Spatially differentiated midpoint indicator for marine eutrophication of waterborne emissions in Sweden

Purpose

In life cycle assessment (LCA), eutrophication is commonly assessed using site-generic characterisation factors, despite being a site-dependent environmental impact. The purpose of this study was to improve the environmental relevance of marine eutrophication impact assessment in LCA, particularly regarding the impact assessment of waterborne nutrient emissions from Swedish agriculture.

Methods

Characterisation factors were derived using site-dependent data on nutrient transport for all agricultural soils in Sweden, divided into 968 catchment areas, and considering the Baltic Sea, the receiving marine compartment, as both nitrogen- and phosphorus-limited. These new characterisation factors were then applied to waterborne nutrient emissions from typical grass ley and spring barley cultivation in all catchments.

Results and discussion

The site-dependent marine eutrophication characterisation factors obtained for nutrient leaching from soils varied between 0.056 and 0.986 kg N_eq/kg N and between 0 and 7.23 kg N_eq/kg P among sites in Sweden. On applying the new characterisation factors to spring barley and grass ley cultivation at different sites in Sweden, the total marine eutrophication impact from waterborne nutrient emissions for these crops varied by up to two orders of magnitude between sites. This variation shows that site plays an important role in determining the actual impact of an emission, which means that site-dependent impact assessment could provide valuable information to life cycle assessments and increase the relevance of LCA as a tool for assessment of product-related eutrophication impacts.

Conclusions

Characterisation factors for marine eutrophication impact assessment at high spatial resolution, considering both the site-dependent fate of eutrophying compounds and specific nutrient limitations in the recipient waterbody, were developed for waterborne nutrient emissions from agriculture in Sweden. Application of the characterisation factors revealed variations in calculated impacts between sites in Sweden, highlighting the importance of spatial differentiation of characterisation modelling within the scale of the impact.

     

Pathway-based analyses

Background

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing.

Methods

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

Results

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

Conclusions

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

     

ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level

Purpose

Life cycle impact assessment (LCIA) translates emissions and resource extractions into a limited number of environmental impact scores by means of so-called characterisation factors. There are two mainstream ways to derive characterisation factors, i.e. at midpoint level and at endpoint level. To further progress LCIA method development, we updated the ReCiPe2008 method to its version of 2016. This paper provides an overview of the key elements of the ReCiPe2016 method.

Methods

We implemented human health, ecosystem quality and resource scarcity as three areas of protection. Endpoint characterisation factors, directly related to the areas of protection, were derived from midpoint characterisation factors with a constant mid-to-endpoint factor per impact category. We included 17 midpoint impact categories.

Results and discussion

The update of ReCiPe provides characterisation factors that are representative for the global scale instead of the European scale, while maintaining the possibility for a number of impact categories to implement characterisation factors at a country and continental scale. We also expanded the number of environmental interventions and added impacts of water use on human health, impacts of water use and climate change on freshwater ecosystems and impacts of water use and tropospheric ozone formation on terrestrial ecosystems as novel damage pathways. Although significant effort has been put into the update of ReCiPe, there is still major improvement potential in the way impact pathways are modelled. Further improvements relate to a regionalisation of more impact categories, moving from local to global species extinction and adding more impact pathways.

Conclusions

Life cycle impact assessment is a fast evolving field of research. ReCiPe2016 provides a state-of-the-art method to convert life cycle inventories to a limited number of life cycle impact scores on midpoint and endpoint level.

     

The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA

Purpose

Pesticides are applied to agricultural fields to optimise crop yield and their global use is substantial. Their consideration in life cycle assessment (LCA) is affected by important inconsistencies between the emission inventory and impact assessment phases of LCA. A clear definition of the delineation between the product system model (life cycle inventory—LCI, technosphere) and the natural environment (life cycle impact assessment—LCIA, ecosphere) is missing and could be established via consensus building.

Methods

A workshop held in 2013 in Glasgow, UK, had the goal of establishing consensus and creating clear guidelines in the following topics: (1) boundary between emission inventory and impact characterisation model, (2) spatial dimensions and the time periods assumed for the application of substances to open agricultural fields or in greenhouses and (3) emissions to the natural environment and their potential impacts. More than 30 specialists in agrifood LCI, LCIA, risk assessment and ecotoxicology, representing industry, government and academia from 15 countries and four continents, met to discuss and reach consensus. The resulting guidelines target LCA practitioners, data (base) and characterisation method developers, and decision makers.

Results and discussion

The focus was on defining a clear interface between LCI and LCIA, capable of supporting any goal and scope requirements while avoiding double counting or exclusion of important emission flows/impacts. Consensus was reached accordingly on distinct sets of recommendations for LCI and LCIA, respectively, recommending, for example, that buffer zones should be considered as part of the crop production system and the change in yield be considered. While the spatial dimensions of the field were not fixed, the temporal boundary between dynamic LCI fate modelling and steady-state LCIA fate modelling needs to be defined.

Conclusions and recommendations

For pesticide application, the inventory should report pesticide identification, crop, mass applied per active ingredient, application method or formulation type, presence of buffer zones, location/country, application time before harvest and crop growth stage during application, adherence with Good Agricultural Practice, and whether the field is considered part of the technosphere or the ecosphere. Additionally, emission fractions to environmental media on-field and off-field should be reported. For LCIA, the directly concerned impact categories and a list of relevant fate and exposure processes were identified. Next steps were identified: (1) establishing default emission fractions to environmental media for integration into LCI databases and (2) interaction among impact model developers to extend current methods with new elements/processes mentioned in the recommendations.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

A framework for including enhanced exposure to naturally occurring radioactive materials (NORM) in LCA

Purpose

Despite advances in the development of impact categories for ionising radiation, the focus on artificial radionuclides produced in the nuclear fuel cycle means that the potential impacts resulting from increased exposure to naturally occurring radioactive materials (NORM) are still only covered to a limited degree in life cycle assessment (LCA). Here, we present a potential framework for the inclusion of the exposure routes and impact pathways particular to NORM in LCA.

Methods

We assess the potential magnitude of enhanced NORM exposure, particularly in light of the potential use of NORM residues in building materials, and set out the potential exposure routes that may exist. We then assess the current state of the art, in terms of available fate, exposure and damage models, both within and outside of the LCA sphere. Finally, these exposure routes and modelling techniques are combined in order to lay out a potential framework for NORM assessment in LCA, both in terms of impact on humans and ecosystems.

Results and discussion

Increased exposure to NORM radionuclides can result either from their release to the environment or their proximity to humans as they reside in stockpiles, landfills or products. The exposure route via products is considered to be increasingly significant in light of current attempts to incorporate technologically enhanced NORMs (TENORM) including bauxite residue into building materials, by groups such as the ETN-MSCA REDMUD project. Impact assessment models for NORM exposure are therefore required to avoid potential burden shifting in the assessment of such TENORM products. Models describing the fate of environmental releases, the exhalation of radon from building products and the shielding effects on landfills/stockpiles are required to assess potential exposure. Subsequently, models relating exposure to radiation sources and the effective internal and external dose received by receptors are required. Finally, an assessment of the damage caused to the receptors is desirable.

Conclusions

A sufficient suite of currently existing and internationally recognised models exist that can, with varying degrees of modification, form the building blocks of a comprehensive NORM characterisation method for LCA. The challenge ahead lies in consolidating these models, from disparate fields, into a coherent and generally applicable method for the assessment of enhanced NORM exposure in LCA.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Blue mussel (<Emphasis Type="Italic">Mytilus edulis</Emphasis>) bouchot culture in Mont-St Michel Bay: potential mitigation effects on climate change and eutrophication

Purpose

Bivalve production is an important aquaculture activity worldwide, but few environmental assessments have focused on it. In particular, bivalves’ ability to extract nutrients from the environment by intensely filtering water and producing a shell must be considered in the environmental assessment.

Methods

LCA of blue mussel bouchot culture (grown out on wood pilings) in Mont Saint-Michel Bay (France) was performed to identify its impact hotspots. The chemical composition of mussel flesh and shell was analyzed to accurately identify potential positive effects on eutrophication and climate change. The fate of mussel shells after consumption was also considered.

Results and discussion

Its potential as a carbon-sink is influenced by assumptions made about the carbon sequestration in wooden bouchots and in the mussel shell. The fate of the shells which depends on management of discarded mussels and household waste plays also an important role. Its carbon-sink potential barely compensates the climate change impact induced by the use of fuel used for on-site transportation. The export of N and P in mussel flesh slightly decreases potential eutrophication. Environmental impacts of blue mussel culture are determined by the location of production and mussel yields, which are influenced by marine currents and the distance to on-shore technical base.

Conclusions

Bouchot mussel culture has low environmental impacts compared to livestock systems, but the overall environmental performances depend on farming practices and the amount of fuel used. Changes to the surrounding ecosystem induced by high mussel density must be considered in future LCA studies.

     

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

Background

Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.

Methods

A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).

Results

Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.

Conclusions

The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.

     

Identification of a urine metabolite constellation characteristic for kidney allograft rejection

Introduction

Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.

Objectives

We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.

Methods

NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.

Results

We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).

Conclusion

A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.

     

Developing characterisation factors for land fragmentation impacts on biodiversity in LCA: key learnings from a sugarcane case study

Purpose

Habitat change was identified by the Millennium Ecosystem Assessment as the main direct driver of biodiversity loss. However, while habitat loss is already implemented in Life Cycle Impact Assessment (LCIA) methods, the additional impact on biodiversity due to habitat fragmentation is not assessed yet. Thus, the goal of this study was to include fragmentation effects from land occupation and transformation at both midpoint and endpoint levels in LCIA.

Methods

One promising metric, combining the landscape spatial configuration with species characteristics, is the metapopulation capacity λ, which can be used to rank landscapes in terms of their capacity to support viable populations spatially structured. A methodology to derive worldwide regionalised fragmentation indexes based on λ was used and combined with the Species Fragmented-Area Relationship (SFAR), which relies on λ to assess a species loss due to fragmentation. We adapted both developments to assess fragmentation impacts due to land occupation and transformation at both midpoint and endpoint levels in LCIA. An application to sugarcane production occurring in different geographical areas, more or less sensitive to land fragmentation, was performed.

Results and discussion

The comparison to other existing LCIA indicators highlighted its great potential for complementing current assessments through fragmentation effect inclusion. Last, both models were discussed through the evaluation grid used by the UNEP-SETAC land use LCIA working group for biodiversity impact assessment models.

Conclusions

Midpoint and endpoint characterisation factors were successfully developed to include the impacts of habitat fragmentation on species in LCIA. For now, they are provided for bird species in all forest ecoregions belonging to the biodiversity hotspots. Further work is required to develop characterisation factors for all taxa and all terrestrial ecoregions.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Regionalization of agri-food life cycle assessment: a review of studies in Portugal and recommendations for the future

Purpose

One of the main trends in life cycle assessment (LCA) today is towards increased regionalization in inventories and impact assessment methods. LCA studies require the collection of activity data but also of increasingly region-specific background data to accurately depict supply chain processes and enable the application of an increasing number of geographically explicit impact assessment models. This is particularly important for agri-food products. In this review, we assess progress in Portugal towards this goal and provide recommendations for future developments.

Methods

We perform a comprehensive review of available LCA studies conducted for Portuguese agri-food products, in order to evaluate the current state of Portuguese agri-food LCA. Among other issues, we assess availability of data, methods used, level of regionalization, impact assessment model relevance and coherence for inter-product comparability. We also provide conclusions and recommendations based on recent developments in the field.

Results and discussion

We found 22 LCA studies, covering 22 different products. The analysis of these studies reveals limitations in inter-study comparability. The main challenges have to do with a lack of country-specific foreground data sources applied consistently in the studies found, with discrepancies in impact assessment categories, and with the use of simple functional units that may misrepresent the product analyzed.

Conclusions

We conclude that Portuguese agri-food LCA studies do not have a systematic and country-scale approach in order to guarantee regional accuracy and comparability. We propose a research strategy to engage the Portuguese agri-food LCA community in devising a consistent framework before practical application studies are conducted.

     

Consistent characterisation factors at midpoint and endpoint relevant to agricultural water scarcity arising from freshwater consumption

Purpose

The shortage of agricultural water from freshwater sources is a growing concern because of the relatively large amounts needed to sustain food production for an increasing population. In this context, an impact assessment methodology is indispensable for the identification and assessment of the potential consequences of freshwater consumption in relation to agricultural water scarcity. This paper reports on the consistent development of midpoint and endpoint characterisation factors (CFs) for assessing these impacts.

Methods

Midpoint characterisation factors focus specifically on shortages in food production resulting from agricultural water scarcity. These were calculated by incorporating country-specific compensation factors for physical availability of water resources and socio-economic capacity in relation to the irrigation water demand for agriculture. At the endpoint, to reflect the more complex impact pathways from food production losses to malnutrition damage from agricultural water scarcity, international food trade relationships and economic adaptation capacity were integrated in the modelling with measures of nutritional vulnerability for each country.

Results and discussion

The inter-country variances of CFs at the midpoint revealed by this study were larger than those derived using previously developed methods, which did not integrate compensation processes by food stocks. At the endpoint level, both national and trade-induced damage through international trade were quantified and visualised. Distribution of malnutrition damage was also determined by production and trade balances for commodity groups in water-consuming countries, as well as dependency on import ratios for importer countries and economic adaptation capacity in each country. By incorporating the complex relationships between these factors, estimated malnutrition damage due to freshwater consumption at the country scale showed good correlation with total reported nutritional deficiency damage.

Conclusions

The model allows the establishment of consistent CFs at the midpoint and endpoint for agricultural water scarcity resulting from freshwater consumption. The complex relationships between food production supply and nutrition damage can be described by considering the physical and socio-economic parameters used in this study. Developed CFs contribute to a better assessment of the potential impacts associated with freshwater consumption in global supply chains and to life cycle assessment and water footprint assessments.

     

Metabolites predict cardiovascular disease events in persons living with HIV: a pilot case–control study

Introduction

Persons living with HIV (PLWH) are at higher risk for cardiovascular disease (CVD) events than uninfected persons. Current risk-stratification methods to define PLWH at highest risk for CVD events are lacking.

Methods

Using tandem flow injection mass spectrometry, we quantified plasma levels of 60 metabolites in 24 matched pairs of PLWH [1:1 with and without known coronary artery disease (CAD)]. Metabolite levels were reduced to interpretable factors using principal components analysis.

Results

Factors derived from short-chain dicarboxylacylcarnitines (SCDA) (p?=?0.08) and glutamine/valine (p?=?0.003) were elevated in CAD cases compared to controls.

Conclusion

SCDAs and glutamine/valine may be valuable markers of cardiovascular risk among persons living with HIV in the future, pending validation in larger cohorts.

     

Quantitative analysis of tetrahydrofolate metabolites from <Emphasis Type="Italic">clostridium autoethanogenum</Emphasis>

Introduction

Quantification of tetrahydrofolates (THFs), important metabolites in the Wood–Ljungdahl pathway (WLP) of acetogens, is challenging given their sensitivity to oxygen.

Objective

To develop a simple anaerobic protocol to enable reliable THFs quantification from bioreactors.

Methods

Anaerobic cultures were mixed with anaerobic acetonitrile for extraction. Targeted LC–MS/MS was used for quantification.

Results

Tetrahydrofolates can only be quantified if sampled anaerobically. THF levels showed a strong correlation to acetyl-CoA, the end product of the WLP.

Conclusion

Our method is useful for relative quantification of THFs across different growth conditions. Absolute quantification of THFs requires the use of labelled standards.

     

Investigation of the relationship between cell surface hydrophobicity and demulsifying capability of biodemulsifier-producing bacteria

Background

Cell surface hydrophobicity (CSH) is one of the key physicochemical features of biodemulsifier-producing bacteria that influence their demulsification capability maintenance in petroleum contaminated environments.

Methods

In present study, biodemulsifier-producing bacteria were isolated from petroleum contaminated environments using different isolation media and the correlation between their CSH and demulsifying ability was investigated. The demulsifying ability of isolates was measured through demulsification tests on water in kerosene emulsions. The microbial adhesion to the hydrocarbon (MATH) assay was used to denote their CSH.

Results

The evaluation of CSH showed that majority of biodemulsifier producing bacteria have high CSH which indicating a positive correlation between CSH and demulsifying capability.

Conclusions

According to these results it can be concluded that CSH can be used as an indicator for assessment of biodemulsifier-producing bacteria and screening of new isolates for their biodemulsifier production.

     

Predicting adverse drug reactions through interpretable deep learning framework

Background

Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.

Methods

In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Results

We evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.

Conclusions

The deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.