Low-Intensity Exercise Induces Acute Shifts In Liver And Skeletal Muscle Substrate Metabolism But Not Chronic Adaptations In Tissue Oxidative Capacity

Adaptations in hepatic and skeletal muscle substrate metabolism following acute and chronic (6 wk; 5 days/wk; 1 h/day) low-intensity treadmill exercise were tested in healthy male C57BL/6J mice. Low-intensity exercise maximizes lipid utilization; therefore, we hypothesized pathways involved in lipid metabolism would be most robustly affected. Acute exercise nearly depleted liver glycogen immediately postexercise (0 h), whereas hepatic triglyceride (TAG) stores increased in the early stages after exercise (0-3 h). Also, hepatic peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1 alpha) gene expression and fat oxidation (mitochondrial and peroxisomal) increased immediately postexercise (0 h), whereas carbohydrate and amino acid oxidation in liver peaked 24-48 h later. Alternatively, skeletal muscle exhibited a less robust response to acute exercise as stored substrates (glycogen and TAG) remained unchanged, induction of PGC-1 alpha gene expression was delayed (up at 3 h), and mitochondrial substrate oxidation pathways (carbohydrate, amino acid, and lipid) were largely unaltered. Peroxisomal lipid oxidation exhibited the most dynamic changes in skeletal muscle substrate metabolism after acute exercise; however, this response was also delayed (peaked 3-24 h postexercise), and expression of peroxisomal genes remained unaffected. Interestingly, 6 wk of training at a similar intensity limited weight gain, increased muscle glycogen, and reduced TAG accrual in liver and muscle; however, substrate oxidation pathways remained unaltered in both tissues. Collectively, these results suggest changes in substrate metabolism induced by an acute low-intensity exercise bout in healthy mice are more rapid and robust in liver than in skeletal muscle; however, training at a similar intensity for 6 wk is insufficient to induce remodeling of substrate metabolism pathways in either tissue. NEW & NOTEWORTHY Effects of low-intensity exercise on substrate metabolism pathways were tested in liver and skeletal muscle of healthy mice. This is the first study to describe exercise-induced adaptations in peroxisomal lipid metabolism and also reports comprehensive adaptations in mitochondrial substrate metabolism pathways (carbohydrate, lipid, and amino acid). Acute low-intensity exercise induced shifts in mitochondrial and peroxisomal metabolism in both tissues, but training at this intensity did not induce adaptive remodeling of metabolic pathways in healthy mice

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

Agricultural water management (AWM) is an interdisciplinary concern, cutting across traditional domains such as agronomy, climatology, geology, economics, and sociology. Each of these disciplines has developed numerous process-based and empirical models for AWM. However, models that simulate all major hydrologic, water quality, and crop growth processes in agricultural systems are still lacking. As computers become more powerful, more researchers are choosing to integrate existing models to account for these major processes rather than building new cross-disciplinary models. Model integration carries the hope that, as in a real system, the sum of the model will be greater than the parts. However, models based upon simplified and unrealistic assumptions of physical or empirical processes can generate misleading results which are not useful for informing policy. In this article, we use literature and case studies from the High Plains Aquifer and Southeastern United States regions to elucidate the challenges and opportunities associated with integrated modeling for AWM and recommend conditions in which to use integrated models. Additionally, we examine the potential contributions of integrated modeling to AWM — the actual practice of conserving water while maximizing productivity

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors

Toward standard practices for sharing computer code and programs in neuroscience

Computational techniques are central in many areas of neuroscience and are relatively easy to share. This paper describes why computer programs underlying scientific publications should be shared and lists simple steps for sharing. Together with ongoing efforts in data sharing, this should aid reproducibility of research.This article is based on discussions from a workshop to encourage sharing in neuroscience, held in Cambridge, UK, December 2014. It was financially supported and organized by the International Neuroinformatics Coordinating Facility (http://www.incf.org), with additional support from the Software Sustainability institute (http://www.software.ac.uk). M.H. was supported by funds from the German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences

Global sea-surface iodide observations, 1967-2018

The marine iodine cycle has significant impacts on air quality and atmospheric chemistry. Specifically, the reaction of iodide with ozone in the top few micrometres of the surface ocean is an important sink for tropospheric ozone (a pollutant gas) and the dominant source of reactive iodine to the atmosphere. Sea surface iodide parameterisations are now being implemented in air quality models, but these are currently a major source of uncertainty. Relatively little observational data is available to estimate the global surface iodide concentrations, and this data has not hitherto been openly available in a collated, digital form. Here we present all available sea surface (<20 m depth) iodide observations. The dataset includes values digitised from published manuscripts, published and unpublished data supplied directly by the originators, and data obtained from repositories. It contains 1342 data points, and spans latitudes from 70°S to 68°N, representing all major basins. The data may be used to model sea surface iodide concentrations or as a reference for future observations

Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions