Search

Reducing food loss and waste (FLW) could lessen the environmental impacts of food systems and improve food security. However, rebound effects-whereby efficiency improvements cause price decreases and consumption increases-may offset some avoided FLW. Here we model rebounds in food consumption under a scenario of costless FLW reduction. We project that consumption rebound could offset 53-71% of avoided FLW. Such rebounds would imply similar percentage reductions in environmental benefits (carbon emissions, land use, water use) and improvements in food security benefits (increased calorie availability), highlighting a tension between these two objectives. Evidence from energy systems suggests that indirect effects not included in our analysis could further increase rebounds. However, costs of reducing FLW would reduce rebounds. Rebound effects are therefore important to consider in efforts aimed at reducing FLW., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

In comparison with protein, dietary fat receives little attention in the food system sustainability literature, although we calculate that the average consumption of fats in many populous regions of the world is below nutritional recommendations. Animal products are the major source of dietary fat, particularly in regions with excess fat consumption. We estimate that an additional 45 Mt of dietary fat per year need to be produced and consumed for the global population to reach recommended levels of fat consumption, and we review different strategies to fill this gap sustainably. These strategies include diverting oils currently used for energy production to human consumption, increasing palm oil and peanut oil yields while avoiding further deforestation, developing sustainable cropping systems for the production of rapeseed and soybean oils, increasing the consumption of whole soybeans and derived products, and expanding the use of animal fats already produced., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models.

Food waste undermines long-term resilience of the global food system by aggravating ecosystem damage. The global community must therefore work to reduce the amount of food that gets wasted. However, we should be mindful of some potential conflicts between food waste reduction and food system resilience. Over-production and over-supply are a contributing cause of waste, yet they also provide resilience in the form of redundancy. In this paper, we examine individual interventions designed to minimise food waste by scoring their impact on different aspects of resilience. We find that there are strong synergistic elements and interventions that support short- and long-term resilience, such as improved storage, which reduces the need to provide a constant flow of 'surplus food' and replaces it with a stock of 'spare' food. Some interventions carry a risk of trade-offs due to possible losses of redundancy, and investment lock-in that may reduce the ability of farmers to adapt by changing what and where they farm. Trade-offs do not mean that those interventions should not be pursuit, but they should be recognised so that can be adequately addressed with complimentary actions. This review underlines the necessity of food-systems thinking and joined-up policy., Competing Interests: Tom Quested, Richard Swannell and Bojana Bajzelj are associated with WRAP, a UK-based charity with a longstanding history of working on food waste reduction. No other potential conflict of interest is declared., (© 2020 The Author(s).)

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming in vitro and in vivo studies do not reflect the complexity of the phenomenon. Complementary to wet lab methods are in silico approaches, which present a cost-efficient method for toxicity prediction. The aim of our study was to explore the capabilities of counter-propagation artificial neural networks (CPANNs) for the classification of an imbalanced dataset related to idiosyncratic drug-induced liver injury and to develop a model for prediction of the hepatotoxic potential of drugs. Genetic algorithm optimization of CPANN models was used to build models for the classification of drugs into hepatotoxic and non-hepatotoxic class using molecular descriptors. For the classification of an imbalanced dataset, we modified the classical CPANN training algorithm by integrating random subsampling into the training procedure of CPANN to improve the classification ability of CPANN. According to the number of models accepted by internal validation and according to the prediction statistics on the external set, we concluded that using an imbalanced set with balanced subsampling in each learning epoch is a better approach compared to using a fixed balanced set in the case of the counter-propagation artificial neural network learning methodology., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Water is essential not only to maintain the livelihoods of human beings but also to sustain ecosystems. Over the last few decades several global assessments have reviewed current and future uses of water, and have offered potential solutions to a possible water crisis. However, these have tended to focus on water supply rather than on the range of demands for all water services (including those of ecosystems). In this paper, a holistic global view of water resources and the services they provide is presented, using Sankey diagrams as a visualisation tool. These diagrams provide a valuable addition to the spatial maps of other global assessments, as they track the sources, uses, services and sinks of water resources. They facilitate comparison of different water services, and highlight trade-offs amongst them. For example, they reveal how increasing the supply of water resources to one service (crop production) can generate a reduction in provision of other water services (e.g., to ecosystem maintenance). The potential impacts of efficiency improvements in the use of water are also highlighted; for example, reduction in soil evaporation from crop production through better farming practices, or the results of improved treatment and re-use of return flows leading to reduction of delivery to final sinks. This paper also outlines the measures needed to ensure sustainable water resource use and supply for multiple competing services in the future, and emphasises that integrated management of land and water resources is essential to achieve this goal., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Mitigation plans to combat climate change depend on the combined implementation of many abatement options, but the options interact. Published anthropogenic emissions inventories are disaggregated by gas, sector, country, or final energy form. This allows the assessment of novel energy supply options, but is insufficient for understanding how options for efficiency and demand reduction interact. A consistent framework for understanding the drivers of emissions is therefore developed, with a set of seven complete inventories reflecting all technical options for mitigation connected through lossless allocation matrices. The required data set is compiled and calculated from a wide range of industry, government, and academic reports. The framework is used to create a global Sankey diagram to relate human demand for services to anthropogenic emissions. The application of this framework is demonstrated through a prediction of per-capita emissions based on service demand in different countries, and through an example showing how the "technical potentials" of a set of separate mitigation options should be combined.