Combining industrial ecology tools to assess potential greenhouse gas reductions of a circular economy: Method development and application to Switzerland

Industrial ecology (IE) methodologies, such as input/output or material flow analysis and life cycle assessment (LCA), are often used for the environmental evaluation of circular economy strategies. Up to now, an approach that utilizes these methods in a systematic, integrated framework for a holistic assessment of a geographic region's sustainable circular economy potential has been lacking. The approach developed in this study (IE4CE approach) combines IE methodologies to determine the environmental impact mitigation potential of circular economy strategies for a defined geographic region. The approach foresees five steps. First, input/output analysis helps identify sectors with high environmental impacts. Second, a refined analysis is conducted using material flow and LCA. In step 3, circular strategies are used for scenario design and evaluated in step 4. In step 5, the assessment results are compiled and compared across sectors. The approach was applied to a case study of Switzerland, analyzing 8 sectors and more than 30 scenarios in depth. Carbon capture and storage (CCS) from waste incineration, biogas and cement production, food waste prevention in households, hospitality and production, and the increased recycling of plastics had the highest mitigation potential. Most of the scenarios do not influence each other. One exception is the CCS scenarios: waste avoidance scenarios decrease the reduction potential of CCS. A combination of scenarios from different sectors, including their impact on the CCS scenario potential, led to an environmental impact mitigation potential of 11.9 Mt CO₂-eq for 2050, which equals 14% of Switzerland's current consumption-based impacts.     

A review of methods to trace material flows into final products in dynamic material flow analysis: From industry shipments in physical units to monetary input–output tables,Part 1

Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.     

PocketOptimizer 2.0: A modular framework for computer-aided ligand-binding design

The ability to design customized proteins to perform specific tasks is of great interest. We are particularly interested in the design of sensitive and specific small molecule ligand-binding proteins for biotechnological or biomedical applications. Computational methods can narrow down the immense combinatorial space to find the best solution and thus provide starting points for experimental procedures. However, success rates strongly depend on accurate modeling and energetic evaluation. Not only intra- but also intermolecular interactions have to be considered. To address this problem, we developed PocketOptimizer, a modular computational protein design pipeline, that predicts mutations in the binding pockets of proteins to increase affinity for a specific ligand. Its modularity enables users to compare different combinations of force fields, rotamer libraries, and scoring functions. Here, we present a much-improved version––PocketOptimizer 2.0. We implemented a cleaner user interface, an extended architecture with more supported tools, such as force fields and scoring functions, a backbone-dependent rotamer library, as well as different improvements in the underlying algorithms. Version 2.0 was tested against a benchmark of design cases and assessed in comparison to the first version. Our results show how newly implemented features such as the new rotamer library can lead to improved prediction accuracy. Therefore, we believe that PocketOptimizer 2.0, with its many new and improved functionalities, provides a robust and versatile environment for the design of small molecule-binding pockets in proteins. It is widely applicable and extendible due to its modular framework. PocketOptimizer 2.0 can be downloaded at https://github.com/Hoecker-Lab/pocketoptimizer .     

In Vitro Release of Endogenous β- Alanine, GABA, and Glutamate, and Electrophysiological Effect of β-Alanine in Pigeon Optic Tectum

The efflux of 20 amino acids, induced by either high K+ concentration or veratrine, was determined in pigeon tectal slices. Ca2+-dependent, K+-induced release of beta-alanine, gamma-aminobutyric acid (GABA), and glutamate was observed. Veratrine caused release of the same amino acids plus glycine in a tetrodotoxin-sensitive manner. beta-Alanine had a strong inhibitory effect on the activity of tectal neurons which was blocked by strychnine but not by bicuculline. The results indicated a transmitter function for beta-alanine in the optic tectum, and were consistent with the previously proposed transmitter role of GABA and glutamate in this structure.     

Distinguishing excess mutations and increased cell death based on variant allele frequencies

Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high.     

A method for the inference of cytokine interaction networks

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.     

Causes of variation in mineral soil C content and turnover in differently managed beech dominated forests

Background and aims

Forest soils are important carbon stores and considered as net CO₂ sinks over decadal to centennial time scales. Intensive forest management is thought to reduce the carbon sequestration potential of forest soils. Here we study the effects of decades of forest management (as unmanaged forest, forest under selection cutting, forest under age class management) on the turnover of mineral associated soil organic matter (MOM) in German beech (Fagus sylvatica L.) dominated forests.

Methods

Radiocarbon contents were determined by accelerator mass spectrometry (AMS) in 79 Ah horizon MOM fractions of Cambisols (n?=?13), Luvisols (n?=?51) and Stagnosols (n?=?15). Mean residence times (MRTs) for soil organic carbon (SOC) were estimated with a 2-pool model using the litter input derived from a forest inventory.

Results

MOM fractions from Ah horizons contained 64?±?8.8 % of the bulk SOC. The radiocarbon content of MOM fractions in Ah horizons, expressed as Δ¹⁴C, ranged between ?2.8?‰ and 114?‰ for the three soil groups. Almost all samples contained a detectable proportion of ‘bomb’ carbon fixed from the atmosphere since 1963. Under the assumption that depending on the soil texture between 19 % and 24 % of the SOC from the labile pool is transferred to the stable SOC pool, the corresponding MRTs ranged between 72 and 723 years, with a median of 164 years.

Conclusions

Our results indicate that the MOM fraction of Ah horizons from beech forests contained a high proportion of young carbon, but we did not find a significant decadal effect of forest management on the radiocarbon signature and related turnover times. Instead, both variables were controlled by clay contents and associated SOC concentrations (p?<?0.01). This underlines the importance of pedogenic properties for SOC turnover in the MOM fraction.     

Association of five SNPs with human hair colour in the Polish population

Twenty-two variants (single nucleotide polymorphisms – SNPs) of the genes involved in hair pigmentation (OCA2, HERC2, MC1R, SLC24A5, SLC45A2, TPCN2, TYR, TYRP1) were genotyped in a group of 186 Polish participants, representing a range of hair colours (45 red, 64 blond, 77 dark). A genotype-phenotype association analysis was performed.Using z-statistics we identified three variants highly associated with different hair colour categories (rs12913832:A>G in HERC2, rs1805007:T>C and rs1805008:C>T in MC1R). Two variants: rs1800401:C>T in OCA2 and rs16891982:C>G in SLC45A2 showed a high probability of a relation with hair colour, although that probability did not exceed the threshold of statistical significance after applying the Bonferroni correction. We created and validated mathematical logistic regression models in order to test the usefulness of the sets of polymorphisms for hair colour prediction in the Polish population. We subjected four models to stratified cross-validation. The first model consisted of three polymorphisms that proved to be important in the associative analysis. The second model included, apart from the mentioned polymorphisms, additionally rs16891982:C>G in SLC45A. The third model included, apart from the variants relevant in the associating analysis, rs1800401:C>T in OCA. The fourth model consisted of the set of polymorphisms from the first model supplemented with rs16891982:C>G in SLC45A and rs1800401:C>T in OCA. The validation of our models has shown that the inclusion of rs16891982:C>G in SLC45A and rs1800401:C>T in OCA increases the prediction of red hair in comparison with the algorithm including only rs12913832:A>G in HERC2, rs1805007:T>C and rs1805008:C>T in MC1R. The model consisting of all the five above-mentioned genetic variants has shown good prediction accuracies, expressed by the area under the curve (AUC) of the receiver operating characteristics: 0.84 for the red-haired, 0.82 for the dark-haired and 0.71 for the blond-haired.A genotype-phenotype association analysis brought results similar to those in other studies and confirmed the role of rs16891982:C>G, rs12913832:A>G, rs1805007:T>C and rs1805008:C>T in hair colour determination in the Polish population. Our study demonstrated for the first time the possibility of a share of the rs1800401:C>T SNP in the OCA2 gene in hair colour determination. Including this single nucleotide polymorphism in the actual hair colour predicting models would improve their predictive accuracy.     

Large‐scale disturbance legacies and the climate sensitivity of primary Picea abies forests

Determining the drivers of shifting forest disturbance rates remains a pressing global change issue. Large‐scale forest dynamics are commonly assumed to be climate driven, but appropriately scaled disturbance histories are rarely available to assess how disturbance legacies alter subsequent disturbance rates and the climate sensitivity of disturbance. We compiled multiple tree ring‐based disturbance histories from primary Picea abies forest fragments distributed throughout five European landscapes spanning the Bohemian Forest and the Carpathian Mountains. The regional chronology includes 11,595 tree cores, with ring dates spanning the years 1750–2000, collected from 560 inventory plots in 37 stands distributed across a 1,000 km geographic gradient, amounting to the largest disturbance chronology yet constructed in Europe. Decadal disturbance rates varied significantly through time and declined after 1920, resulting in widespread increases in canopy tree age. Approximately 75% of current canopy area recruited prior to 1900. Long‐term disturbance patterns were compared to an historical drought reconstruction, and further linked to spatial variation in stand structure and contemporary disturbance patterns derived from LANDSAT imagery. Historically, decadal Palmer drought severity index minima corresponded to higher rates of canopy removal. The severity of contemporary disturbances increased with each stand's estimated time since last major disturbance, increased with mean diameter, and declined with increasing within‐stand structural variability. Reconstructed spatial patterns suggest that high small‐scale structural variability has historically acted to reduce large‐scale susceptibility and climate sensitivity of disturbance. Reduced disturbance rates since 1920, a potential legacy of high 19th century disturbance rates, have contributed to a recent region‐wide increase in disturbance susceptibility. Increasingly common high‐severity disturbances throughout primary Picea forests of Central Europe should be reinterpreted in light of both legacy effects (resulting in increased susceptibility) and climate change (resulting in increased exposure to extreme events).     

Factors shaping community patterns of protists and bacteria on a European scale

Factors shaping community patterns of microorganisms are controversially discussed. Physical and chemical factors certainly limit the survival of individual taxa and maintenance of diversity. In recent years, a contribution of geographic distance and dispersal barriers to distribution patterns of protists and bacteria has been demonstrated. Organismic interactions such as competition, predation and mutualism further modify community structure and maintenance of distinct taxa. Here, we address the relative importance of these different factors in shaping protists and bacterial communities on a European scale using high-throughput sequencing data obtained from lentic freshwater ecosystems. We show that community patterns of protists are similar to those of bacteria. Our results indicate that cross-domain organismic factors are important variables with a higher influence on protists as compared with bacteria. Abiotic physical and chemical factors also contributed significantly to community patterns. The contribution of these latter factors was higher for bacteria, which may reflect a stronger biogeochemical coupling. The contribution of geographical distance was similar for both microbial groups.