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To evaluate the association of ultra-processed food (UPF) consumption with gains in weight and waist circumference, and incident overweight/obesity, in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) cohort. We applied FFQ at baseline and categorized energy intake by degree of processing using the NOVA classification. Height, weight and waist circumference were measured at baseline and after a mean 3·8-year follow-up. We assessed associations, through Poisson regression with robust variance, of UPF consumption with large weight gain (1·68 kg/year) and large waist gain (2·42 cm/year), both being defined as ≥90th percentile in the cohort, and with incident overweight/obesity. Brazil. Civil servants of Brazilian public academic institutions in six cities (n 11 827), aged 35-74 years at baseline (2008-2010). UPF provided a mean 24·6 (sd 9·6) % of ingested energy. After adjustment for smoking, physical activity, adiposity and other factors, fourth (>30·8 %) v. first (<17·8 %) quartile of UPF consumption was associated (relative risk (95 % CI)) with 27 and 33 % greater risk of large weight and waist gains (1·27 (1·07, 1·50) and 1·33 (1·12, 1·58)), respectively. Similarly, those in the fourth consumption quartile presented 20 % greater risk (1·20 (1·03, 1·40)) of incident overweight/obesity and 2 % greater risk (1·02; (0·85, 1·21)) of incident obesity. Approximately 15 % of cases of large weight and waist gains and of incident overweight/obesity could be attributed to consumption of >17·8 % of energy as UPF. Greater UPF consumption predicts large gains in overall and central adiposity and may contribute to the inexorable rise in obesity seen worldwide.

Force exertion is an integral part of cellular behavior. Traction force microscopy (TFM) has been instrumental for studying such forces, providing spatial force measurements at subcellular resolution. However, the applications of classical TFM are restricted by the typical planar geometry. Here, we develop a particle-based force sensing strategy for studying cellular interactions. We establish a straightforward batch approach for synthesizing uniform, deformable and tuneable hydrogel particles, which can also be easily derivatized. The 3D shape of such particles can be resolved with superresolution (<50 nm) accuracy using conventional confocal microscopy. We introduce a reference-free computational method allowing inference of traction forces with high sensitivity directly from the particle shape. We illustrate the potential of this approach by revealing subcellular force patterns throughout phagocytic engulfment and force dynamics in the cytotoxic T-cell immunological synapse. This strategy can readily be adapted for studying cellular forces in a wide range of applications. Traction force microscopy is an effective method for measuring cellular forces but it is limited by planar geometry. Here the authors develop a facile method to produce deformable hydrogel particles and a reference-free computational method to resolve surface traction forces from particle shape deformation.

Regulatory boards are promoting closed distribution systems (CDSs), which are different from traditional public-access networks, that can be owned and managed by energy communities (ECs). The inclusion of local renewable energy potential and an adequate schedule of storage devices in a CDS allow cooperation among the EC’s members in order to reduce operational expenditure (OPEX), providing internally competitive electricity prices with respect to those provided by publicly regulated networks and electricity markets. The CDS operators can assume a new role as the centralized energy dispatchers of generation and storage assets in order to maximize the profits of the members of the EC. This paper proposes an innovative optimal active and reactive power dispatch model for maximum community welfare conditions. A key difference between this proposal and existing social-welfare-based dispatches on public-access networks is the exclusion of the profit of the external wholesale electricity market. The focus of the proposed method is to maximize the welfare of all community members. A remuneration framework based on a collective EC with a single frontier is adopted, considering agreements between members based on locational marginal pricing (CDS-LMP). Results from an illustrative case study show a reduction of 50% in the EC’s OPEX with a payback time of 6 years for investments in CDSs, renewable sources, and storage.

Primary cilia are microtubule based sensory organelles important for receiving and processing cellular signals. Recent studies have shown that cilia also release extracellular vesicles (EVs). Because EVs have been shown to exert various physiological functions, these findings have the potential to alter our understanding of how primary cilia regulate specific signalling pathways. So far the focus has been on lgEVs budding directly from the ciliary membrane. An association between cilia and MVB-derived smEVs has not yet been described. We show that ciliary mutant mammalian cells demonstrate increased secretion of small EVs (smEVs) and a change in EV composition. Characterisation of smEV cargo identified signalling molecules that are differentially loaded upon ciliary dysfunction. Furthermore, we show that these smEVs are biologically active and modulate the WNT response in recipient cells. These results provide us with insights into smEV-dependent ciliary signalling mechanisms which might underly ciliopathy disease pathogenesis. Extracellular vesicles (EV) are known to be released from the primary cilium, but the role ciliary proteins play in EV biogenesis remains unexplored. Here, the authors demonstrate increased secretion of small EVs with altered cargo composition from cells with known ciliarelated mutations. Wnt related molecules made up a majority of altered cargo

Leaf springs are critical components for the railway vehicle safety in which they are installed. Although these components are produced in high-strength alloyed steel and designed to operate under cyclic loading conditions in the high-cyclic fatigue region, their failure is still possible, which can lead to economic and human catastrophes. The aim of this document was to precisely characterise the mechanical crack growth behaviour of the chromium–vanadium alloyed steel representative of leaf springs under cyclic conditions, that is, the crack propagation in mode I. The common fatigue crack growth prediction models (Paris and Walker) considering the effect of stress ratio and parameters such as propagation threshold, critical stress intensity factor and crack closure ratio were also determined using statistical methods, which resulted in good approximations with respect to the experimental results. Lastly, the fracture surfaces under the different test conditions were analysed using SEM, with no significant differences to declare. As a result of this research work, it is expected that the developed properties and fatigue crack growth prediction models can assist design and maintenance engineers in understanding fatigue behaviour in the initiation and propagation phase of cracks in leaf springs for railway freight wagons.

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

We disclose a direct approach to the diastereoselective synthesis of phosphorus substituted N-acylaziridines based on a one-pot ZnCl2-catalyzed Joullié–Ugi three-component reaction of phosphorylated 2H-azirines, carboxylic acids and isocyanides. Hence, this robust protocol offers rapid access to an array of N-acylaziridines in moderate-to-good yields and up to 98:2 dr for substrates over a wide scope. The relevance of this synthetic methodology was achieved via a gram-scale reaction and the further derivatization of the nitrogen-containing three-membered heterocycle. The diastereo- and regioselective ring expansion of the obtained N-acylaziridines to oxazole derivatives was accomplished in the presence of BF3·OEt2 as an efficient Lewid acid catalyst.

The Logarithmic Mean Divisia Index (LMDI) decomposition analysis is widely employed to examine the drivers behind changes in carbon dioxide emissions related to energy consumption. This analysis has been applied using single‐period, year‐by‐year, and multi‐period time frames worldwide. However, these time frames often overlook trend changes in carbon emission time series, which may lead to inaccurate and biased identification of driving factors. This study replicates previous findings and proposes a novel multi‐period methodology for defining time frames in decomposition analysis. The proposed approach addresses the limitations of traditional methods by accounting for trend changes in the time series and performing an exhaustive search to optimally identify the most suitable time frames for LMDI‐based decomposition. The methodology comprises two stages: the first generates an exhaustive list of possible time series partitions, and the second determines the optimal partition by minimizing the total mean square error (TMSE) using sequential linear models. The results, supported by computational performance tests, demonstrate that the proposed method effectively identifies optimal time frame definitions, making it particularly suitable for annualized case studies on carbon dioxide emissions decomposition in the context of the energy transition.

A crossing nose is a component of railway infrastructure subject to very severe loading conditions. Depending on the severity of these loads, the occurrence of structural fatigue, severe plastic deformation, or rolling fatigue may occur. Under fatigue conditions with high plastic deformation, cyclic plasticity approaches, together with local plasticity models, become more viable for mechanical design. In this work, the fatigue behavior in strain-controlled conditions of 51CrV4 steel, applicable to the crossing nose component, was evaluated. In this investigation, both strain-life and energy-life approaches were considered for fatigue prediction analysis. The results were considered through obtaining a Ramberg-Osgood cyclic elasto-plastic curve. Since this component is subject to cyclic loading, even if spaced in time, the isotropic and kinematic cyclic hardening behavior of the Chaboche model was subsequently analyzed, considering a comparative approach between experimental data and the FEM. As a result, the material properties and finite element model parameters presented in this work can contribute to the enrichment of the literature on strain-life fatigue and cyclic plasticity, and they could be applied in mechanical designs with 51CrV4 steel components or used in other future analyses.