bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the interplay between natural and intersexual selection on the evolution of a cognitive trait
Authors: Barou‐Dagues Marie , Dubois Frédérique 
Published: July 2022

AbstractThere has been an increased focus on the role of natural and sexual selection in shaping cognitive abilities, but the importance of the interaction between both forces remains largely unknown. Intersexual selection through female mate choice might be an important driver of the evolution of cognitive traits, especially in monogamous species, where females may obtain direct fitness benefits by choosing mates with better cognitive abilities. However, the importance given by female to male cognitive traits might vary among species and/or populations according to their life‐history traits and ecology. To disentangle the effects of natural and sexual selection, here we use an agent‐based simulation model and compare the model's predictions when females mate with the first randomly encountered male (i.e., under natural selection) versus when they choose among males based on their cognitive trait values (i.e., under natural and intersexual selection). Males and females are characterized, respectively, by their problem‐solving ability and assessment strategy. At each generation, agents go through (1) a choosing phase during which females assess the cognitive abilities of potential mates until eventually finding an acceptable one and (2) a reproductive phase during which all males compete for limited resources that are exploited at a rate, which depends on their cognitive abilities. Because males provide paternal care, the foraging success of mated males determines the breeding success of the pair through its effect on nestling provisioning efficiency. The model predicts that intersexual selection plays a major role in most ecological conditions, by either reinforcing or acting against the effect of natural selection. The latter case occurs under harsh environmental conditions, where intersexual selection contributes to maintaining cognitive diversity. Our findings thus demonstrate the importance of considering the interaction between both selective forces and highlight the need to build a conceptual framework to target relevant cognitive traits.
Guidelines for inflammation models in mice for food components
Authors: Han Yanhui , Itenberg Sasha A. , Wu Xian , Xiao Hang 
Published: July 2022

AbstractInflammation is one of the major health threats to humans, and it is associated with various chronic diseases, including metabolic syndrome, type 2 diabetes, cardiovascular diseases, and several types of cancer. For example, colonic inflammation can result in the damage of the intestinal barrier, as indicated by invasion of immune cells and reduced tight junctions. Inflammatory responses can also lead to erythema and edema on skin. The intervention of dietary components offers striking possibilities for the prevention of inflammation by modulating the inflammatory signaling pathways and microbiota–inflammation axis. Thus, the preclinical animal models are of critical importance for investigating the anti‐inflammatory effects of food components. This guideline introduces two inflammatory mouse models: dextran sulfate sodium‐induced colonic inflammation and 12‐O‐tetradecanoylphorbol‐13‐acetate‐induced skin inflammation. These mimic models offer the potential to assess food components in attenuating the colonic and skin inflammation in humans.
Income share agreements offer upskilling opportunities for adult learners
Authors: Sutton Halley 
Published: July 2022

SAN DIEGO — “There are always going to be resources for unemployed people. But where are the resources for those people who make minimum wage, or just above minimum wage? In San Diego, that's not going to take you far,” said Alastair Penny, Senior Manager at the San Diego Workforce Partnership. One such resource to help educate more of your community might be to invest in, or partner with, an organization that offers an income share agreement for college education.
Integrated Exposure and Algal Ecotoxicological Assessments of Effluents from Secondary and Advanced-Tertiary Wastewater-Treatment Plants
Authors: Palumbo Maria Teresa , Russo Simona , Polesello Stefano , Guzzella Licia , Roscioli Claudio , Marziali Laura , Valsecchi Lucia , Cappelli Francesca , Pascariello Simona , Tasselli Stefano , Villa Sara , Peruzzo Massimo , Culatina Stefano , Bellotti Giovanni , Turolla Andrea , Antonelli Manuela , Malpei Francesca , Valsecchi Sara 
Published: July 2022

Abstract
The great concern over the environmental impact of wastewaters has led to the designing of advanced treatment processes to upgrade conventional treatment plants and achieve a significant reduction of contaminants in receiving waters. In the present study we combined chemical and ecotoxicological analyses, aiming to evaluate the reduction of toxicity effects associated with the removal of micropollutants and to define the contribution of the detected compounds to the overall toxicity of the mixtures in a series of wastewater effluents collected from a secondary treatment (OUT 2) and from a tertiary activated carbon treatment (OUT 3) plant. The target compounds were selected after a screening procedure among pharmaceuticals, musk fragrances, and trace metals. The classical algal growth inhibition test was conducted on the original effluent samples and on different fractions obtained by solid-phase extraction (SPE) treatment. A good accordance was found between the removal of toxicity (30%–80%) and organic compounds (70%–80%) after the tertiary treatment, suggesting its high efficiency to improve the wastewater quality. The discrepancy between the contribution to the overall toxicity of the nonadsorbable compounds (i.e., inorganic or very polar organic compounds) as experimentally measured by the SPE bioassays (18%–76%) and calculated by the concentration addition approach (&gt;97%) could be mitigated by including the bioavailability correction in metal-toxicity modeling of wastewater mixtures. For the organic compounds, the toxic equivalency method enabled us to quantify the portion of toxicity explained by the detected chemicals in both OUT 2 (82%–104%) and OUT 3 (5%–57%), validating the selection of the target molecules. The applied integrating approach could be implemented by the inclusion of both additional target chemicals and toxicity endpoints. Environ Toxicol Chem 2022;41:2404–2419. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Advances and prospects of transcription‐factor‐based biosensors in high‐throughput screening for cell factories construction
Authors: Li Changfan , Wang Chang , Zhu Jiang , Xue Feng , Sun Xiaoman , Gu Yang 
Published: July 2022

AbstractNon/semi‐rational engineering strategies can improve the performance of microbial cell factories under the situations of indistinct metabolic regulations and interactions. However, generating positive variants through these strategies is remarkably low. Using traditional screening methods to evaluate the performance of mutants, such as mass spectrometry and chromatography, is low‐throughput, slow‐detection, and labor‐intensive. Specifically, the efficiency of high‐throughput screening (HTS) for strain screening could reach even more 103−106 strains/day, bridging the gap between non/semi‐rational engineering strategies and microbial cell factories construction. This review highlights transcription factor (TFs)‐based biosensors in the advances in developing synthetic biosensors for HTS. We hope that this review will help take full advantage of valuable TFs to guide the HTS technology and promote its development.
Effects of a single‐dose methylphenidate challenge on resting‐state functional connectivity in stimulant‐treatment naive children and adults with ADHD
Authors: Kaiser Antonia , Broeder Caroline , Cohen Jessica R. , Douw Linda , Reneman Liesbeth , Schrantee Anouk 
Published: July 2022

AbstractPrior studies suggest that methylphenidate, the primary pharmacological treatment for attention‐deficit/hyperactivity disorder (ADHD), alters functional brain connectivity. As the neurotransmitter systems targeted by methylphenidate undergo significant alterations throughout development, the effects of methylphenidate on functional connectivity may also be modulated by age. Therefore, we assessed the effects of a single methylphenidate challenge on brain network connectivity in stimulant‐treatment naïve children and adults with ADHD. We obtained resting‐state functional MRI from 50 boys (10–12 years of age) and 49 men (23–40 years of age) with ADHD (DSM IV, all subtypes), before and after an oral challenge with 0.5 mg/kg methylphenidate; and from 11 boys and 12 men as typically developing controls. Connectivity strength (CS), eigenvector centrality (EC), and betweenness centrality (BC) were calculated for the striatum, thalamus, dorsal anterior cingulate cortex (dACC), and prefrontal cortex (PFC). In line with our hypotheses, we found that methylphenidate decreased measures of connectivity and centrality in the striatum and thalamus in children with ADHD, but increased the same metrics in adults with ADHD. Surprisingly, we found no major effects of methylphenidate in the dACC and PFC in either children or adults. Interestingly, pre‐methylphenidate, participants with ADHD showed aberrant connectivity and centrality compared to controls predominantly in frontal regions. Our findings demonstrate that methylphenidate's effects on connectivity of subcortical regions are age‐dependent in stimulant‐treatment naïve participants with ADHD, likely due to ongoing maturation of dopamine and noradrenaline systems. These findings highlight the importance for future studies to take a developmental perspective when studying the effects of methylphenidate treatment.
Convective flow of a Williamson hybrid nanofluid in a porous medium through a cone and wedge with the effect of the shape of nanoparticles
Authors: Al‐Arabi Taghreed H. , Mahdy A. , Rashad Ahmed M. , Saad Wafaa , Nabwey Hossam A. 
Published: July 2022

AbstractThe performance of nanofluid in the heat transport phenomena showed satisfactory results, which have been considered by scientists and researchers. The dispersion of two nanoparticles to form a mixture which named as hybrid nanofluid. It has been proved experimentally that hybrid nanofluid has good thermal performance compared to unitary nanofluid. The present study inspected the convective flow of a Williamson hybrid nanofluid through a cone and wedge in a porous medium of different shapes of nanoparticles (cylindrical‐, spherical‐, blades‐, platelets‐, bricks‐) under the influence of a magnetic field. The partial differential equations were converted to an ordinary differential equation and the resulting equations were solved using the bvp4c MATLAB code. The effect of some important physical parameters on both the velocity and temperature profile was graphically plotted and the effect of the shape and size of nanoparticles was discussed, the numerical values of the friction coefficient and Nusselt number were obtained. As observed, the friction factor of Ag–TiO2–H2O hybrid nanofluid was more than that of TiO2–H2O nanofluid. A significant enhancement in bulk temperature is seen for the Ag–TiO2 hybrid and nanofluid formed by blade‐shaped nanoparticles followed by cylindrical, platelet, brick, and spherical nanoparticles. In addition, for a 5% volume fraction of using blade nanoparticles shape givean increase in skin friction factor about 4.8% compared tobrick hybrid nanoparticles. A comparative study of different forms for Ag‐TiO2was presented graphically.
Propelling steady growth and high‐quality development through deeper reform and more comprehensive opening up: Outlook, policy simulations, and reform implementation—A summary of the Annual SUFE Macroeconomic Report (2021–2022)
Authors: Huang Kevin X. D. , Tian Guoqiang , Zhao Lin 
Published: July 2022

AbstractChina's economy underwent a steady recovery in 2021. Investment grew steadily with structural improvement. Exports and imports surged while trade surplus expanded. On the other hand, although labor market conditions improved, income distribution worsened, contributing to sluggish growth in consumption, whereas the gap between consumer price index and producer price index widened, and the profits of enterprises of different sizes diverged, which may go beyond how they are correlated with the locations of the enterprises in the chain of production and trade. While proper liquidity was maintained with prudent monetary policy, risk spillover rose in the financial system, particularly for small and medium‐sized banks. Household and local government debts remained at relatively high levels, further dragging down growth in consumption and infrastructure investment. The “dual carbon” goals exerted downward pressure on near‐term growth in trading off their long‐term benefits. The economy also faced challenges in its external environment in the midst of the prolonged COVID‐19 pandemic aboard, trade protectionism, and the readjustment of the global value chain. Moreover, excessive supervision and inadequate implementation disturbed China's economy, resulting in declined market vitality and confidence of market participants. Based on the Institute for Advanced Research‐China Macroeconomic Model, the baseline real gross domestic product growth rate is projected to be 5.5% in 2022. Alternative scenario analyses and policy simulations are conducted, in addition to the benchmark forecast, to reflect the influences of various risks and possible favorable situations. The findings suggest that China should deepen reform and open up more comprehensively and initiatively, while special effort should be placed on providing accommodative policy and friendly public opinion environment, to facilitate steady growth and propel high‐quality development. A comprehensive macroeconomic governance framework with Chinese characteristics must be developed from systems thinking, to resolve the various issues, internal and external, cyclical and secular, structural and institutional, in an all‐inclusive and coherent manner.
Sustained release of sulforaphane by bioactive extracellular vesicles for neuroprotective effect on chick model
Authors: Shahlaei Mona , Saeidifar Maryam , Zamanian Ali 
Published: July 2022

AbstractNovel studies have shown neurological treatment possibilities with extracellular vesicles (EVs) as natural particles with a special composition that are produced by different cell types. Their stability, natural structure, composition, and bioavailability make them good candidates as drug vehicles. Here, EVs were isolated from amniotic fluid (AF) through differential centrifugation, and characterized for size (<200 nm), structure, and composition, their effectiveness on the human PC12 cell line, and brain of chick embryos exposed to sodium valproate (animal autistic model). Sulforaphane (SFN) was employed as a bioactive compound and then encapsulated into Evs using three methods including passive (incubation), active (sonication), and active‐passive (sonication‐incubation). Further, the loading and in vitro releases of SFN fitted the Korsmeyer–Peppas (R2 = 0.99) kinetic model by non‐Fickian diffusion case II (n = 0.44, passive loading) and Fickian diffusion case I (n = 0.41, active and active‐passive loading). SFN‐loaded EVs (SFN@EVs; 11 μM: 103 nM) stimulated hPC‐12 cell proliferation. The gene expression analysis revealed that SFN@EVs could upregulate Nrf2 and reduce IL‐6 expression. Eventually, histopathological results of the coronal cross‐section of the chick embryos brain showed treatment with SFN@EVs. This treatment illustrated normality in the gray and white matter and the orientation of the bipolar neurons. Our findings showed EVs' potentially acting as a gene expression regulator in autism spectrum disorder.
Segmentation of laser induced retinal lesions using deep learning (December 2021)
Authors: Gil Eddie M. , Keppler Mark , Boretsky Adam , Yakovlev Vladislav V. , Bixler Joel N. 
Published: July 2022

AbstractObjectiveDetection of retinal laser lesions is necessary in both the evaluation of the extent of damage from high power laser sources, and in validating treatments involving the placement of laser lesions. However, such lesions are difficult to detect using Color Fundus cameras alone. Deep learning‐based segmentation can remedy this, by highlighting potential lesions in the image.MethodsA unique database of images collected at the Air Force Research Laboratory over the past 30 years was used to train deep learning models for classifying images with lesions and for subsequent segmentation. We investigate whether transferring weights from models that learned classification would improve performance of the segmentation models. We use Pearson's correlation coefficient between the initial and final training phases to reveal how the networks are transferring features.ResultsThe segmentation models are able to effectively segment a broad range of lesions and imaging conditions.ConclusionDeep learning‐based segmentation of lesions can effectively highlight laser lesions, making this a useful tool for aiding clinicians.
Corrosion behavior and mechanism of the high‐strength low‐alloy steel joined by multilayer and multipass welding method
Authors: Zhang Huixia , Hao Fuyao , Zhang Yu , Li Xiangbo , Guo Han 
Published: July 2022

AbstractThe corrosion behavior and mechanism of the high‐strength low‐alloy steel‐welded joint fabricated by the multilayer and multipass welding method were investigated using a scanning Kelvin probe, electrochemical measurements, and so forth. The results revealed that the microstructure of the first layer welding zone was dominated by granular bainite and acicular ferrite and was fine and uniform, which exhibited the best corrosion resistance. Whereas, since the average cooling rate decreased with the increase of welding pass, the grain size of the second and third layer weldings gradually grew, and the voltaic potential gradually decreased. In addition, the microstructure of the heat‐affected zone (HAZ) changed from the tempered sorbite structure of the equilibrium phase to the granular bainite or bainite structure of the nonequilibrium phase under the action of heat transfer. The HAZ became the weakest link for corrosion of welded joint, on account of the nonequilibrium organization and galvanic coupling among base metal, weld metal and HAZ.
Exploring the Association between Habitual Food Intake and the Urine and Blood Metabolome in Adolescents and Young Adults: A Cohort Study
Authors: Brachem Christian , Oluwagbemigun Kolade , Langenau Julia , Weinhold Leonie , Alexy Ute , Schmid Matthias , Nöthlings Ute 
Published: July 2022

ScopeHabitual diet may be reflected in metabolite profiles that can improve accurate assessment of dietary exposure and further enhance our understanding of their link to health conditions. The study aims to explore the relationship of habitual food intake with blood and urine metabolites in adolescents and young adults.MethodsThe study population comprises 228 participants (94 males and 134 females) of the DONALD study. Dietary intake is assessed by yearly repeated 3d‐food records. Habitual diet is estimated as the average consumption of 23 food groups in adolescence. Using an untargeted metabolomics approach, the study quantifies 2638 metabolites in plasma and 1407 metabolites in urine. In each sex, unique diet–metabolite associations using orthogonal projection to latent structures (oPLS) and random forests (RF) is determined.ResultsSix metabolites in agreement between oPLS and RF in urine, one in female (vanillylmandelate to processed/other meat) and five in males (indole‐3‐acetamide, and N6‐methyladenosine to eggs; hippurate, citraconate/glutaconate, and X – 12111 to vegetables) are observed. No association in blood in agreement is observed.ConclusionA limited reflection of habitual food group intake by single metabolites in urine and not in blood is observed. The explored biomarkers should be confirmed in additional studies.
FReSCO: Flow Reconstruction and Segmentation for low‐latency Cardiac Output monitoring using deep artifact suppression and segmentation
Authors: Jaubert Olivier , Montalt‐Tordera Javier , Brown James , Knight Daniel , Arridge Simon , Steeden Jennifer , Muthurangu Vivek 
Published: July 2022

PurposeReal‐time monitoring of cardiac output (CO) requires low‐latency reconstruction and segmentation of real‐time phase‐contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for “FReSCO” (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring).MethodsDeep artifact suppression and segmentation U‐Nets were independently trained. Breath‐hold spiral phase‐contrast MR data (N = 516) were synthetically undersampled using a variable‐density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U‐net. A subset of the data (N = 96) was segmented and used to train the segmentation U‐net. Real‐time spiral phase‐contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed‐sensing CO.ResultsThe FReSCO framework was demonstrated prospectively at the scanner. Beat‐to‐beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = −0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458).ConclusionsThe FReSCO framework was successfully demonstrated for real‐time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.