Impacts of hydraulic conditions on microplastics biofilm development, shear stresses distribution, and microbial community structures in drinking water distribution pipes.

Both microplastic and biofilm are contamination sources in drinking water, but their integrated impacts on water quality have been rarely studied, especially in drinking water distribution pipes with complex hydraulic conditions. This study explored the impacts of hydraulic conditions (0-2 m/s) on microplastic biofilm (MP-BM) development, shear stresses distribution, and microbial community structures. The research was conducted for two weeks using a pilot test device to simulate practical water pipes. The following were the primary conclusions: (1) According to morphology analysis, clusters (>5 µm) significantly increased in the plastisphere when the flow velocity ranged from 0.55 m/s to 0.95 m/s, and average size of clusters decreased when the flow velocity ranged from 1.14 m/s to 1.40 m/s (2) Characteristics of MP-BM impact shear stress on both plastisphere and pipe wall biofilm. Shear stresses were positively correlated with flow velocity, number of MP-BM, and size of MP-BM, while negatively correlated with diameters of pipes. (3) 31 genera changed strictly and monotonously with the fluid velocity, accounting for 15.42%. Opportunistic pathogens in MP-BM such as Sediminibacterium, Curvibacter, and Flavobacterium were more sensitive to hydraulic conditions. Moreover, microplastics (<100 µm) deserve more attention to avoid human ingestion and to prevent mechanical damage and bio-chemical risks.

Relationship between carotid intima-media thickness (cIMT) and dual-system imbalance in tobacco dependence: An rs-fMRI research.

BACKGROUND AND PURPOSE: According to the classic cognitive behavioral theory proposes, dysfunctional goal-directed and habit control systems are considered central to the pathogenesis of dependent behavior and impair recovery from addictions. The functional connectivity (FC) of the brain circuits for goal-directed or habitual behavior has not been clearly reported in tobacco-dependent groups. Smoking is one of the factors in the formation of atherosclerosis. Studies have shown that the thickness of carotid intima-media (cIMT) is associated with attention-executive-psychomotor functioning. Therefore, we hypothesized whether cIMT in tobacco-dependent individuals is associated with changes in the FC of the dual-system network. METHODS: A total of 29 male tobacco-dependent subjects (tobacco-dependent group) (mean age: 64.20 years, standard deviation [SD]: 4.81 years) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Exactly 28 male nonsmokers (control group) (mean age: 61.95 years, SD: 5.52 years) were also recruited to undergo rs-fMRI. We used the dorsolateral striatum (putamen) and dorsomedial striatum (caudate) as regions of interest for whole-brain resting-state connectivity to construct habitual and goal-directed brain networks, respectively. In addition, all participants were evaluated by carotid artery ultrasound to obtain the cIMT values. Then, we compared the dual-system brain networks between the tobacco dependence and control groups and the relationship between cIMT and imbalance of dual-system brain networks in tobacco dependence. RESULTS: The results showed a reduction in the connection between the caudate and precuneus and an increased connection between the putamen and prefrontal cortex; and supplementary motor area. The bilateral connectivity between the caudate and inferior frontal gyrus showed a significant negative correlation with the cIMT, and no positive correlation was observed with cIMT in the brain region that connects to the caudate. However, for the putamen, increased connectivity with the inferior temporal and medial frontal gyri was strongly associated with a high cIMT. CONCLUSIONS: The results indicate that the formation of tobacco dependence behavior is related to changes in the dual-system brain network. Carotid sclerosis is associated with the weakening of the goal-directed network and enhancement of the habit network in tobacco dependence. This finding suggests that tobacco dependence behavior and clinical vascular diseases are related to changes in brain functional networks.

Effect of disinfectant exposure and starvation treatment on the detachment of simulated drinking water biofilms.

Biofilms were one of the main habitats of microbes in the drinking water distribution system. The variation of environmental conditions can lead to the detachment of biofilms and the deterioration of water quality. In this study, the effects of disinfectant exposure and starvation treatment on the detachment of biofilms were investigated. The results showed that detaching rate increased with the concentration of chloramine in the inlet water and 1.0 mg/L of chloramine led to the largest detached biomass. The starvation treatment resulted in less biofilm biomass but the detaching rates of treated biofilms were higher than those without starvation. The 16S rRNA sequencing results showed that detached and stubborn biofilms had a significant difference in microbial diversity and richness. The microbial community composition of the two types of biofilm showed the difference in the abundance of Nitrospira, Bryobacter, Hyphomicrobium, and Pedomicrobium. Chloramine exposure did not have a significant impact on the microbial community while the starvation treatment led to a higher abundance of chemolithotrophs bacteria. Metagenomic results indicated that detached biofilms had higher abundances of ARGs and starvation treatment could enrich the ARGs. The results of this research could provide the knowledge of biofilm sloughing and help understand the health risk of antibiotic resistance in drinking water.

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal source of geographical local and foreign samples were examined herein, by applying support vector machine (SVM) algorithm. Random forest (RF) and Adaboost were applied for comparison as well. Discriminatory sequences were selected from Clostridiale, Bacteroidales, or Lactobacillales bacterial groups using extremely randomized trees (ExtraTrees). 1.51-12.64% of the unique sequences in the original library composed the representative markers, and they contributed 70% of the discrepancies between source microbiomes. The overall accuracy of the SVM model and the RF model on local samples was 96.08% and 98.04%, respectively, higher than that of the Adaboost (90.20%). As for the non-local samples, the SVM assigned most of the fecal samples into the correct category while several false-positive judgments occurred in closely related groups. The results in this paper suggested that the SVM was a time-saving and accurate method for fecal source tracking in contaminated water body with the potential capability of executing tasks based on geographically unassociated samples, and underlined the necessity of qPCR analysis for accurate detection of human source pollution.

Microplastics as carbon-nutrient sources and shaper for microbial communities in stagnant water.

Microplastics (MPs) are emerging pollutants as vectors for microbial colonization, but their role as nutrients sources for microbial communities has rarely been reported. This study explored the impact of six types of MPs on assimilable organic carbon (AOC) and microbial communities over eight weeks. The following were the primary conclusions: (1) MPs contributed to AOC increment and subsequently increased bacterial regrowth potential. The maximum AOC reached 722.03 µg/L. The increase in AOC formation corresponded to AOC NOX, except in PVC samples where AOC P17 primarily increased. (2) The MPs accelerated bacterial growth and changed the bacterial distribution between the biofilm and water phases. A high MP surface-area-to-volume ratio or low MPs density contributed to bacterial accumulation and biofilm formation around the plastisphere, thereby decreasing the relative microbial proportion in the water phase. (3) High-throughput sequencing and scanning electron microscope revealed that different MPs shaped various microbial communities temporally and spatially. (4) Biofilm formatting and formatted models were established and simulated to explain the kinetic interaction between the AOC and bacteria inhabiting the plastisphere. Finally, the challenges that plastic-deprived AOC represent in terms of anti-bacterial measures and chemical safety are discussed.

Resting-State Functional Magnetic Resonance Imaging Reveals Overactivation of the Habitual Control Brain System in Tobacco Dependence.

INTRODUCTION: We studied the regulatory mechanism of the habitual brain network in tobacco dependence to provide a theoretical basis for the regulation and cessation of tobacco dependence. METHODS: We used resting-state functional magnetic resonance imaging (rs-fMRI) to explore the Fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity (FC) of the habitual brain network in tobacco-dependent subjects and to evaluate the relationship between the FC level and tobacco selection preference behavior. In total, 29 male tobacco-dependent participants and 28 male nonsmoking participants were recruited. rs-fMRI was used to collect blood oxygen level-dependent signals of the participants in the resting and awake states. After rs-fMRI, all subjects completed cigarette/coin selection tasks (task 1 and task 2). RESULTS: Compared with the control group, the tobacco dependence group showed increased fractional amplitude values of fALFF in the left posterior cingulate cortex and right parahippocampus. FC in the tobacco-dependent group was increased in the right inferior temporal gyrus, left middle frontal gyrus, left cingulated gyrus, and bilateral superior frontal gyrus, compared with that in the control group. Moreover, the preference selection behavior was associated with the enhancement of FC about parts of the brain regions in the habitual brain network of the tobacco-dependent participants. Thus, habitual network activity was significantly enhanced in tobacco-dependent participants in the resting state. Moreover, a positive correlation was found between the cigarette selection preference of the smokers and certain brain regions related to the habitual network. DISCUSSION: This suggests that increased activity of the habitual brain network may be essential in the development of tobacco-dependent behavior.

Impact of substrate material and chlorine/chloramine on the composition and function of a young biofilm microbial community as revealed by high-throughput 16S rRNA sequencing.

The bacterial composition of biofilms in drinking water distribution systems is significantly impacted by the disinfection regime and substrate material. However, studies that have addressed the changes in the biofilm community during the early stage of formation (less than 10 weeks) were not yet adequate. Here, we explore the effects of the substrate materials (cast iron, stainless steel, copper, polyvinyl chloride, and high density polyethylene) and different disinfectants (chlorine and chloramine) on the community composition and function of young biofilm by using 16S rDNA sequencing. The results showed that Alphaproteobacteria (39.14%-80.87%) and Actinobacteria (5.90%-40.03%) were the dominant classes in chlorine-disinfection samples, while Alphaproteobacteria (17.46%-74.18%) and Betaproteobacteria (3.79%-68.50%) became dominant in a chloraminated group. The infrequently discussed genus Phreatobacter became predominant in the chlorinated samples, but it was inhibited by chloramine and copper ions. The key driver of the community composition was indicated as different disinfectants according to principle coordination analysis (PCoA) and Permutational multivariate analysis of variance (Adonis test), and the bacterial community changed significantly over time. Communities of biofilms grown on cast iron showed a great distance from the other materials according to Bray-Curtis dissimilarity, and they had a unique dominant genus, Dechloromonas. A metagenomics prediction based on 16S rDNA was used to detect the functional pathways of antibiotic biosynthesis and beta-lactam resistance, and it revealed that several pathways were significantly different in terms of their chlorinated and chloraminated groups.

Influence of Lifestyles on Mild Cognitive Impairment: A Decision Tree Model Study.

OBJECTIVE: To explore the effects of different lifestyle choices on mild cognitive impairment (MCI) and to establish a decision tree model to analyse their predictive significance on the incidence of MCI. METHODS: Study participants were recruited from geriatric and physical examination centres from October 2015 to October 2019: 330 MCI patients and 295 normal cognitive (NC) patients. Cognitive function was evaluated by the Mini-Mental State Examination Scale (MMSE) and Clinical Dementia Scale (CDR), while the Barthel Index (BI) was used to evaluate life ability. Statistical analysis included the χ 2 test, logistic regression, and decision tree. The ROC curve was drawn to evaluate the predictive ability of the decision tree model. RESULTS: Logistic regression analysis showed that low education, living alone, smoking, and a high-fat diet were risk factors for MCI, while young age, tea drinking, afternoon naps, social engagement, and hobbies were protective factors for MCI. Social engagement, a high-fat diet, hobbies, living condition, tea drinking, and smoking entered all nodes of the decision tree model, with social engagement as the root node variable. The importance of predictive variables in the decision tree model showed social engagement, a high-fat diet, tea drinking, hobbies, living condition, and smoking as 33.57%, 27.74%, 22.14%, 11.94%, 4.61%, and 0%, respectively. The area under the ROC curve predicted by the decision tree model was 0.827 (95% CI: 0.795~0.856). CONCLUSION: The decision tree model has good predictive ability. MCI was closely related to lifestyle; social engagement was the most important factor in predicting the occurrence of MCI.