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In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of ammonia removal and aeration energy consumption than the ammonia PI controller because of the measurable disturbances used. On the other hand, with the ammonia MPC, comparable or even slightly poorer results than with the ammonia feedforward-PI controller are obtained. Further improvements to the MPC could be possible with an improved accuracy of the nonlinear reduced model of the ammonia nitrogen, more sophisticated control criteria used inside the controller and the extension of the problem from univariable ammonia to multivariable total nitrogen control. [ABSTRACT FROM AUTHOR]

Presents a study where upgrading of an existing wastewater treatment plant was supported by simulation. Decision making between two technologies to improve nitrogen removal; Mathematical models designed to perform simulations; Use of the process model to evaluate the final plant configuration and to optimize the plant operating parameters.

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health., Competing Interests: Declaration of interests R.J.X. is a member of the Scientific Advisory Boards at Nestlé and Magnet Biomedicine, a founder of Jnana and Celsius Therapeutics, and a board member of MoonLake Immunotherapeutics. A provisional patent application describing potential treatments for CVD using the isolates and related compositions described in this study has been filed. The authors listed on that application are R.J.X., C.L., M.S., A.M.T.M, and D.R.P., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment., Competing Interests: Declaration of interests R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics, board director at MoonLake Immunotherapeutics, and consultant to Nestlé. D.R.M. is a co-founder of MedBiome Inc., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Fungi are consistently enriched in inflamed intestines, with elusive effects on host immunity. In a recent issue of Nature Medicine, Martini et al. identify a subset of Th1 cells able to lyse the epithelium, enriched in Crohn's disease patient samples after fungal exposure., Competing Interests: Declaration of interests R.J.X is a co-founder of Celsius Therapeutics and Jnana Therapeutics, board director at MoonLake Immunotherapeutics, and consultant to Nestlé., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease., (© 2023. The Author(s).)

CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies., Competing Interests: Declaration of interests R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics, a member of the Scientific Advisory Board at Nestle, and a member of the Board of Directors at Moonlake Immunotherapeutics. S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs, Seer, and PrognomIQ., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Continuous consumption combined with incomplete removal during wastewater treatment means residues of psychoactive substances (licit drugs, medications of abuse and illicit drugs) are constantly introduced into the aquatic environment, where they have the potential to affect non-target organisms. In this study, 17 drug residues of psychoactive substances were determined in wastewater influent, effluent and in receiving rivers of six Slovene municipal wastewater treatment plants employing different treatment technologies. Variations in removal efficiencies (REs) during spring, summer and winter were explored, and ecotoxic effects were evaluated using in silico (Ecological Structure-Activity Relationships software-ECOSAR) and in vivo (algal growth inhibition test) methods. Drug residues were detected in influent and effluent in the ng/L to μg/L range. In receiving rivers, biomarkers were in the ng/L range, and there was good agreement between measured and predicted concentrations. On average, REs were highest for nicotine, 11-nor-9-carboxy-∆9-tetrahydrocannabinol (THC-COOH), cocaine residues, and amphetamine (>90 %) and lowest for methadone residues (<30 %). REs were comparable between treatments involving activated sludge and membrane bioreactors, while the moving biofilm bed reactor (MBBR) removed cotinine, cocaine, and benzoylecgonine to a lesser extent. Accordingly, higher levels of nicotine and cocaine residues were detected in river water receiving MBBR discharge. Although there were seasonal variations in REs and levels of drug residues in receiving rivers, no general pattern could be observed. No significant inhibition of algal growth (Chlamydomonas reinhardtii) was observed for the tested compounds (1 mg/L) during 72 h and 240 h of exposure, although effects on aquatic plants were predicted in silico. In addition, environmental risk assessment revealed that levels of nicotine, methadone, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), morphine, and 3,4-methylenedioxymethamphetamine (MDMA) pose a risk to aquatic organisms. Since nicotine and EDDP can have acute and chronic effects, the authors support regular monitoring of receiving surface waters, followed up by regulatory actions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The perinatal period represents a critical window for cognitive and immune system development, promoted by maternal and infant gut microbiomes and their metabolites. Here, we tracked the co-development of microbiomes and metabolomes from late pregnancy to 1 year of age using longitudinal multi-omics data from a cohort of 70 mother-infant dyads. We discovered large-scale mother-to-infant interspecies transfer of mobile genetic elements, frequently involving genes associated with diet-related adaptations. Infant gut metabolomes were less diverse than maternal but featured hundreds of unique metabolites and microbe-metabolite associations not detected in mothers. Metabolomes and serum cytokine signatures of infants who received regular-but not extensively hydrolyzed-formula were distinct from those of exclusively breastfed infants. Taken together, our integrative analysis expands the concept of vertical transmission of the gut microbiome and provides original insights into the development of maternal and infant microbiomes and metabolomes during late pregnancy and early life., Competing Interests: Declaration of interests R.J.X. is co-founder of Jnana Therapeutics and Celsius Therapeutics, board director at MoonLake Immunotherapeutics, and consultant to Nestlé; these organizations had no role in the study., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This study analyzed 16 bisphenols (BPs) in wastewater and sludge samples collected from different stages at a municipal wastewater treatment plant based on sequencing batch reactor technology. It also describes developing an analytical method for determining BPs in the solid phase of activated sludge based on solid-phase extraction and gas chromatography-mass spectrometry. Obtained concentrations are converted into mass flows, and the biodegradation of BPs and adsorption to primary and secondary sludge are determined. Ten of the sixteen BPs were present in the influent with concentrations up to 434 ng L -1 (BPS). Only five BPs with concentrations up to 79 ng L -1 (BPA) were determined in the plant effluent, accounting for 8 % of the total BPs determined in the influent. Eleven per cent of the total BPs were adsorbed on primary and secondary sludge. Overall, BPs biodegradation efficiency was 81%. The highest daily emissions via effluent release (1.48 g day -1 ) and sludge disposal (4.63 g day -1 ) were for BPA, while total emissions reached 2 g day -1 via effluent and 6 g day -1 via sludge disposal. The data show that the concentrations of BPs in sludge are not negligible, and their environmental emissions should be monitored and further studied.

The gut microbiome has an important role in infant health and development. We characterized the fecal microbiome and metabolome of 222 young children in Dhaka, Bangladesh during the first two years of life. A distinct Bifidobacterium longum clade expanded with introduction of solid foods and harbored enzymes for utilizing both breast milk and solid food substrates. The clade was highly prevalent in Bangladesh, present globally (at lower prevalence), and correlated with many other gut taxa and metabolites, indicating an important role in gut ecology. We also found that the B. longum clades and associated metabolites were implicated in childhood diarrhea and early growth, including positive associations between growth measures and B. longum subsp. infantis, indolelactate and N-acetylglutamate. Our data demonstrate geographic, cultural, seasonal, and ecological heterogeneity that should be accounted for when identifying microbiome factors implicated in and potentially benefiting infant development., Competing Interests: Declaration of interests L.S., C.L.B., C.I.L.R., S.D., O.D-B., C.N-B., and O.S. are employees of Société des Produits Nestlé (SPN). R.J.X. is a member of the Scientific Advisory Board at Nestlé, founder of Jnana and Celsius Therapeutics and board member of MoonLake Immunotherapeutics. We filed a patent related to findings described in this study. R.J.X., H.V., T.V., O.S., L.S., S.D., and C.N.-B. are listed as inventors (Bifidobacterium Longum Transitional Microorganisms, Compositions and Uses Thereof, PCT/US2022/035,310)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: The bacillus Calmette-Guérin (BCG) vaccine protects against tuberculosis and heterologous infections but elicits high inter-individual variation in specific and nonspecific, or trained, immune responses. While the gut microbiome is increasingly recognized as an important modulator of vaccine responses and immunity in general, its potential role in BCG-induced protection is largely unknown., Results: Stool and blood were collected from 321 healthy adults before BCG vaccination, followed by blood sampling after 2 weeks and 3 months. Metagenomics based on de novo genome assembly reveals 43 immunomodulatory taxa. The nonspecific, trained immune response is detected by altered production of cytokines IL-6, IL-1β, and TNF-α upon ex vivo blood restimulation with Staphylococcus aureus and negatively correlates with abundance of Roseburia. The specific response, measured by IFN-γ production upon Mycobacterium tuberculosis stimulation, is associated positively with Ruminococcus and Eggerthella lenta. The identified immunomodulatory taxa also have the strongest effects on circulating metabolites, with Roseburia affecting phenylalanine metabolism. This is corroborated by abundances of relevant enzymes, suggesting alternate phenylalanine metabolism modules are activated in a Roseburia species-dependent manner., Conclusions: Variability in cytokine production after BCG vaccination is associated with the abundance of microbial genomes, which in turn affect or produce metabolites in circulation. Roseburia is found to alter both trained immune responses and phenylalanine metabolism, revealing microbes and microbial products that may alter BCG-induced immunity. Together, our findings contribute to the understanding of specific and trained immune responses after BCG vaccination., (© 2021. The Author(s).)

The human gut microbiota is increasingly recognized as an important factor in modulating innate and adaptive immunity through release of ligands and metabolites that translocate into circulation. Urbanizing African populations harbor large intestinal diversity due to a range of lifestyles, providing the necessary variation to gauge immunomodulatory factors. Here, we uncover a gradient of intestinal microbial compositions from rural through urban Tanzanian, towards European samples, manifested both in relative abundance and genomic variation observed in stool metagenomics. The rural population shows increased Bacteroidetes, led by Prevotella copri, but also presence of fungi. Measured ex vivo cytokine responses were significantly associated with 34 immunomodulatory microbes, which have a larger impact on circulating metabolites than non-significant microbes. Pathway effects on cytokines, notably TNF-α and IFN-γ, differential metabolome analysis and enzyme copy number enrichment converge on histidine and arginine metabolism as potential immunomodulatory pathways mediated by Bifidobacterium longum and Akkermansia muciniphila., (© 2021. The Author(s).)

This study investigates hydrodynamic performance of a novel pinned disc rotating generator of hydrodynamic cavitation in comparison with a serrated disc variant on a pilot-scale. Experimental results show that at a given rotational speed and liquid flow rate, the pinned disc generates more intense cavitation (i.e. lower cavitation number, higher volume fraction of vapor and higher amplitude of pressure fluctuations) than the serrated disc, while also consuming less energy per liquid pass (i.e., higher flow rate and pumping pressure difference of water at similar power consumption). Additionally, mechanical and chemical wastewater treatment performance of the novel cavitator was evaluated on an 800 L influent sample from a wastewater treatment plant. Mechanical effects resulted in a reduction of average particle size from 148 to 38 µm and increase of specific surface area, while the oxidation potential was confirmed by reduction of COD, TOC, and BOD up to 27, 23 and 30% in 60 cavitation passes, respectively. At optimal operating conditions and 30 cavitation passes, pinned disc cavitator had a 310% higher COD removal capacity while consuming 65% less energy per kg of COD removed than the serrated disc cavitator. Furthermore, the specific COD-reduction energy consumption of the pinned disc cavitator on the pilot scale is comparable to the best cases of lab-scale orifice and venturi devices operating at much lower wastewater processing capacity., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange ( http://orange.biolab.si ) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

Motivation: Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve., Results: We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training., Availability and Implementation: scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si., (© The Author(s) 2019. Published by Oxford University Press.)

Wastewater from washing, oil separators, the metal processing and detergent industries, was tested and treated for treatment of different types of liquid waste at industrial level at Domžale-Kamnik Wastewater Treatment Plant (WWTP). The effect of implementing the electrocoagulation (EC) and flotation processes, respectively, is analysed and includes the duration of the EC implementation, voltage, number of electrodes, and chemical addition, as well as the pH effect and conductivity. The tests were performed not only on various types of liquid waste, but also on different mixtures of liquid waste. Laboratory analysis of the samples before and after EC have shown an effective reduction not only in organic loads in accordance with the COD (chemical oxygen demand) parameter, but also in mineral oil content, toxic metal concentration, and surfactants. The COD in liquid waste from the detergent industry was reduced by 73% and the content of surfactants by 64%. In liquid waste from the metal processing industry, the COD decreased by up to 95%, while the content of toxic metals decreased from 59 to 99%. Similar phenomena were shown in liquid waste from oil separators, where the COD was reduced to 33% and the concentration of mineral oils by 99%. Some of the liquid wastes were mixed together in the ratio 1:1, thus allowing testing of the operation of EC technology in heterogeneous liquid waste, where the final result proved to be effective cleaning as well. After treatment in the process of EC, the limit values of the treated water proved appropriate for discharge into the sewerage system.

The results of an inter-laboratory comparison exercise to determine cytostatic anticancer drug residues in surface water, hospital wastewater and wastewater treatment plant effluent are reported. To obtain a critical number of participants, an invitation was sent out to potential laboratories identified to have the necessary knowledge and instrumentation. Nine laboratories worldwide confirmed their participation in the exercise. The compounds selected (based on the extent of use and laboratories capabilities) included cyclophosphamide, ifosfamide, 5-fluorouracil, gemcitabine, etoposide, methotrexate and cisplatinum. Samples of spiked waste (hospital and wastewater treatment plant effluent) and surface water, and additional non-spiked hospital wastewater, were prepared by the organising laboratory (Jožef Stefan Institute) and sent out to each participant partner for analysis. All analytical methods included solid phase extraction (SPE) and the use of surrogate/internal standards for quantification. Chemical analysis was performed using either liquid or gas chromatography mass (MS) or tandem mass (MS/MS) spectrometry. Cisplatinum was determined using inductively coupled plasma mass spectrometry (ICP-MS). A required minimum contribution of five laboratories meant that only cyclophosphamide, ifosfamide, methotrexate and etoposide could be included in the statistical evaluation. z-score and Q test revealed 3 and 4 outliers using classical and robust approach, respectively. The smallest absolute differences between the spiked values and the measured values were observed in the surface water matrix. The highest within-laboratory repeatability was observed for methotrexate in all three matrices (CV ≤ 12 %). Overall, inter-laboratory reproducibility was poor for all compounds and matrices (CV 27-143 %) with the only exception being methotrexate measured in the spiked hospital wastewater (CV = 8 %). Random and total errors were identified by means of Youden plots.

Motivation: RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein-RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein-RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed., Results: We have developed an integrative orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources and discover non-overlapping, class-specific RNA binding patterns of varying strengths. The orthogonality constraint halves the effective size of the factor model and outperforms other NMF models in predicting RBP interaction sites on RNA. We have integrated the largest data compendium to date, which includes 31 CLIP experiments on 19 RBPs involved in splicing (such as hnRNPs, U2AF2, ELAVL1, TDP-43 and FUS) and processing of 3'UTR (Ago, IGF2BP). We show that the integration of multiple data sources improves the predictive accuracy of retrieval of RNA binding sites. In our study the key predictive factors of protein-RNA interactions were the position of RNA structure and sequence motifs, RBP co-binding and gene region type. We report on a number of protein-specific patterns, many of which are consistent with experimentally determined properties of RBPs., Availability and Implementation: The iONMF implementation and example datasets are available at https://github.com/mstrazar/ionmf, Contact: : tomaz.curk@fri.uni-lj.si, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

The disintegration of raw sludge is very important for enhancement of the biogas production in anaerobic digestion process as it provides easily degradable substrate for microorganisms to perform maximum sludge treatment efficiency and stable digestion of sludge at lower costs. In the present study the disintegration was studied by using a novel rotation generator of hydrodynamic cavitation (RGHC). At the first stage the analysis of hydrodynamics of the RGHC were made with tap water, where the cavitation extent and aggressiveness was evaluated. At the second stage RGHC was used as a tool for pretreatment of a waste-activated sludge (WAS), collected from wastewater treatment plant (WWTP). In case of WAS the disintegration rate was measured, where the soluble chemical oxygen demand (SCOD) and soluble Kjeldahl nitrogen were monitored and microbiological pictures were taken. The SCOD increased from initial 45 mg/L up to 602 mg/L and 12.7% more biogas has been produced by 20 passes through RGHC. The results were obtained on a pilot bioreactor plant, volume of 400 L., (Copyright © 2015. Published by Elsevier B.V.)

The yeast Saccharomyces cerevisiae is one of the oldest and most frequently used microorganisms in biotechnology with successful applications in the production of both bulk and fine chemicals. Yet, yeast researchers are faced with the challenge to further its transition from the old workhorse to a modern cell factory, fulfilling the requirements for next generation bioprocesses. Many of the principles and tools that are applied for this development originate from the field of synthetic biology and the engineered strains will indeed be synthetic organisms. We provide an overview of the most important aspects of this transition and highlight achievements in recent years as well as trends in which yeast currently lags behind. These aspects include: the enhancement of the substrate spectrum of yeast, with the focus on the efficient utilization of renewable feedstocks, the enhancement of the product spectrum through generation of independent circuits for the maintenance of redox balances and biosynthesis of common carbon building blocks, the requirement for accurate pathway control with improved genome editing and through orthogonal promoters, and improvement of the tolerance of yeast for specific stress conditions. The causative genetic elements for the required traits of the future yeast cell factories will be assembled into genetic modules for fast transfer between strains. These developments will benefit from progress in bio-computational methods, which allow for the integration of different kinds of data sets and algorithms, and from rapid advancement in genome editing, which will enable multiplexed targeted integration of whole heterologous pathways. The overall goal will be to provide a collection of modules and circuits that work independently and can be combined at will, depending on the individual conditions, and will result in an optimal synthetic host for a given production process.

Waste water treatment plant (WWTP) is considered as an important source of surface water contamination by enteric pathogens. In this study, we describe the occurrence of enteric viruses (group A rotaviruses, noroviruses, astroviruses, sapoviruses, hepatitis A virus, and hepatitis E virus) and Clostridium difficile in the effluent of a wastewater treatment plant during a 1-year period. Enteric viruses were simultaneously and efficiently concentrated in a single step using methacrylate monolithic chromatographic support. Rotaviruses, noroviruses (genogroup I and II), and sapoviruses were detected in all 12 concentrated samples, whereas astroviruses were not detected in August and September and hepatitis A and E viruses were not detected at all. Clostridium difficile was detected in all samples and altogether 121 strains were isolated and grouped into 32 different ribotypes of which 014/020 and 010 were most prevalent. Pathogens detected in WWTP effluent partially reflect the epidemiological situation of enteric viruses and C. difficile in human population and open the discussion on implementation of possible techniques for virus and bacteria removal from WWTP effluent prior to release into the surface water system.

Bistable switches are fundamental regulatory elements of complex systems, ranging from electronics to living cells. Designed genetic toggle switches have been constructed from pairs of natural transcriptional repressors wired to inhibit one another. The complexity of the engineered regulatory circuits can be increased using orthogonal transcriptional regulators based on designed DNA-binding domains. However, a mutual repressor-based toggle switch comprising DNA-binding domains of transcription-activator-like effectors (TALEs) did not support bistability in mammalian cells. Here, the challenge of engineering a bistable switch based on monomeric DNA-binding domains is solved via the introduction of a positive feedback loop composed of activators based on the same TALE domains as their opposing repressors and competition for the same DNA operator site. This design introduces nonlinearity and results in epigenetic bistability. This principle could be used to employ other monomeric DNA-binding domains such as CRISPR for applications ranging from reprogramming cells to building digital biological memory.

In this study, the removal of clofibric acid, ibuprofen, naproxen, ketoprofen, carbamazepine and diclofenac residues from wastewater, using a novel shear-induced cavitation generator has been systematically studied. The effects of temperature, cavitation time and H2O2 dose on removal efficiency were investigated. Optimisation (50°C; 15 min; 340 mg L(-1) of added H2O2) resulted in removal efficiencies of 47-86% in spiked deionised water samples. Treatment of actual wastewater effluents revealed that although matrix composition reduces removal efficiency, this effect can be compensated for by increasing H2O2 dose (3.4 g L(-1)) and prolonging cavitation time (30 min). Hydrodynamic cavitation has also been investigated as either a pre- or a post-treatment step to biological treatment. The results revealed a higher overall removal efficiency of recalcitrant diclofenac and carbamazepine, when hydrodynamic cavitation was used prior to as compared to post biological treatment i.e., 54% and 67% as compared to 39% and 56%, respectively. This is an important finding since diclofenac is considered as a priority substance to be included in the EU Water Framework Directive., (Copyright © 2013 Elsevier B.V. All rights reserved.)