diabetes, and hepatoprotective effects. The current study is targeted to determine the effect of ginsenoside CK on hepatitis induced by concanavalin A (Con A). Mice were treated with different dosages of ginsenoside CK for 7 days, and Con A (15 mg/kg) was intravenously injected to induce autoimmune hepatitis (AIH) after the last administration. The results demonstrated that pretreatment with ginsenoside CK (40 mg/kg) could obviously ameliorate the increase in serum indicators related to liver function such as AST, ALT, and ALP, and hepatic lesions induced by Con A. Meanwhile, ginsenoside CK suppressed hepatocyte apoptosis, which was observed in pathological data, and immunoblotting results showed that the expression of Bax, Bcl-2, and other proteins was regulated by CK. Furthermore, the release of inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and IL-6 in mice with AIH were lowered by the administration of 40 mg/kg of ginsenoside CK. Furthermore, ginsenoside CK elevated the gene expression of Nrf2 and Sirt1 and augmented downstream target genes such as HO-1. In addition, a significant inhibition effect of the TLR4/NF-κB signal was observed in 40 mg/kg CK-pretreated mice compared with the model group. To sum up, the results indicated that ginsenoside CK has a notable hepatoprotective effect against AIH by activating Sirt1/Nrf2 and suppressing the TLR4/NF-κB signaling pathway.

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.

In this study, we aimed to explore the potential targets and functional mechanisms of Rk1 combined with Rg5 (Rk1+Rg5) against type II diabetes mellitus (T2DM). Network pharmacology and molecular docking were used to predict and verify the targets and signaling pathways of Rk1+Rg5 against T2DM. The results were further confirmed by a db/db mouse model and a model using PA-induced L6 cells. According to network pharmacology, a total of 250 core targets of Rk1+Rg5 towards T2DM were identified; the insulin resistance signaling pathways were enriched by KEGG. Results of molecular docking indicated good binding affinity of Rk1 and Rg5 to Akt1. In vivo and in vitro studies further showed that Rk1+Rg5 is an inhibitor of skeletal muscle insulin resistance. The results showed that Rk1+Rg5 significantly improved the hyperglycemic state of db/db mice, alleviated dyslipidemia, and promoted skeletal muscle glucose uptake. This phenomenon was closely related to the alleviation of the insulin resistance in skeletal muscles. Finally, the combination activated the Akt signaling pathway and promoted GLUT4 translocation to the cell membrane for glucose uptake. Altogether, our findings, for the first time, demonstrate that the combination of Rk1 and Rg5 could be beneficial for anti-T2DM, possibly involving ameliorated insulin resistance.

Objective. The central mechanism of acupuncture for primary dysmenorrhea was explored by summarizing the changes in different regional networks of the brain induced by acupuncture stimulation by analyzing the existing studies. Methods. The original studies were collected and selected from three English databases such as PubMed and four Chinese databases as China Knowledge Network (CNKI). The main keyword clusters are neuroimaging, acupuncture, and primary dysmenorrhea. Results. The literature review yielded 130 possibly qualified studies, and 23 articles fulfilled the criteria for inclusion. Regarding the type of acupuncture studies, 6 moxibustion studies and 17 manual acupuncture studies for primary dysmenorrhea were included. Based on functional magnetic resonance imaging (fMRI), perfusion-weighted imaging (PWI), and positron emission tomography-computer tomography techniques (PET-CT), one or more analysis methods such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC), and independent components analysis (ICA) were used. The results are summarized. To summarize the high-frequency brain area alterations observed in patients with acupuncture-induced primary dysmenorrhea were the anterior cingulate gyrus, thalamus, insula, precentral gyrus, middle frontal gyrus, postcentral gyrus, putamen, and cerebellum. Conclusion. The results suggest that the mechanism of acupuncture in the treatment of primary dysmenorrhea is the involvement of networks regulating different areas of the brain in the analgesic effects of acupuncture. The brain regions involved in primary dysmenorrhea acupuncture analgesia were mainly located in the pain matrix, default mode network, salience network, and limbic system.

Background The number of people suffering from dementia is increasing rapidly in China. Early identification, referral, and intervention for dementia patients within communities are important to public health. However, these measures could be impacted by misconceptions about dementia and associated psychiatric stigma from community health professionals and relatives of dementia patients. Methods A cross-sectional survey was conducted on 249 participants, which included community doctors, community nurses, and relatives of diagnosed and suspected dementia patients in Guiyang, China. Participants were recruited through convenient sampling. The Chinese version of Dementia Knowledge Assessment Scale (DKAS) and the Perceived Psychiatric Stigma Scale (PPSS) were used to evaluate the participants’ knowledge of dementia and dementia-related psychiatric stigma. Results A total of 249 participants completed the questionnaire. The participants had moderate overall knowledge of dementia and the associated psychiatric stigma. Participants who were ≥45 years old, had a low level of education, had a low monthly income, or gained knowledge of dementia through non-media channels had lower awareness of dementia and stronger psychiatric stigma. In the “Communication & behavior” subscale of DKAS, all participants had a low level of awareness. Relatives of diagnosed and suspected dementia patients had higher total PPSS and “Marital preclusion” subscale scores than community doctors and nurses but lower psychiatric stigma based on the PPSS “Self-deprecation” subscale score. Conclusions Despite their profession, community doctors and nurses did not show an absolute advantage over relatives of diagnosed and suspected dementia patients in the dementia knowledge, and they even showed higher psychiatric stigma in some subscales. The self-deprecation subscale is related to the identification with negative labels such as “people with a mental illness are the weak”. This study shows that reducing stigma on the “Self-deprecation” subscale should be a core component of training and educational programs targeted at improving dementia knowledge among community health service providers.

Starting from the perspective of social perception of voluntary employee green behavior (VEGB) and studies on the attribution of VEGB, we explore the phenomenon that employees can show different perceptions and behavioral responses to VEGB according to their attribution to VEGB. We served to examine the hypotheses. The results of a two-wave study show that when employees believe VEGB is motivated by instrumental concerns, VEGB is more likely to evoke a low level of warmth and competence, which produces less green advocacy. However, if employees believe VEGB is motivated by moral reasons, VEGB is more likely to prompt more warmth and competence perceptions and elicit greener advocacy from employees. In addition, theoretical and practical contributions are discussed.

To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm can easily fall into premature convergence, local optimum and destroy the optimal solution. Furthermore, the symbiotic organisms search (SOS) strategy can be introduced, which integrates the mutualism strategy and commensalism strategy to propose a new migration operator. To address the problem that the optimal solution is easily destroyed, a parasitic natural enemy insect mechanism is introduced, and predator mutation and parasitic mutation strategies with symmetry are defined, which can be guided according to the iterative characteristics of the population. By comparing with eight multi-objective benchmark test functions with four multi-objective algorithms, the results show that the algorithm outperforms other comparative algorithms in terms of the convergence of the solution set and the uniformity of distribution. Finally, the algorithm is applied to multi-objective, flexible job-shop scheduling (FJSP) to test its practical application value, and it is shown through experiments that the algorithm is effective in solving the multi-objective FJSP problem.

To address the problems of the single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when the teaching–learning-based optimization (TLBO) algorithm solves the no-wait flow-shop-scheduling problem, the multi-strategy discrete teaching–learning-based optimization algorithm (MSDTLBO) is introduced. Considering the differences between individuals, the algorithm is redefined from the student’s point of view, giving the basic integer sequence encoding. To address the fact that the algorithm is prone to falling into local optimum and to leading to a reduction in search accuracy, the population was divided into three groups according to the learning ability of the individuals, and different teaching strategies were adopted to achieve the effect of teaching according to their needs. To improve the destruction-and-reconstruction process with symmetry, an iterative greedy algorithm of destruction–reconstruction was used as the main body, and a knowledge base was used to control the number of meaningless artifacts to be destroyed and to dynamically change the artifact-selection method in the destruction process. Finally, the algorithm was applied to the no-wait flow-shop-scheduling problem (NWFSP) to test its practical application value. After comparing twenty-one benchmark test functions with six algorithms, the experimental results showed that the algorithm has a certain effectiveness in solving NWFSP.

Climate change is an existential threat to the vast global permafrost domain. The diverse human cultures, ecological communities, and biogeochemical cycles of this tenth of the planet depend on the persistence of frozen conditions. The complexity, immensity, and remoteness of permafrost ecosystems make it difficult to grasp how quickly things are changing and what can be done about it. Here, we summarize terrestrial and marine changes in the permafrost domain with an eye toward global policy. While many questions remain, we know that continued fossil fuel burning is incompatible with the continued existence of the permafrost domain as we know it. If we fail to protect permafrost ecosystems, the consequences for human rights, biosphere integrity, and global climate will be severe. The policy implications are clear: the faster we reduce human emissions and draw down atmospheric CO2, the more of the permafrost domain we can save. Emissions reduction targets must be strengthened and accompanied by support for local peoples to protect intact ecological communities and natural carbon sinks within the permafrost domain. Some proposed geoengineering interventions such as solar shading, surface albedo modification, and vegetation manipulations are unproven and may exacerbate environmental injustice without providing lasting protection. Conversely, astounding advances in renewable energy have reopened viable pathways to halve human greenhouse gas emissions by 2030 and effectively stop them well before 2050. We call on leaders, corporations, researchers, and citizens everywhere to acknowledge the global importance of the permafrost domain and work towards climate restoration and empowerment of Indigenous and immigrant communities in these regions.

Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R2 (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.

Mycophenolic acid (MPA) is an antimetabolic immunosuppressive drug widely used in solid organ transplantation and autoimmune diseases. Pharmacokinetics (PK) of MPA demonstrates high inter- and intra-variability. The aim of this study was to compare the population PK properties of MPA in adult renal transplant patients in the early and stable post-transplant stages and to simulate an optimal dosing regimen for patients at different stages. A total of 51 patients in the early post-transplant period (1 week after surgery) and 48 patients in the stable state (5.5–10 years after surgery) were included in the study. In the two-compartment population PK model, CL/F (23.36 L/h vs. 10.25 L/h) and V/F (78.07 vs. 16.24 L) were significantly different between the two stages. The dose-adjusted area under the concentration time curve (AUCss,12h/dose) for patients in the early stage were significantly lower than those for patients in the stable state (40.83 ± 22.26 mg h/L vs. 77.86 ± 21.34 mg h/L; p < 0.001). According to Monte Carlo simulations, patients with 1.0–1.5 g of mycophenolate mofetil twice daily in the early phase and 0.50–0.75 g twice daily in the stable phase had a high probability of achieving an AUCss,12h of 30–60 mg h/L. In addition, limited sampling strategies showed that two 4-point models (C0-C1-C2-C4 and C1-C2-C3-C6) performed well in predicting MPA exposure by both Bayesian estimate and regression equation and could be applied in clinical practice to assist therapeutic drug monitoring of MPA.

To promote carbon emission reduction and resource reuse, this study is devoted to studying the impact of government subsidy policies on the operation decision of a low-carbon closed-loop supply chain system. In the production stage, governments can use carbon reduction subsidies and remanufacturing subsidies to reduce the green behavior costs of manufacturers; in the recycling stage, governments can use consumer recycling subsidies to increase the recycling willingness of consumers. In this study, we introduce these three subsidy policies into the target function of the subsidized members. Using the Nash non-cooperative game and variational inequality, we developed a low-carbon supply chain network equilibrium model to examine the impact of subsidy policies on the system operation decision. The results show that, under the three subsidy policies, raising the subsidy level can help to improve the recycling rate, promote the reduction in carbon emissions and improve the profits of retailers; however, manufacturers’ incomes increase only when the carbon reduction subsidy level is below a certain threshold. Notably, under the coexistence of three subsidy policies, the carbon reduction subsidy policy has a more significant impact on the system. Finally, the numerical results show that, when the subsidy level is higher than six, although the increase in carbon reduction subsidy level leads to a decrease in the profits of manufacturers, this policy has the best contribution to the environmental benefits of the system. Our results can serve as guidelines for governments when designing the optimal subsidy programs to achieve the ultimate goal of establishing an environmentally friendly supply chain network system.

Experience in the clinical use of posaconazole (PCZ) in pediatric patients is limited, and no specific dose recommendations exist. This study aimed to investigate an appropriate dosing regimen, and assess the exposure-response relationships of PCZ in children. We reviewed the medical records of inpatients aged 12 years were 14.80, 14.52 and 12.90 mg/kg/day, respectively (p = 0.001); and for those receiving PCZ for treatment, the median doses were 23.50, 20.96 and 15.38 mg/kg/day, respectively (p = 0.001). Among children taking PCZ for prophylaxis, 12% developed a proven or probable breakthrough IFIs; the median PCZ concentrations were significantly lower than those children with successful treatment response (0.43 versus 1.20 μg mL−1; p < 0.001). 79.2% patients taking PCZ for treatment had a positive clinical response, and the median PCZ concentrations were significantly higher than those children with disease progression (1.06 versus 0.53 μg mL−1; p = 0.024). No association between Cmin values and hepatotoxicity was observed. Factors such as age, CRP, ALT and co-administration with proton pump inhibitors exhibited significant effects on PCZ Cmin. It is necessary to adjust the dosing regimens based on PCZ Cmin to individualize antifungal therapy and provide guidelines for dose adjustment in children.

In order to reduce the chattering caused by the discontinuity of the control function in the traditional sliding mode observer (SMO), this article proposes a sliding mode observer with phase-locked loop (PLL) to estimate the speed and position of the rotor. The back electromotive force (EMF) of a permanent magnet synchronous motor (PMSM) in a static coordinate system is accurately estimated by SMO, and then, PLL is constructed to combine the observed rotor position angle and back EMF to compensate the phase lag in angle estimation so as to obtain a more accurate speed. It solves the problems of poor robustness and complex algorithms in the traditional SMO prediction algorithm. The simulation results show that the SMO with PLL can effectively reduce the system chattering and effectively improve the accuracy of rotor speed and position estimation.

Polymyxin B is an effective but potentially nephrotoxic antibiotic that is commonly used to treat resistant Gram-negative infections. As a weight-based dosing drug, obese patients may be at a high risk of nephrotoxicity. However, the pharmacokinetics and dosing recommendations for this population are currently lacking. This study aimed to describe the polymyxin B population pharmacokinetics and to evaluate pharmacokinetic/pharmacodynamics (PK/PD) target attainment for obese patients. This study included 26 patients (body mass index, BMI >30) who received polymyxin B for ≥3 days. The total body weight (TBW) ranged from 75 to 125 kg, and the BMI ranged from 30.04 to 40.35. A two-compartment model adequately described the data using Phoenix NLME software. Monte Carlo simulation was used to assess polymyxin B exposure and the probability of target attainment (PTA). As a result, body weight had no significant effect on polymyxin B pharmacokinetics. According to model-based simulation, adjusted body weight (ABW)-based regimens had a high probability of achieving optimal exposure with minimal toxicity risk by comparing TBW and ideal body weight (IBW)-based regimens. The fixed dose of 125 mg or 150 mg q12h had a high toxicity risk. PTA results showed that TBW, IBW, and ABW-based regimens had similar PTA values. Therefore, for obese patients, ABW-based regimens but with a daily dose

To address the problem of high false positive/negative rate and low accuracy in protein complex detection, we propose the Dual-population Firefly Algorithm Based on Gender Differences (DFAGD) based on the unique core-attachment structure of protein complexes and the biological properties of fireflies. This method divides the detection of protein complexes into two phases. Firstly, a global search of the male population is used to detect the core proteins, and then a local search of the female population is used to detect the attachment proteins, which improves the accuracy of detection. In the male population strategy, the population diversity is redefined, and when the diversity falls below the threshold, a spring model is introduced to bring the population into the repulsion phase so that it does not fall into a local optimum. The female population selects elite and excellent individuals from the detection results of the male population to perform guided neighborhood searches, which can effectively improve detection accuracy. Finally, the effectiveness of the protein complex detection method is tested by comparing it to eight classical detection methods using four datasets of Saccharomyces cerevisiae proteins. [ABSTRACT FROM AUTHOR]

Cold rolling of steel is a complex nonlinear process, and there exist intricate spatiotemporal correlations among its process parameters, chemical composition, and mechanical properties. However, traditional long short-term memory networks cannot distinguish the importance of different input features and often only consider the temporal information during feature extraction, thus neglecting the spatial associations of the feature data. To better extract features from cold-rolled steel data and enhance the model's prediction accuracy, in this paper a fusion of improved multi-head attention modules is proposed. These modules enable the adaptive allocation of weights to different influencing factors, allowing the accurate distinction of the importance of different features. Additionally, a feed-forward neural network is introduced into the memory to further explore and extract the spatiotemporal features of the data. Experimental results on an industrial dataset demonstrate that the proposed model outperforms five other advanced models in terms of prediction performance. [ABSTRACT FROM AUTHOR]