The Effects of Acceptance and Commitment Therapy (ACT) Intervention on Inflammation and Stress Biomarkers: a Randomized Controlled Trial.

BACKGROUND: Psychological processes can be manifested in physiological health. We investigated whether acceptance and commitment therapy (ACT), targeted on psychological flexibility (PF), influences inflammation and stress biomarkers among working-age adults with psychological distress and overweight/obesity. METHOD: Participants were randomized into three parallel groups: (1) ACT-based face-to-face (n = 65; six group sessions led by a psychologist), (2) ACT-based mobile (n = 73; one group session and mobile app), and (3) control (n = 66; only the measurements). Systemic inflammation and stress markers were analyzed at baseline, at 10 weeks after the baseline (post-intervention), and at 36 weeks after the baseline (follow-up). General PF and weight-related PF were measured with questionnaires (Acceptance and Action Questionnaire, Acceptance and Action Questionnaire for Weight-Related Difficulties). RESULTS: A group × time interaction (p = .012) was detected in the high-sensitivity C-reactive protein (hsCRP) level but not in other inflammation and stress biomarkers. hsCRP decreased significantly in the face-to-face group from week 0 to week 36, and at week 36, hsCRP was lower among the participants in the face-to-face group than in the mobile group (p = .035, post hoc test). Age and sex were stronger predictors of biomarker levels at follow-up than the post-intervention PF. CONCLUSION: The results suggest that ACT delivered in group sessions may exert beneficial effects on low-grade systemic inflammation. More research is needed on how to best apply psychological interventions for the health of both mind and body among people with overweight/obesity and psychological distress. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01738256, Registered 17 August, 2012.

Diet-derived changes by sourdough-fermented rye bread in exhaled breath aspiration ion mobility spectrometry profiles in individuals with mild gastrointestinal symptoms.

The potential of utilising exhaled breath volatile organic compound (VOC) profiles in studying diet-derived metabolic changes was examined. After a four-week initial diet period with white wheat bread (WW), seven participants received in randomised order high-fibre diets containing sourdough whole grain rye bread (WGR) or white wheat bread enriched with bioprocessed rye bran (WW + BRB), both for 4 weeks. Alveolar exhaled breath samples were analysed with ChemPro®100i analyser (Environics OY, Mikkeli, Finland) at the end of each diet period in fasting state and after a standardised meal. The AIMS signal intensities in fasting state were different after the WGR diet as compared to other diets. The result suggests that WGR has metabolic effects not completely explained by the rye fibre content of the diet. This study encourages to utilise the exhaled breath VOC profile analysis as an early screening tool in studying physiological functionality of foods.

On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system.

Improvements in microbial drinking water quality monitoring are needed for the better control of drinking water distribution systems and for public health protection. Conventional water quality monitoring programmes are not always able to detect a microbial contamination of drinking water. In the drinking water production chain, in addition to the vulnerability of source waters, the distribution networks are prone to contamination. In this study, a pilot-scale drinking-water distribution network with an on-line monitoring system was utilized for detecting bacterial intrusion. During the experimental Escherichia coli intrusions, the contaminant was measured by applying a set of on-line sensors for electric conductivity (EC), pH, temperature (T), turbidity, UV-absorbance at 254 nm (UVAS SC) and with a device for particle counting. Monitored parameters were compared with the measured E. coli counts using the integral calculations of the detected peaks. EC measurement gave the strongest signal compared with the measured baseline during the E. coli intrusion. Integral calculations showed that the peaks in the EC, pH, T, turbidity and UVAS SC data were detected corresponding to the time predicted. However, the pH and temperature peaks detected were barely above the measured baseline and could easily be mixed with the background noise. The results indicate that on-line monitoring can be utilized for the rapid detection of microbial contaminants in the drinking water distribution system although the peak interpretation has to be performed carefully to avoid being mixed up with normal variations in the measurement data.

Nonlinear transfer of elements from soil to plants: impact on radioecological modeling.

In radioecology, transfer of radionuclides from soil to plants is typically described by a concentration ratio (CR), which assumes linearity of transfer with soil concentration. Nonlinear uptake is evidenced in many studies, but it is unclear how it should be taken into account in radioecological modeling. In this study, a conventional CR-based linear model, a nonlinear model derived from observed uptake into plants, and a new simple model based on the observation that nonlinear uptake leads to a practically constant concentration in plant tissues are compared. The three models were used to predict transfer of (234)U, (59)Ni and (210)Pb into spruce needles. The predictions of the nonlinear and the new model were essentially similar. In contrast, plant radionuclide concentration was underestimated by the linear model when the total element concentration in soil was relatively low, but within the range commonly observed in nature. It is concluded that the linear modeling could easily be replaced by a new approach that more realistically reflects the true processes involved in the uptake of elements into plants. The new modeling approach does not increase the complexity of modeling in comparison with CR-based linear models, and data needed for model parameters (element concentrations) are widely available.

Composition of active bacterial communities and presence of opportunistic pathogens in disinfected and non-disinfected drinking water distribution systems in Finland.

Many factors, including microbiome structure and activity in the drinking water distribution system (DWDS), affect the colonization potential of opportunistic pathogens. The present study aims to describe the dynamics of active bacterial communities in DWDS and identify the factors that shape the community structures and activity in the selected DWDSs. Large-volume drinking water and hot water, biofilm, and water meter deposit samples were collected from five DWDSs. Total nucleic acids were extracted, and RNA was further purified and transcribed into its cDNA from a total of 181 water and biofilm samples originating from the DWDS of two surface water supplies (disinfected with UV and chlorine), two artificially recharged groundwater supplies (non-disinfected), and a groundwater supply (disinfected with UV and chlorine). In chlorinated DWDSs, concentrations of <0.02-0.97 mg/l free chlorine were measured. Bacterial communities in the RNA and DNA fractions were analysed using Illumina MiSeq sequencing with primer pair 341F-785R targeted to the 16S rRNA gene. The sequence libraries were analysed using QIIME pipeline, Program R, and MicrobiomeAnalyst. Not all bacterial cells were active based on their 16S rRNA content, and species richness was lower in the RNA fraction (Chao1 mean value 490) than in the DNA fraction (710). Species richness was higher in the two DWDSs distributing non-disinfected artificial groundwater (Chao1 mean values of 990 and 1 000) as compared to the two disinfected DWDSs using surface water (Chao1 mean values 190 and 460) and disinfected DWDS using ground water as source water (170). The difference in community structures between non-disinfected and disinfected water was clear in the beta-diversity analysis. Distance from the waterworks also affected the beta diversity of community structures, especially in disinfected distribution systems. The two most abundant bacteria in the active part of the community (RNA) and total bacterial community (DNA) belonged to the classes Alphaproteobacteria (RNA 28 %, DNA 44 %) and Gammaproteobacteria (RNA 32 %, DNA 30 %). The third most abundant and active bacteria class was Vampirovibrionia (RNA 15 %), whereas in the total community it was Paceibacteria (DNA 11 %). Class Nitrospiria was more abundant and active in both cold and hot water in DWDS that used chloramine disinfection compared to non-chlorinated or chlorine-using DWDSs. Thirty-eight operational taxonomic units (OTU) of Legionella, 30 of Mycobacterium, and 10 of Pseudomonas were detected among the sequences. The (RT)-qPCR confirmed the presence of opportunistic pathogens in the DWDSs studied as Legionella spp. was detected in 85 % (mean value 4.5 × 104 gene copies/100 ml), Mycobacterium spp. in 95 % (mean value 8.3 × 106 gene copies/100 ml), and Pseudomonas spp. in 78 % (mean value 1.6 × 105 gene copies/100 ml) of the water and biofilm samples. Sampling point inside the system (distance from the waterworks and cold/hot system) affected the active bacterial community composition. Chloramine as a chlorination method resulted in a recognizable community composition, with high abundance of bacteria that benefit from the excess presence of nitrogen. The results presented here confirm that each DWDS is unique and that opportunistic pathogens are present even in conditions when water quality is considered excellent.

What is the most appropriate follow-up time for detecting the epidemiological relationship between coronary artery disease and its main risk factors: novel findings from a 35-year follow-up study.

BACKGROUND: The aim was to investigate the most appropriate follow-up time to detect the associations of coronary artery disease (CAD) with its traditional risk factors in a long-term prospective cohort study. METHODS: The Kuopio Ischaemic Heart Disease Risk Factors Study provided the study material of 1958 middle-aged men free from CAD at baseline and followed up for 35 years. We performed Cox models adjusted for age, family history, diabetes, obesity, hypercholesterolemia, hypertension, smoking, and physical activity, investigated covariate interactions, and tested Schoenfeld residuals to detect time-dependent covariates. Moreover, we applied a sliding window procedure with a subarray of 5 years to better differentiate between risk factors manifested within years and those manifested within decades. The investigated manifestations were CAD and fatal acute myocardial infarction (AMI). RESULTS: Seven hundred seventeen (36.6%) men had CAD, and 109 (5.6%) men died from AMI. After 10 years of follow-up, diabetes became the strongest predictor of CAD with a fully adjusted hazard ratio (HR) of 2.5-2.8. During the first 5 years, smoking was the strongest predictor (HR 3.0-3.8). When the follow-up time was 8-19 years, hypercholesterolemia predicted CAD with a HR of >2. The associations of CAD with age and diabetes depended on time. Age hypertension was the only statistically significant covariate interaction. The sliding window procedure highlighted the significance of diabetes over the first 20 years and hypertension after that. Regarding AMI, smoking was associated with the highest fully adjusted HR (2.9-10.1) during the first 13 years. The associations of extreme and low physical activity with AMI peaked when the follow-up time was 3-8 years. Diabetes showed its highest HR (2.7-3.7) when the follow-up time was 10-20 years. During the last 16 years, hypertension was the strongest predictor of AMI (HR 3.1-6.4). CONCLUSION: The most appropriate follow-up time for most CAD risk factors was 10-20 years. Concerning smoking and hypertension shorter and longer follow-up times could be considered, respectively, particularly when studying fatal AMI. In general, prospective cohort studies of CAD would provide more comprehensive results by reporting point estimates in relation to more than one timepoint and concerning sliding windows.

Prediction of microRNA targets in Caenorhabditis elegans using a self-organizing map.

MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that regulate transcriptional processes via binding to the target gene mRNA. In animals, this binding is imperfect, which makes the computational prediction of animal miRNA targets a challenging task. The accuracy of miRNA target prediction can be improved with the use of machine learning methods. Previous work has described methods using supervised learning, but they suffer from the lack of adequate training examples, a common problem in miRNA target identification, which often leads to deficient generalization ability. RESULTS: In this work, we introduce mirSOM, a miRNA target prediction tool based on clustering of short 3(')-untranslated region (3(')-UTR) substrings with self-organizing map (SOM). As our method uses unsupervised learning and a large set of verified Caenorhabditis elegans 3(')-UTRs, we did not need to resort to training using a known set of targets. Our method outperforms seven other methods in predicting the experimentally verified C.elegans true and false miRNA targets. AVAILABILITY: mirSOM miRNA target predictions are available at http://kokki.uku.fi/bioinformatics/mirsom.

Epidemiological analysis of coronary heart disease and its main risk factors: are their associations multiplicative, additive, or interactive?

OBJECTIVE: The purpose of this study was to discover how considering multiplicative, additive, and interactive effects modifies results of a prospective cohort study on coronary heart disease (CHD) incidence and its main risk factors. MATERIAL AND METHODS: The Kuopio Ischaemic Heart Disease Risk Factor (KIHD) Study provided the study material, 2682 Eastern Finnish middle-aged men, followed since the 1980s. We applied multiplicative and additive survival models together with different statistical metrics and confidence intervals for risk ratios and risk differences to estimate the nature of associations. RESULTS: The mean (SD) follow-up time among men who were free of CHD at baseline (n = 1958) was 21.4 (10.4) years, and 717 (37%) of them had the disease and 301 (15%) died for CHD before the end of follow-up. All tested non-modifiable and modifiable risk factors statistically significantly predicted CHD incidence. We detected three interactions: circulating low-density lipoprotein cholesterol (LDL-C) × age, obesity × age, and obesity × smoking of which LDL-C × age was the most evident one. High LDL-C increased the risk of CHD more among men younger than 50 [risk ratio (RR) 2.10] than those older than 50 (RR 1.22). LDL-C status was the only additive covariate. The additive effect of high LDL-C increased almost linearly up to 18 years and then reached a plateau. The simple multiplicative survival model stressed glycemic status as the strongest modifiable risk factor for developing CHD [hazard ratio (HR) for diabetes vs. normoglycemia was 2.69], whereas the model considering interactions and time dependence emphasised the role of LDL-C status (HR for high LDL-C vs. lower than borderline was 4.43). Age was the strongest non-modifiable predictor. CONCLUSIONS: Including covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses and ease to define the order of importance across CHD risk factors. KEY MESSAGESIncluding covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses on coronary heart disease.Including covariate interactions and time dependence in survival models potentially ease to define the order of importance across coronary heart disease risk factors.

Epidemiological predictive modeling: lessons learned from the Kuopio ischemic heart disease risk factor study.

PURPOSE: The use of predictive models in epidemiology is relatively narrow as most of the studies report results of traditional statistical models such as Linear, Logistic, or Cox regressions. In this study, a high-dimensional epidemiological cohort, collected within the Kuopio Ischemic Heart Disease Risk Factor Study in 1984-1989, was used to investigate the predictive ability of models with embedded variable selection. METHODS: Simple Logistic Regression with seven preselected risk factors was compared to k-Nearest Neighbors, Logistic Lasso Regression, Decision Tree, Random Forest, and Multilayer Perceptron in predicting cardiovascular death for the aged men from Kuopio Ischemic Heart Disease Risk Factor for the long horizon of 30 ± 3 years: 746 predictor variables were available for 2682 men (705 cardiovascular deaths were registered). We considered two scenarios of handling competing risks (removing subjects and treating them as non-cases). RESULTS: The best average AUC on the test sample was 0.8075 (95%CI, 0.8051-0.8099) in scenario 1 and 0.7155 (95%CI, 0.7128-0.7183) in scenario 2 achieved with Logistic Lasso Regression, which was 6.04% and 5.50% higher than the baseline AUC provided by Logistic Regression with manually preselected predictors. CONCLUSIONS: In both scenarios Logistic Lasso Regression, Random Forest, and Multilayer Perceptron outperformed Simple Logistic Regression.

Effects of data preprocessing on results of the epidemiological analysis of coronary heart disease and behaviour-related risk factors.

BACKGROUND: We carried out this study to demonstrate the effects of outcome sensitivity, participant exclusions, and covariate manipulations on results of the epidemiological analysis of coronary heart disease (CHD) and its behaviour-related risk factors. MATERIAL AND METHODS: Our study population consisted of 1592 54-year-old men, who participated in the Kuopio Ischaemic Heart Disease Risk Factor (KIHD) Study. We used the Cox proportional-hazards model to predict the hazard of CHD and applied different sets of outcomes concerning outcome sensitivity and data preprocessing procedures regarding participant exclusions and covariate manipulations. RESULTS: The mean follow-up time was 23 years, and 730 men received the CHD diagnosis. Cox regressions based on data with no participant exclusions most often discovered statistically significant associations. Loose inclusion criteria for study participants with any CVD during the follow-up and strict exclusion criteria for participants with no CVD were best in discovering the associations between risk factors and CHD. Outcome sensitivity affected the associations, whereas the covariate type, continuous or categorical, did not. CONCLUSIONS: This study suggests that excluding study participants who are not disease-free at baseline is probably unnecessary for epidemiological analyses. Epidemiological research reports should present results based on no data exclusions together with results based on reasoned exclusions.

Post-Analysis of Predictive Modeling with an Epidemiological Example.

Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the high-dimensional predictor space. Investigating how model performance varies for subjects with different conditions is one of the important parts of post-analysis. This paper presents a model-independent approach for post-analysis, aiming to reveal those subjects' conditions that lead to low or high model performance, compared to the average level on the whole sample. Conditions of interest are presented in the form of rules generated by a multi-objective evolutionary algorithm (MOGA). In this study, Lasso logistic regression (LLR) was trained to predict cardiovascular death by 2016 using the data from the 1984-1989 examination within the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), which contained 2682 subjects and 950 preselected predictors. After 50 independent runs of five-fold cross-validation, the model performance collected for each subject was used to generate rules describing "easy" and "difficult" cases. LLR with 61 selected predictors, on average, achieved 72.53% accuracy on the whole sample. However, during post-analysis, three categories of subjects were discovered: "Easy" cases with an LLR accuracy of 95.84%, "difficult" cases with an LLR accuracy of 48.11%, and the remaining cases with an LLR accuracy of 71.00%. Moreover, the rule analysis showed that medication was one of the main confusing factors that led to lower model performance. The proposed approach provides insightful information about subjects' conditions that complicate predictive modeling.

Diverse and active archaea communities occur in non-disinfected drinking water systems-Less activity revealed in disinfected and hot water systems.

The knowledge about the members of active archaea communities in DWDS is limited. The current understanding is based on high-throughput 16S ribosomal RNA gene (DNA-based) amplicon sequencing that reveals the diversity of active, dormant, and dead members of the prokaryote (bacteria, archaea) communities. The sequencing primers optimized for bacteria community analysis may underestimate the share of the archaea community. This study characterized archaea communities at five full-scale drinking water distribution systems (DWDS), representing a variety of drinking water production units (A-E); A&B use artificially recharged non-disinfected groundwater (ARG), the other DWDS's supplied water disinfected by using ultraviolet (UV) light and chlorine compounds, C&D were surface waterworks and E was a ground waterworks. For the first time for archaea community analyses, this study employed the archaea-specific high-throughput sequencing primers for 16S ribosomal RNA (rRNA) as a target (reverse-transcribed cDNA; an RNA-based approach) in addition to the previously used 16S rRNA gene target (rDNA; a DNA-based approach) to reveal the active fraction of the archaea present in DWDS. The archaea community structure in varying environmental conditions in the water and biofilm of the five DWDSs were investigated by taking into consideration the system properties (cold or hot water system) and water age (distance from the treatment plants) in samples from each season of one year. The RNA-based archaea amplicon reads were obtained mostly from cold water samples from DWDSs (A-B) distributing water without disinfection where the DNA-based and RNA-based analysis created separate clusters in a weighted beta-diversity analysis. The season and location in DWDS A further affected the diversity of these archaea communities as was seen by different clusters in beta-diversity plots. The recovery of archaea reads was not adequate for analysis in any of the disinfected samples in DWDSs C-E or non-disinfected hot water in DWDSs A-B when utilizing RNA-based template. The metabolically active archaea community of DWDSs thus seemed to be effectively controlled by disinfection of water and in the hot water systems by the temperature. All biofilms regardless of DWDS showed lower species richness values (mainly Nitrososphaeria class) than non-disinfected water from DWDSs A-B where several archaea classes occurred (e.g. Woesearchaeia, Nitrososphaeria, Micrarchaeia, Methanomicrobia, Iairchaeia, Bathyarchaeia) indicating only part of the archaea members were able to survive in biofilms. Thus, Archaea has been shown as a significant part of normal DWDS biota, and their role especially in non-disinfected DWDS may be more important than previously considered.

Facing the threat: common yellowjacket wasps as indicators of heavy metal pollution.

We investigated the common wasp, Vespula vulgaris as a bioindicator and biomonitor of metals in the industrial area. Using traps, we collected 257 yellowjackets along a pollution gradient in the Harjavalta Cu-Ni smelter in Southwest Finland. Our method detected metal elements such as arsenic (As), cobalt (Co), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), zinc (Zn), and mercury (Hg) in wasps. The data analyses revealed V. vulgaris can be a proper indicator for As, Cd, Co, Cu, Ni, and Pb, rather than for Fe and Zn contamination. Body burdens of As, Cd, Co, Cu, Ni, and Pb decreased with an increase in distance from smelter. Enrichment factor (EF) followed the pattern Pb ˃ Cd ˃ As ˃ Co ˃ Cu ˃ Ni. The highest bioaccumulation (BAF) values were revealed for Cd (5.9) and the lowest for Pb (0.1). Specially designed software (WaspFacer) allowed revealing body burdens of As, Cd, Co, Cu, Ni, and Pb to be associated with rather smaller than more asymmetric facial colour markings in yellowjackets. These results add to the body of literature on how heavy metal contaminants can have tangible phenotypic effects on insects and open future opportunities for using wasps as indicators of metal pollution.

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling.

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Electromagnetic Fields, Genomic Instability and Cancer: A Systems Biological View.

This review discusses the use of systems biology in understanding the biological effectsof electromagnetic fields, with particular focus on induction of genomic instability and cancer. Weintroduce basic concepts of the dynamical systems theory such as the state space and attractors andthe use of these concepts in understanding the behavior of complex biological systems. We thendiscuss genomic instability in the framework of the dynamical systems theory, and describe thehypothesis that environmentally induced genomic instability corresponds to abnormal attractorstates; large enough environmental perturbations can force the biological system to leave normalevolutionarily optimized attractors (corresponding to normal cell phenotypes) and migrate to lessstable variant attractors. We discuss experimental approaches that can be coupled with theoreticalsystems biology such as testable predictions, derived from the theory and experimental methods,that can be used for measuring the state of the complex biological system. We also reviewpotentially informative studies and make recommendations for further studies.

Control of Dust Dispersion From an Enclosed Renovation Site Into Adjacent Areas by Using Local Exhaust Ventilation.

OBJECTIVE: In real-world applications, implementation of an enclosure and negative pressurization is not always adequate to prevent the dispersion of dust from renovation sites. This study aimed to quantify the effect of local exhaust ventilation (LEV) in controlling the dust concentration within an enclosed renovation site to reduce the dust dispersion into adjacent areas. METHODS: The concentrations of inhalable and respirable dust were measured in 16 cases during renovation projects. Filter samples and time-resolved dust concentration data were collected simultaneously from the renovation site and adjacent areas to assess the efficacy of LEV in limiting the dust dispersion. RESULTS: The dispersion of dust outside of the enclosed renovation sites was limited significantly with using LEV. The estimated dust removal efficiency of LEV was 79% for inhalable dust concentration in the renovation site and 62% in the adjacent area. The use of LEV reduced the concentration of respirable dust by 33‒90% in the adjacent area and 80-87% within the renovation site. CONCLUSIONS: Using LEV was found to play a substantial role in dust containment, particularly when the enclosure failed to maintain the negative pressure. The study provides data-driven recommendations that are of practical importance as they promote healthier workplaces and policy improvements. In conclusion, dust dispersion into adjacent areas is prevented with an airtight enclosure (including airlocks) and continuous negative pressure. Dust containment was also obtained by having target dust concentration at the enclosed renovation site to below 4 mg m-3 for inhalable dust and below 1 mg m-3 for respirable dust, even though the enclosures not being continuously under negative pressure. The suggested target concentrations are achievable by using on-tool LEV during the most dust-producing tasks.

Active eukaryotes in drinking water distribution systems of ground and surface waterworks.

BACKGROUND: Eukaryotes are ubiquitous in natural environments such as soil and freshwater. Little is known of their presence in drinking water distribution systems (DWDSs) or of the environmental conditions that affect their activity and survival. METHODS: Eukaryotes were characterized by Illumina high-throughput sequencing targeting 18S rRNA gene (DNA) that estimates the total community and the 18S rRNA gene transcript (RNA) that is more representative of the active part of the community. DWDS cold water (N = 124), hot water (N = 40), and biofilm (N = 16) samples were collected from four cities in Finland. The sampled DWDSs were from two waterworks A-B with non-disinfected, recharged groundwater as source water and from three waterworks utilizing chlorinated water (two DWDSs of surface waterworks C-D and one of ground waterworks E). In each DWDS, samples were collected from three locations during four seasons of 1 year. RESULTS: A beta-diversity analysis revealed that the main driver shaping the eukaryotic communities was the DWDS (A-E) (R = 0.73, P < 0.001, ANOSIM). The kingdoms Chloroplastida (green plants and algae), Metazoa (animals: rotifers, nematodes), Fungi (e.g., Cryptomycota), Alveolata (ciliates, dinoflagellates), and Stramenopiles (algae Ochrophyta) were well represented and active-judging based on the rRNA gene transcripts-depending on the surrounding conditions. The unchlorinated cold water of systems (A-B) contained a higher estimated total number of taxa (Chao1, average 380-480) than chlorinated cold water in systems C-E (Chao1 ≤ 210). Within each DWDS, unique eukaryotic communities were identified at different locations as was the case also for cold water, hot water, and biofilms. A season did not have a consistent impact on the eukaryotic community among DWDSs. CONCLUSIONS: This study comprehensively characterized the eukaryotic community members within the DWDS of well-maintained ground and surface waterworks providing good quality water. The study gives an indication that each DWDS houses a unique eukaryotic community, mainly dependent on the raw water source and water treatment processes in place at the corresponding waterworks. In particular, disinfection as well as hot water temperature seemed to represent a strong selection pressure that controlled the number of active eukaryotic species.

Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods.

The distribution of drinking water generates soft deposits and biofilms in the pipelines of distribution systems. Disturbances in water distribution can detach these deposits and biofilms and thus deteriorate the water quality. We studied the effects of simulated pressure shocks on the water quality with online analysers. The study was conducted with copper and composite plastic pipelines in a pilot distribution system. The online data gathered during the study was evaluated with Self-Organising Map (SOM) and Sammon's mapping, which are useful methods in exploring large amounts of multivariate data. The objective was to test the usefulness of these methods in pinpointing the abnormal water quality changes in the online data. The pressure shocks increased temporarily the number of particles, turbidity and electrical conductivity. SOM and Sammon's mapping were able to separate these situations from the normal data and thus make those visible. Therefore these methods make it possible to detect abrupt changes in water quality and thus to react rapidly to any disturbances in the system. These methods are useful in developing alert systems and predictive applications connected to online monitoring.

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases.

BACKGROUND: The redundancy of information is becoming a critical issue for epidemiologists. High-dimensional datasets require new effective variable selection methods to be developed. This study implements an advanced evolutionary variable selection method which is applied for cardiovascular predictive modeling. The epidemiological follow-up study KIHD (Kuopio Ischemic Heart Disease Risk Factor Study) was used to compare the designed variable selection method based on an evolutionary search with conventional stepwise selection. The sample contains in total 433 predictor variables and a response variable indicating incidents of cardiovascular diseases for 1465 study subjects. RESULTS: The effectiveness of variable selection methods was investigated in combination with two models: Generalized Linear Logistic Regression and Support Vector Machine. We managed to decrease the number of variables from 433 to 38 and save the predictive ability of the models used. Their performance was evaluated with an F-score metric. At most, we gained 65.6% and 67.4% of the F-score before and after variable selection respectively. All the results were averaged over 5-folds of a cross-validation procedure. CONCLUSIONS: The presented evolutionary variable selection method allows a reduced set of variables to be chosen which are relevant to predicting cardiovascular diseases. A reference list of the most meaningful variables is introduced to be used as a basis for new epidemiological studies. In general, the multicollinearity of variables enables different combinations of predictors to be used and the same performance of models to be attained.

Evaluation of Real-World Implementation of Partitioning and Negative Pressurization for Preventing the Dispersion of Dust From Renovation Sites.

OBJECTIVES: The aim of this study was to assess the implementation of partitioning and the negative pressure method in limiting the dispersion of dust to areas adjacent to renovation sites. METHODS: The pressure difference between the worksites and adjacent areas and PM10 concentrations in the both zones were measured in 12 renovation sites, and the factors affecting the prevention of dispersion of dust were assessed. RESULTS: Poor implementation of partitioning and negative pressurization found in half of the renovation sites lead difficulties in achieving a proper negative pressure, causing dispersion of dust into adjacent areas. Main problems related to flimsy partitioning walls and poor air tightness of the enclosure. Dust concentrations in adjacent areas were substantially lower when natural ventilation in the renovation site was rejected and partitioning walls and their junctions to existing structures were sealed. In case of leaky enclosures, despite the high air exchange rates, a definite negative pressure could not be maintained. Instead, negative pressure minimum of -5 Pa was found to be sufficient for limiting the dispersion of dust from renovation sites. CONCLUSIONS: Improvement on implementation of dust controls is required through revising the guidance documents, education, and efficient supervision. This study revealed that the current Finnish practice to implement the negative pressurization based on the air exchange rate achieved with the portable exhaust fans alone is not reasonable to assure adequate dust containment. Continuous negative pressure minimum of -5 Pa is suggested, and it should be monitored with alarm devices throughout the renovation processes.