The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake

In recent years, the growing number of available climate models and future scenarios has led to emergent constraints becoming a popular tool to constrain uncertain future projections. However, when emergent constraints are applied over large areas, it is unclear (i) if the well-performing models simulate the correct dynamics within the considered area, (ii) which key dynamical features the emerging constraint is stemming from, and (iii) if the observational uncertainty is low enough to allow for a considerable reduction in the projection uncertainties. We therefore propose to regionally optimize emergent relationships with the twofold goal to (a) identify key model dynamics associated with the emergent constraint and model inconsistencies around them and (b) provide key areas where a narrow observational uncertainty is crucial for constraining future projections. Here, we consider two previously established emergent constraints of the future carbon uptake in the North Atlantic (Goris et al., 2018). For the regional optimization, we use a genetic algorithm and pre-define a suite of shapes and size ranges for the desired regions. Independent of pre-defined shape and size range, the genetic algorithm persistently identifies the Gulf Stream region centred around 30∘ N as optimal as well as the region associated with broad interior southward volume transport centred around 26∘ N. Close to and within our optimal regions, observational data of volume transport are available from the RAPID array with relative low observational uncertainty. Yet, our regionally optimized emergent constraints show that additional measures of specific biogeochemical variables along the array will fundamentally improve our estimates of the future carbon uptake in the North Atlantic. Moreover, our regionally optimized emergent constraints demonstrate that models that perform well for the upper-ocean volume transport and related key biogeochemical properties do not necessarily reproduce the interior-ocean volume transport well, leading to inconsistent gradients of key biogeochemical properties. This hampers the applicability of emergent constraints over large areas and highlights the need to additionally evaluate spatial model features.</p

Weight-loss induced changes in physical activity and activity energy expenditure in overweight and obese subjects before and after energy restriction

Activity energy expenditure (AEE) is the component of daily energy expenditure that is mainly influenced by the amount of physical activity (PA) and by the weight of the body displaced. This study aimed at analyzing the effect of weight loss on PA and AEE. The body weight and PA of 66 overweight and obese subjects were measured at baseline and after 12 weeks of 67% energy restriction. PA was measured using a tri-axial accelerometer for movement registration (Tracmor) and quantified in activity counts. Tracmor recordings were also processed using a classification algorithm to recognize 6 common activity types engaged in during the day. A doubly-labeled water validated equation based on Tracmor output was used to estimate AEE. After weight loss, body weight decreased by 13±4%, daily activity counts augmented by 9% (95% CI: +2%, +15%), and this increase was weakly associated with the decrease in body weight (R(2) = 7%; P<0.05). After weight loss subjects were significantly (P<0.05) less sedentary (-26 min/d), and increased the time spent walking (+11 min/d) and bicycling (+4 min/d). However, AEE decreased by 0.6±0.4 MJ/d after weight loss. On average, a 2-hour/day reduction of sedentary time by increasing ambulatory and generic activities was required to restore baseline levels of AEE. In conclusion, after weight loss PA increased but the related metabolic demand did not offset the reduction in AEE due to the lower body weight. Promoting physical activity according to the extent of weight loss might increase successfulness of weight maintenance.Alberto G. Bonomi, Stijn Soenen, Annelies H. C. Goris, Klaas R. Westerter

Provably scale-covariant networks from oriented quasi quadrature measures in cascade

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl

How the structure of the large subunit controls function in an oxygen-tolerant [NiFe]-hydrogenase

Salmonella enterica is an opportunistic pathogen that produces a [NiFe]-hydrogenase under aerobic conditions. In the present study, genetic engineering approaches were used to facilitate isolation of this enzyme, termed Hyd-5. The crystal structure was determined to a resolution of 3.2 Å and the hydro-genase was observed to comprise associated large and small subunits. The structure indicated that His(229) from the large subunit was close to the proximal [4Fe–3S] cluster in the small subunit. In addition, His(229) was observed to lie close to a buried glutamic acid (Glu(73)), which is conserved in oxygen-tolerant hydrogenases. His(229) and Glu(73) of the Hyd-5 large subunit were found to be important in both hydrogen oxidation activity and the oxygen-tolerance mechanism. Substitution of His(229) or Glu(73) with alanine led to a loss in the ability of Hyd-5 to oxidize hydrogen in air. Furthermore, the H229A variant was found to have lost the overpotential requirement for activity that is always observed with oxygen-tolerant [NiFe]-hydrogenases. It is possible that His(229) has a role in stabilizing the super-oxidized form of the proximal cluster in the presence of oxygen, and it is proposed that Glu(73)could play a supporting role in fine-tuning the chemistry of His(229) to enable this function

Structure and vacancy distribution in copper telluride nanoparticles influence plasmonic activity in the near-infrared

Copper chalcogenides find applications in different domains including photonics, photothermal therapy and photovoltaics. CuTe nanocrystals have been proposed as an alternative to noble metal particles for plasmonics. Although it is known that deviations from stoichiometry are a prerequisite for plasmonic activity in the near-infrared, an accurate description of the material and its (optical) properties is hindered by an insufficient understanding of the atomic structure and the influence of defects, especially for materials in their nanocrystalline form. We demonstrate that the structure of Cu1.5±xTe nanocrystals can be determined using electron diffraction tomography. Real-space high-resolution electron tomography directly reveals the three-dimensional distribution of vacancies in the structure. Through first-principles density functional theory, we furthermore demonstrate that the influence of these vacancies on the optical properties of the nanocrystals is determined. Since our methodology is applicable to a variety of crystalline nanostructured materials, it is expected to provide unique insights concerning structure–property correlations

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations

Genetic variation in WNT9B increases relapse hazard in Multiple Sclerosis

Objective: Many multiple sclerosis (MS) genetic susceptibility variants have been identified, but understanding disease heterogeneity remains a key challenge. Relapses are a core feature of MS and a common primary outcome of clinical trials, with prevention of relapses benefiting patients immediately and potentially limiting long-term disability accrual. We aim to identify genetic variation associated with relapse hazard in MS by analyzing the largest study population to date.Methods: We performed a genomewide association study (GWAS) in a discovery cohort and investigated the genomewide significant variants in a replication cohort. Combining both cohorts, we captured a total of 2,231 relapses occurring before the start of any immunomodulatory treatment in 991 patients. For assessing time to relapse, we applied a survival analysis utilizing Cox proportional hazards models. We also investigated the association between MS genetic risk scores and relapse hazard and performed a gene ontology pathway analysis.Results: The low-frequency genetic variant rs11871306 within WNT9B reached genomewide significance in predicting relapse hazard and replicated (meta-analysis hazard ratio (HR) = 2.15, 95% confidence interval (CI) = 1.70-2.78, p = 2.07 × 10-10 ). A pathway analysis identified an association of the pathway "response to vitamin D" with relapse hazard (p = 4.33 × 10-6 ). The MS genetic risk scores, however, were not associated with relapse hazard.Interpretation: Genetic factors underlying disease heterogeneity differ from variants associated with MS susceptibility. Our findings imply that genetic variation within the Wnt signaling and vitamin D pathways contributes to differences in relapse occurrence. The present study highlights these cross-talking pathways as potential modulators of MS disease activity