Assessment of agricultural adaptations to climate change from a water-energy-food nexus perspective

Adapting agriculture to climate change without deteriorating natural resources (e.g., water and energy) is critical to sustainable development. In this paper, we first comprehensively evaluate six agricultural adaptations in response to climate change (2021–2050) through the lens of the water-energy-food (WEF) nexus in Saskatchewan, Canada, using a previously developed nexus model—WEF-Sask. The adaptations involve agronomic measures (early planting date, reducing soil evaporation, irrigation expansion), genetic improvement (cultivars with larger growing degree days (GDD) requirement), and combinations of individual adaptations. The results show that the selected adaptations compensate for crop yield losses (wheat, canola, pea), caused by climate change, to various extents. However, from a nexus perspective, there are mixed effects on water productivity (WP), total agricultural water (green and blue) use, energy consumption for irrigation, and hydropower generation. Individual adaptations such as early planting date and increased GDD requirement compensate for yield losses in both rainfed (0–60 %) and irrigated (18–100 %) conditions with extra use of green water (5–7 %), blue water (2–14 %), and energy for irrigation (2–14 %). Reducing soil water evaporation benefits the overall WEF nexus by compensating for rainfed yield losses (25–82 %) with less use of blue water and energy consumption for irrigation. The combination of the above three adaptations has the potential to sustain agricultural production in water-scarce regions. If irrigation expansion is also included, the combined adaptation almost fully offsets agricultural production losses from climate change but significantly increases blue water use (143–174 %) and energy consumption for irrigation while reducing hydropower production (3 %). This study provides an approach to comprehensively evaluating agricultural adaptation strategies, in response to climate change, and insights to inform decision-makers

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.</p

Dietary Restriction Depends on Nutrient Composition to Extend Chronological Lifespan in Budding Yeast Saccharomyces cerevisiae

10.1371/journal.pone.0064448PLoS ONE85

Plasma Metabolomic Profiles Reflective of Glucose Homeostasis in Non-Diabetic and Type 2 Diabetic Obese African-American Women

Insulin resistance progressing to type 2 diabetes mellitus (T2DM) is marked by a broad perturbation of macronutrient intermediary metabolism. Understanding the biochemical networks that underlie metabolic homeostasis and how they associate with insulin action will help unravel diabetes etiology and should foster discovery of new biomarkers of disease risk and severity. We examined differences in plasma concentrations of >350 metabolites in fasted obese T2DM vs. obese non-diabetic African-American women, and utilized principal components analysis to identify 158 metabolite components that strongly correlated with fasting HbA1c over a broad range of the latter (r = −0.631; p<0.0001). In addition to many unidentified small molecules, specific metabolites that were increased significantly in T2DM subjects included certain amino acids and their derivatives (i.e., leucine, 2-ketoisocaproate, valine, cystine, histidine), 2-hydroxybutanoate, long-chain fatty acids, and carbohydrate derivatives. Leucine and valine concentrations rose with increasing HbA1c, and significantly correlated with plasma acetylcarnitine concentrations. It is hypothesized that this reflects a close link between abnormalities in glucose homeostasis, amino acid catabolism, and efficiency of fuel combustion in the tricarboxylic acid (TCA) cycle. It is speculated that a mechanism for potential TCA cycle inefficiency concurrent with insulin resistance is “anaplerotic stress” emanating from reduced amino acid-derived carbon flux to TCA cycle intermediates, which if coupled to perturbation in cataplerosis would lead to net reduction in TCA cycle capacity relative to fuel delivery

dbpRisk: Disinfection By-Product Risk Estimation

This work describes a new open-source software platform, the dbpRisk software, for conducting simulation experiments in order to model the formation for disinfection by-product in drinking water distribution networks under various conditions and uncertainties. The goal is to identify the risk-level at each node location, contributing in the enhancement of consumer safety. The use of the dbpRisk software is demonstrated using a real water distribution network model from the Nicosia water transport network. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-31664-2_