Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23-45.

BACKGROUND: The relationship between sleep duration and physical fitness is one aspect of sleep health. Potential factors associated with sleep duration interfere with physical fitness performance, but the impact trends on physical fitness indicators remain unclear. METHODS: This study examined associations between sleep duration and physical fitness among young to middle-aged adults in Taiwan. A total of 42,781 Taiwanese adults aged 23-45 participated in the National Physical Fitness Examination Survey 2013 (NPFES-2013) in Taiwan between October 2013 and March 2014. A standardized structural questionnaire was used to record participants' sleep duration, which was stratified as short (< 6 h/day (h/d)), moderate (6-7 h/d; 7-8 h/d; 8-9 h), and long (≥ 9 h/d) sleep duration groups. Physical fitness was assessed based on four components: body composition (body mass index [BMI], waist-to-height ratio [WHtR], and waist-to-hip ratio [WHR]), muscle strength and endurance (1-min bent-leg sit-up test [BS]), flexibility (sit-and-reach test [SR]), and cardiorespiratory endurance index (3-min step test [CEI]). RESULTS: By using analysis of covariance (ANCOVA), after sex grouping and age adjustment, we observed that sleep duration was significantly associated with obesity, functional fitness, and self-perception of health. The sleep duration for low obesity-related values (BMI, WHtR, and WHR) for men was 7-9 h/d, and that for women was 7-8 h/d. Sleeping more than 8 h/d showed poor functional fitness performances (BS and SR). For both sexes, sleep duration of 8-9 h/d was the optimal sleep duration for self-perceptions of health. CONCLUSIONS: Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23-45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness.

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association.

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018-2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.

Removal of lead from two polluted soils by magnetic wheat straw biochars.

Due to high sorption capacity for heavy metals, magnetic biochar (MBC) has the potential to adsorb heavy metals in soils, which are then removed together with MBC from soils by a magnetic field. In this study, two magnetic biochars (MBC300 and MBC700) were derived from the magnetization of wheat straw biochars pyrolyzed at 300 and 700 °C. Strong binding of Pb with iron oxide particles deposited on biochar was observed. After the MBCs (7.5%, w/w) were applied to two naturally Pb-polluted soils (named as He-soil and Hu-soil) for 720 h, the removal efficiency of Pb from the soil by MBC300 (26.8-40.1%) was similar (p > 0.05) to that by MBC700 (25.1-42.1%). This is because MBC300 has lower sorption capacity for Pb but higher recovery percentage from soils as a result of lower saturation magnetization. The removal efficiencies of Pb by the two MBCs were 13-17% higher for He-soil than for Hu-soil, which was due to higher proportion of mobile forms of Pb in He-soil (82.3%) than in Hu-soil (51.5%). Spectroscopic analysis indicated that Pb in soils tended to bind onto the surface of MBC in more stable forms. Moreover, removing Pb from soils by MBC could decrease Pb concentration in ryegrass by about 30%. Therefore, it might be a potential method to remedy Pb-polluted soils.

Interaction between three marine microalgae and two phthalate acid esters.

Three marine microalgae were exposed to diethyl phthalate (DEP) and di-n-butyl phthalate (DBP) to investigate interactions between the algae and the pollutants. The 96-h 50% effect concentration (EC50) and no observed effect concentration (NOEC) based on cell density or chlorophyll a were in the order of Chaetoceros muelleri >Cylindrotheca closterium > Dunaliella salina for DEP, and C. closterium > C. muelleri > D. salina for DBP, respectively. Besides, EC50 value for C. closterium based on cell density increased over time in general, exhibiting strong adaptive ability to the pollutants. When singly existed, DEP was degraded more quickly at environmental relevant concentration (0.1 mg L-1) than at concentration of ≥EC20; DBP was degraded more quickly by C. closterium than by C. muelleri and D. salina. C. closterium was the most effective species to degrade the pollutants. When they were coexisted, degradation was inhibited by each other at environmental relevant concentration, while bioconcentration percentages of the pollutants increased, indicating that DEP-DBP mixture would be more problematic to marine organisms.

Phytoremediation of PAH-contaminated sediments with different organic matter contents by Potamogeton crispus L.

In this study, phytoremediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sediments with different contents of sediment organic matter (SOM) by Potamogeton crispus L. was investigated to find out the key factor controlling PAH dissipation enhancements. After a 36-day experiment, dissipation ratios of phenanthrene and pyrene in sediments were improved by P. crispus but decreased with increasing SOM content, no matter with or without P. crispus. Furthermore, the results of polyphenol oxidase activity and PAH bioavailability showed that they were both higher in planted treatments than in unplanted treatments. Finally, the enhancements of rapidly desorbing fraction (i.e., the main portion to be readily biodegradable) in sediments with SOM contents of 1.20%, 3.14%, and 5.08% by plants were 20.0%, 40.7%, and 66.7% for phenanthrene, 22.2%, 36.8%, and 58.8% for pyrene, respectively, which is consistent with the change trends of dissipation enhancements of the contaminants in sediments. However, there was no significant correlation between the enhancement of polyphenol oxidase activity and the dissipation enhancement. These results suggested that the improved bioavailability by P. crispus should be the key factor leading to the dissipation enhancements of the contaminants in sediments with different SOM contents, which provides essential information for phytoremediation of PAH-contaminated sediments.

[Fingerprint and spectrum-effect relationships on Tripterygium glycosides preparation].

Tripterygium glycosides preparation which extracted from the traditional Chinese herb Tripterygium wilfordii (TWHY), was widely used to treat the autoimmune diseases. Previous works demonstrated that TWHF had potent anti-inflammatory and immunosuppressive properties. But the different quality and high incident rate of side effects of different manufactures inhibited its clinical application. Since TWHF had been generally known to play a therapeutical effect by synergism of multiple constituents, it was necessary to build the relationship between the HPLC fingerprint and bioactivity so as to ensure the quality safety and efficacy. The HPLC fingerprint showed that description and content of peaks from different manufactures were diverse. Only 11 common peaks were found. In this study, mice spleen cells stimulated by Con A were used to test the proliferation inhibition bioactivity of TWHF preparations, which were incubated with 30, 15, 7.5, 3.75, 1.88 and 0.94 mg x L(-1) TWHF preparations for 48 h. The results showed that mice spleen cells proliferation was inhibited by all TWHF preparations significantly compared with the control group, which suggested the TWHF preparations showed immune suppress activity. The TWHF preparations from 7 manufacture showed different IC50 value, which might belong to different contents which showed in the HPLC fingerprint. Moreover, a relationship between the HPLC fingerprint and the bioactivity were established to identify important constituents by grey relational analysis (GRA). The result showed that all the contents were relative with the IC50, especially No. 5 and 10 peaks, but No. 1 peak, which was proved to be triptolide, had few contribute to the inhibition of mice spleen cells proliferation. The study of relationship between the HPLC fingerprint and the IC50 by GRA could help to investigate mechanism of bioactive and provide an evidence for the quantification of multi-constituents.

Head-to-head comparison of dipeptidyl peptidase-IV inhibitors and sulfonylureas - a meta-analysis from randomized clinical trials.

BACKGROUND: Dipeptidyl peptidase-IV (DPP-4) inhibitors and sulfonylureas are two important second-line anti-diabetic agents. The objective of this research was to evaluate the efficacy and safety of DPP-4 inhibitors compared with sulfonylureas by meta-analytic approach of available randomized studies. METHODS: We searched MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials databases up to 30 June 2013, collecting all randomized clinical trials with a treatment duration of ≥18weeks. Data on glycated haemoglobin (HbA1c ), body weight, hypoglycaemia, total adverse events, and cardiovascular events were retrieved and analysed. RESULTS: The analysis included 12 randomized studies comprising 10 982 patients with type 2 diabetes mellitus. On the basis of meta-analysis, sulfonylureas lowered HbA1c significantly more than DPP-4 inhibitors with weighted mean difference (WMD) of 0.105 [95% confidence interval (CI) 0.103 to 0.107]. The results were consistent in trials with longer (>32 weeks) or shorter (≤32 weeks) duration; however, DPP-4 inhibitors showed greater reduction in HbA1c compared with the second-generation sulfonylureas and in patients with baseline eGFR < 50 mL/min/1.73 m(2) . Patients treated with DPP-4 inhibitors are less likely to achieve HbA1c < 7% compared with sulfonylureas [Mantel-Haenszel odds ratio (MH-OR) 0.91; 95% CI 0.84 to 0.99]. DPP-4 xinhibitors were associated with a reduction in body weight (WMD -1.652; 95% CI -1.658 to -1.646) and lower risk of hypoglycaemia (MH-OR, 0.13; 95% CI 0.11 to 0.16), total adverse events (MH-OR, 0.79; 95% CI 0.72 to 0.87), and cardiovascular events (MH-OR, 0.53; 95% CI 0.32 to 0.87) compared with sulfonylureas. CONCLUSION: Although DPP-4 inhibitors are less efficacious compared with sulfonylureas, they demonstrate a beneficial effect on body weight, episodes of hypoglycaemia, and total adverse events.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Role of low-proportion, hydrophobic dissolved organic matter components in inhibiting methylmercury uptake by phytoplankton.

Uptake of methylmercury (MeHg), a potent neurotoxin, by phytoplankton is a major concern due to its role as the primary pathway for MeHg entry into aquatic food webs, thereby posing a significant risk to human health. While it is widely believed that the MeHg uptake by plankton is negatively correlated with the concentrations of dissolved organic matter (DOM) in the water, ongoing debates continue regarding the specific components of DOM that exerts the dominant influence on this process. In this study, we employed a widely-used resin fractionation approach to separate and classify DOM derived from algae (AOM) and natural rivers (NOM) into distinct components: strongly hydrophobic, weakly hydrophobic, and hydrophilic fractions. We conduct a comparative analysis of different DOM components using a combination of spectroscopy and mass spectrometry techniques, aiming to identify their impact on MeHg uptake by Microcystis elabens, a prevalent alga in freshwater environments. We found that the hydrophobic components had exhibited more pronounced spectral characteristics associated with the protein structures while protein-like compounds between hydrophobic and hydrophilic components displayed significant variations in both distributions and the values of m/z (mass-to-charge ratio) of the molecules. Regardless of DOM sources, the low-proportion hydrophobic components usually dominated inhibition of MeHg uptake by Microcystis elabens. Results inferred from the correlation analysis suggest that the uptake of MeHg by the phytoplankton was most strongly and negatively correlated with the presence of protein-like components. Our findings underscore the importance of considering the diverse impacts of different DOM fractions on inhibition of phytoplankton MeHg uptake. This information should be considered in future assessments and modeling endeavors aimed at understanding and predicting risks associated with aquatic Hg contamination.

Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

Efficient Biodegradation of the Neonicotinoid Insecticide Flonicamid by Pseudaminobacter salicylatoxidans CGMCC 1.17248: Kinetics, Pathways, and Enzyme Properties.

Nitrile-containing insecticides can be converted into their amide derivatives by Pseudaminobacter salicylatoxidans. N-(4-trifluoromethylnicotinoyl) glycinamide (TFNG-AM) is converted to 4-(trifluoromethyl) nicotinoyl glycine (TFNG) using nitrile hydratase/amidase. However, the amidase that catalyzes this bioconversion has not yet been fully elucidated. In this study, it was discovered that flonicamid (FLO) is degraded by P. salicylatoxidans into the acid metabolite TFNG via the intermediate TFNG-AM. A half-life of 18.7 h was observed for P. salicylatoxidans resting cells, which transformed 82.8% of the available FLO in 48 h. The resulting amide metabolite, TFNG-AM, was almost all converted to TFNG within 19 d. A novel amidase-encoding gene was cloned and overexpressed in Escherichia coli. The enzyme, PmsiA, hydrolyzed TFNG-AM to TFNG. Despite being categorized as a member of the amidase signature enzyme superfamily, PsmiA only shares 20-30% identity with the 14 previously identified members of this family, indicating that PsmiA represents a novel class of enzyme. Homology structural modeling and molecular docking analyses suggested that key residues Glu247 and Met242 may significantly impact the catalytic activity of PsmiA. This study contributes to our understanding of the biodegradation process of nitrile-containing insecticides and the relationship between the structure and function of metabolic enzymes.

Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.

PURPOSE: The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. METHODS: Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. RESULTS: The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. CONCLUSIONS: MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.

Impact of biochar coexistence with polar/nonpolar microplastics on phenanthrene sorption in soil.

Microplastics and biochar normally coexist in soil. In this study, two microplastics of different polarities (nonpolar polyethylene (PE) and polar polybutylene adipate-co-terephthalate (PBAT)) and two wheat straw biochars produced at 400 (W4) and 700 °C (W7) were selected to investigate the sorption behaviors of phenanthrene in soil where microplastics and biochar coexisted. The results showed that the presence of PE more significantly weakened the adhesion of soil particles onto biochar than the presence of PBAT. Meanwhile, the presence of biochar enhanced the soil particle attachment on the microplastic surface. As a result, the sorption behavior of phenanthrene was significantly different in soil where biochar coexisted with microplastics of different polarities. The Koc values of PE-biochar-soil mixtures at Ce= 0.005 Cs were up to 42 % lower than those of PBAT-biochar-soil mixtures, which is related to lower micropore area of particles isolated from the former. However, at Ce = 0.05 Cs and 0.5 Cs, the Koc values of PE-biochar-soil mixtures were up to 1.4 times higher than those of PBAT-biochar-soil mixtures because of a more significant reduction in biochar surface polarity when it coexisted with nonpolar PE.

A ratiometric fluorescent paper sensor based on dye-embedded MOF for high-sensitive detection of arginine.

Ratiometric fluorescent sensors can suppress the interference of factors unrelated to analysis due to their built-in self-calibration characteristics, which exhibit higher sensitivity and more obvious visual detection in the process of qualitative and quantitative analysis. Herein, we constructed a ratiometric fluorescence probe based on fluorescent/colorimetric dual-mode method for the determination of arginine by encapsulating rhodamine B in-situ into UiO-66-NH2 MOFs (UiO-66-NH2@RhB). The as-prepared probe showed dual-emission characteristics under a single excitation wavelength. The fluorescence intensity of UiO-66-NH2 was increased significantly by arginine, while the emission peak intensity of rhodamine B remained stable, resulting in a single-signal response with fixed reference. Furthermore, the practicality of the presented sensor was successfully validated by quantitative detection of arginine in human serum. More significantly, paper-based sensors for arginine detection were devised by using carboxymethyl cellulose modified filter papers. Under the irradiation of ultraviolet light, the paper-based sensors would produce obvious color variation from lightpink to bluish violet. This work provided a convenient and efficient method for on-site detection of arginine.

Health Informatics: The Foundations of Public Health.

As technology continues to evolve, vast amounts of diverse digital data are becoming more easily generated and collected [...].

A Review of Endocrine Therapy in Early-stage Breast Cancer: The Journey From Crudeness to Precision.

Endocrine therapy (ET) is the standard of care for hormone receptor-positive early-stage breast cancer in the adjuvant setting. However, response to ET can vary across patient subgroups. Historically, hormone receptor expression and clinical stage are the main predictors of the benefit of ET. A "window of opportunity" trials has raised significant interest in recent years as a means of assessing the sensitivity of a patient's cancer to short-term neoadjuvant ET, which provides important prognostic information, and helps in decision-making regarding treatment options in a time-efficient and cost-efficient manner. In the era of genomics, molecular profiling has led to the discovery and evaluation of the prognostic and predictive abilities of new molecular profiles. To realize the goal of personalized medicine, we are in urgent need to explore reliable biomarkers or genomic signatures to accurately predict the clinical response and long-term outcomes associated with ET. Validation of these biomarkers as reliable surrogate endpoints can also lead to a revolution in the clinical trial designs, and potentially avoid the need for repeated tissue biopsies in the surveillance of disease response. The clinical potential of tumor genomic profiling marks the beginning of a new era of precision medicine in breast cancer treatment.

Fosfomycin Enhances the Inhibition Ability of Linezolid Against Biofilms of Vancomycin-Resistant Enterococcus faecium in vitro.

Purpose: We explored the inhibition ability of linezolid/fosfomycin combination against biofilms of vancomycin-resistant Enterococcus faecium (VREfm) and tried to provide a theoretical basis for the treatment of VREfm biofilm-associated infections. Methods: Four clinical isolates of VREfm (No.2, No.4, No.5, and No.6) were used for this study, which were collected from the First Affiliated Hospital of Anhui Medical University. The checkerboard method was used to assess the synergistic effect of linezolid and fosfomycin. The inhibition ability of biofilm biomass was evaluated by crystal violet staining, and the metabolic activity was tested by an Alamar blue cell viability assay. Changes in biofilm formation-related genes of the strains after incubating with drugs were investigated via the quantitative real-time polymerase chain reaction (RT-qPCR). Results: The fractional inhibitory concentration index (FICI) showed that linezolid combined with fosfomycin had a synergistic effect on all four VREfm isolates. Compared with linezolid monotherapy, linezolid combined with fosfomycin led to a significant decrease in biofilm biomass and metabolic activity, especially in the mature biofilm. The results of RT-qPCR showed linezolid combined with fosfomycin inhibition biofilm formation through the inhibition of cylA, ebpA, and gelE transcription in VREfm in the initial and mature stages. To the mature biofilm, the combination also reduced the expression of asa1, atlA, and esp. Conclusion: The combination of linezolid and fosfomycin represented stronger inhibitory effect on the biofilm formation of VREfm than linezolid alone.

Biodegradability of algal-derived dissolved organic matter and its influence on methylmercury uptake by phytoplankton.

Methylmercury (MeHg) uptake by phytoplankton represents a key step in determining the exposure risks of aquatic organisms and human beings to this potent neurotoxin. Phytoplankton uptake is believed to be negatively related to dissolved organic matter (DOM) concentration in water. However, microorganisms can rapidly change DOM concentration and composition and subsequent impact on MeHg uptake by phytoplankton has rarely been tested. Here, we explored the influences of microbial degradation on the concentrations and molecular compositions of DOM derived from three common algal sources and tested their subsequent impacts on MeHg uptake by the widespread phytoplankton species Microcystis elabens. Our results indicated that dissolved organic carbon was degraded by 64.3‒74.1% within 28 days of incubating water with microbial consortia from a natural meso­eutrophic river. Protein-like components in DOM were more readily degraded, while the numbers of molecular formula for peptides-like compounds had increased after 28 days' incubation, probably due to the production and release of bacterial metabolites. Microbial degradation made DOM more humic-like which was consistent with the positive correlations between changes in proportions of Peaks A and C and bacterial abundance in bacterial community structures as illustrated by 16S rRNA gene sequencing. Despite rapid losses of the bulk DOM during the incubation, we found that DOM degraded after 28 days still reduced the MeHg uptake by Microcystis elabens by 32.7‒52.7% relative to a control without microbial decomposers. Our findings emphasize that microbial degradation of DOM would not necessarily enhance the MeHg uptakes by phytoplankton and may become more powerful in inhibiting MeHg uptakes by phytoplankton. The potential roles of microbes in degrading DOM and changing the uptakes of MeHg at the base of food webs should now be incorporated into future risk assessments of aquatic Hg cycling.