ComplexBrowser: A Tool for Identification and Quantification of Protein Complexes in Large-scale Proteomics Datasets

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Highlights

• •Automated analysis of protein complexes in proteomic experiments.
• •Quantitative measurement of the coordinated changes in protein complex components.
• •Interactive visualizations for exploratory analysis of proteomic results.

     

DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics

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Highlights

• •Retention time shift can lead to inversion of elution order of peptides.
• •Global alignment methods are suboptimal for alignment of distant runs.
• •DIAlignR employs hybrid (global + local) RT alignment approach.
• •DIAlignR can align swapped peaks accurately across distant runs.

     

CORAL: Building up QSAR models for the chromosome aberration test

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.     

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings

Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom‐up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R² = 0.997). When compared with previous developed software with similar features (BRAT and EZ‐Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high‐throughput root length measurements while saving both labor and time.     

Disclosure of Product System Models in Life Cycle Assessment: Achieving Transparency and Privacy

Many of the challenges facing knowledge synthesis from life cycle assessment (LCA) studies stem from the inability of study authors and readers to formally agree on the structure and content of the product system models used to perform LCA computations. This article presents a framework for formally disclosing the foreground of an LCA study in a way that permits the computations to be inspected, verified, and reproduced by a reader, provided that the reader has access to the same life cycle inventory and impact characterization resources as the author. The framework can also be used to partition a study into public and private portions, allowing both portions to be critically reviewed but omitting the private information from the disclosure. A disclosure is made up of six components, including three lists of entities in the model and three sparse matrices describing their interconnections. The entity lists make reference to previously‐published resources, including background inventory databases and characterized elementary flows, and the disclosure framework requires both author and reader to agree on the meaning of each of these references. The framework contributes to ongoing efforts within and beyond industrial ecology to improve the reproducibility and verifiability of scholarly works, and if implemented, plots a course toward distributed, platform‐independent computation and validation of LCA results.     

Environmental analysis of packaging‐derived changes in food production and consumer behavior

This study analyzed the environmental impacts of packaging‐derived changes in food production and consumer behavior to assist packaging designers in making environmentally conscious decisions. Packaging can be functionalized to prevent food loss and waste (FLW), for example, extending the expiration date and apportioning the package size, but it can generate additional environmental impacts from changes in food and packaging production. Previous studies assessed additional impacts from packaging production; however, the effects of packaging functionalization are yet to be connected with food production and consumer behavior. To examine the effect of functionalization on these aspects, we analyzed packaging‐derived changes in food production for milk and cabbage products. The case study compared products with functionalized packaging that permits a longer expiration date or a smaller portion size to their base‐case products. Our results showed that the packaging‐derived changes increased the global warming potential (GWP) of food production more than other processes did. Thus, changes in food production weakened the effectiveness of the packaging functionalization to decrease the GWP. Moreover, the analysis of consumer behavior scenarios showed that consumers’ perception of the expiration date decisively influences the effectiveness of packaging functionalization. When consumers discarded food after the expiration date, provided they consumed in small quantities, the packaging functionalization reduced FLW. From the scenario analysis, we identified appropriate combinations of packaging functionalization and consumer behaviors to effectively decrease total GWP. With our expanded analysis, packaging designers can understand the effectiveness of their decisions on the product life cycle in reducing FLW and environmental impacts.     

Mapping the Influence of Food Waste in Food Packaging Environmental Performance Assessments

Scrutiny of food packaging environmental impacts has led to a variety of sustainability directives, but has largely focused on the direct impacts of materials. A growing awareness of the impacts of food waste warrants a recalibration of packaging environmental assessment to include the indirect effects due to influences on food waste. In this study, we model 13 food products and their typical packaging formats through a consistent life cycle assessment framework in order to demonstrate the effect of food waste on overall system greenhouse gas (GHG) emissions and cumulative energy demand (CED). Starting with food waste rate estimates from the U.S. Department of Agriculture, we calculate the effect on GHG emissions and CED of a hypothetical 10% decrease in food waste rate. This defines a limit for increases in packaging impacts from innovative packaging solutions that will still lead to net system environmental benefits. The ratio of food production to packaging production environmental impact provides a guide to predicting food waste effects on system performance. Based on a survey of the food LCA literature, this ratio for GHG emissions ranges from 0.06 (wine example) to 780 (beef example). High ratios with foods such as cereals, dairy, seafood, and meats suggest greater opportunity for net impact reductions through packaging‐based food waste reduction innovations. While this study is not intended to provide definitive LCAs for the product/package systems modeled, it does illustrate both the importance of considering food waste when comparing packaging alternatives, and the potential for using packaging to reduce overall system impacts by reducing food waste.     

A Carbon Footprint of High‐Speed Railways in China: A Case Study of the Beijing‐Shanghai Line

A carbon footprint (CF) assessment of Chinese high‐speed railways (HSRs) can help guide further development of the world's longest HSR network. In this research, a hybrid economic input‐output and life cycle assessment (EIO‐LCA) method was applied to estimate the CF of the Beijing‐Shanghai HSR line. Specific CFs were analyzed of different subsystems of the line, different stages of production, and three calculation scopes. Results showed that the annual CF of the Beijing‐Shanghai HSR is increasing, whereas the per‐passenger CF constantly declined between 2011 and 2014. Scope 1 emissions account for an average of 4% of the total annual CF, Scope 2 contribute 71%, and Scope 3 comprise 25%. Among the different stages, operation contributes the largest (71%), followed by construction (20%) and maintenance (9%). In the construction stage, the bridges have the largest CF, followed by trains, and then rails. A trade‐off exists between the increase in carbon emissions due to construction of bridges and the reduction in operation emissions affected by leveling changes in terrain. The Beijing‐Shanghai HSR line has a relatively higher per‐passenger CF than eight other HSR lines, which is largely due to China's coal‐based carbon‐intensive energy mix of electricity generation, high proportion of bridges, higher operating speed, and heavier train body. In the future, cleaner electricity supply options, more efficient raw material production, and improvement of trains are keys to reducing the CF of Chinese HSRs.     

Towards Productive Cities: Environmental Assessment of the Food‐Energy‐Water Nexus of the Urban Roof Mosaic

Cities are rapidly growing and need to look for ways to optimize resource consumption. Metropolises are especially vulnerable in three main systems, often referred to as the FEW (i.e., food, energy, and water) nexus. In this context, urban rooftops are underutilized areas that might be used for the production of these resources. We developed the Roof Mosaic approach, which combines life cycle assessment with two rooftop guidelines, to analyze the technical feasibility and environmental implications of producing food and energy, and harvesting rainwater on rooftops through different combinations at different scales. To illustrate, we apply the Roof Mosaic approach to a densely populated neighborhood in a Mediterranean city. The building‐scale results show that integrating rainwater harvesting and food production would avoid relatively insignificant emissions (13.9–18.6 kg CO₂ eq/inhabitant/year) in the use stage, but their construction would have low environmental impacts. In contrast, the application of energy systems (photovoltaic or solar thermal systems) combined with rainwater harvesting could potentially avoid higher CO₂ eq emissions (177–196 kg CO₂ eq/inhabitant/year) but generate higher environmental burdens in the construction phase. When applied at the neighborhood scale, the approach can be optimized to meet between 7% and 50% of FEW demands and avoid up to 157 tons CO₂ eq/year. This approach is a useful guide to optimize the FEW nexus providing a range of options for the exploitation of rooftops at the local scale, which can aid cities in becoming self‐sufficient, optimizing resources, and reducing CO₂ eq emissions.