Close Fit Research Articles

TNF-α and its soluble receptors mediate the relationship between prior severe mood episodes and cognitive dysfunction in euthymic bipolar disorder

BackgroundBipolar disorder (BD) is one of the most disabling mental health conditions in the world. Symptoms of cognitive impairment in BD contribute directly to occupational and social deficiencies and are very difficult to treat. Converging evidence suggests that BD patients have increased peripheral markers of inflammation. The hypothesis of neuroprogression in BD postulates that cognitive deficits develop over the course of the illness and are influenced by prior severe mood episodes, leading to wear-and-tear on the brain– however, there exists a paucity of data statistically testing a mediating role of immune molecules in cognitive dysfunction in BD. MethodsThis is a cross-sectional study. We measured serum levels of tumor necrosis factor alpha (TNF-α), and soluble (s) TNF receptors one and two (sTNF-R1 and sTNF-R2) in 219 euthymic BD patients and 52 Healthy Controls (HCs). Structural equation modeling (SEM) was used for the primary purpose of assessing whether TNF markers (measured by the multiple indicators TNF-α, sTNF-R1 and sTNF-R2) mediate the effect or number of prior severe mood episodes (number of prior psychiatric hospitalizations) on cognitive performance. ResultsBD and HC groups did not differ on circulating levels of TNF molecules in the present study. However, we found higher sTNF-R1 concentration in ‘late-stage’ BD illness (>1 prior psychiatric hospitalization) compared to those in early stage illness. In the subsequent SEM, we found that the model fits the data acceptably (Chi-square = 49.2, p = 0.3), and had a ‘close fit’ (RMSEA = 0.02, PCLOSE = 0.9). Holding covariates constant (age, sex, premorbid IQ, education, and race), we found that the standardized indirect effect was significant, p = 0.015, 90%CI [-0.07, −0.01], indicating that the estimated model was consistent with peripheral TNF markers partially mediating a causal effect of severe mood episodes on executive function. ConclusionsOur results indicate that circulating levels of TNF molecules partially mediate the relationship between prior severe mood episodes and executive function in BD. These results may implicate TNF variables in the neuroprogressive course of BD and could point to novel interventions for cognition.

Assessing functioning in adolescents with chronic fatigue syndrome: psychometric properties and factor structure of the School and Social Adjustment Scale and the Physical Functioning Subscale of the SF36.

Chronic fatigue syndrome (CFS) has a major impact on functioning. However, no validated measures of functioning for this population exist. We aimed to establish the psychometric properties of the 5-item School and Social Adjustment Scale (SSAS) and the 10-item Physical Functioning Subscale of the SF-36 in adolescents with CFS. Measures were completed by adolescents with CFS (n = 121). For the Physical Functioning Subscale, a 2-factor solution provided a close fit to the data. Internal consistency was satisfactory. For the SSAS, a 1-factor solution provided an adequate fit to the data. The internal consistency was satisfactory. Inter-item and item-total correlations did not indicate any problematic items and functioning scores were moderately correlated with other measures of disability, providing evidence of construct validity. Both measures were found to be reliable and valid and provide brief measures for assessing these important outcomes. The Physical Functioning Subscale can be used as two subscales in adolescents with CFS.

Cyclic hardening and softening behavior of the low yield point steel: Implementation and validation

The low-yield-point (LYP) steel exhibits complicated cyclic hardening and softening response under reversed loading condition when applied in the seismic control and isolation system, thereby the evaluation of its hysteretic behavior demanding a sophisticated constitutive model with robust multi-dimensional implementation for reasonable finite element analysis. In this paper, the implementation and validation of an advanced constitutive model for LYP steel developed in a previous study is illustrated, which formulates the cyclic hardening and softening features by decomposed A-F hardening laws with strain memory effect and a novel concept of “transformation zone” with multi-stage evolution rule, respectively. To convert the theoretical model into numerical utilization, a modified radial return algorithm is proposed for the fully-implicit stress integration, and the corresponding consistent tangent moduli are derived. Moreover, the constitutive model is incorporated into a general finite-element software package based on the ABAQUS UMAT platform to facilitate its universal application. A number of experimental results, including the material coupons, the shear panels/links, and a frame-shear wall system made of LYP steel, are collected to validate the model ranging from material level to structural level. The close fit between simulations and experimental results indicates that the developed model is capable of accurately describing the nonlinear cyclic hardening and softening properties with memory effect for LYP steel, which proves the model suitable for the structural seismic simulation and analysis.

Ballistic gelatin calibration standardisation

IntroductionA review of ballistic gelatin calibration standards has highlighted that data used from studies with different calibrations methods may not be able to be compared. Calibration of ballistic gelatin did...

The Usefulness of Spectroscopic Simulations.

This article presents a method for extracting the optical constants of homogeneous isotropic materials using the infrared spectra of that material. The method is based on using the harmonic oscillator model of molecular polarizability to obtain optical constants, then calculating the spectrum, comparing the calculated spectrum to an experimental spectrum of the material, and adjusting the model parameters until a close fit between the spectra is obtained. Corrections that need to be made to the experimental spectra in order to remove instrumental distortions are also briefly described. The remainder of the article centers on describing how the optical constants can be used to simulate spectra of that material in different experimental arrangements and the benefits that spectral simulations afford to experimentalists.

A precise analytical approximation for the deprojection of the Sérsic profile

The Sérsic model shows a close fit to the surface brightness (or surface density) profiles of elliptical galaxies and galaxy bulges, and possibly also those of dwarf spheroidal galaxies and globular clusters. The deprojected density and mass profiles are important for many astrophysical applications, in particular for mass-orbit modeling of these systems. However, the exact deprojection formula for the Sérsic model employs special functions that are not available in most computer languages. We show that all previous analytical approximations to the 3D density profile are imprecise at low Sérsic index (n ≲ 1.5). We derived a more precise analytical approximation to the deprojected Sérsic density and mass profiles by fitting two-dimensional tenth-order polynomials to the residuals of the analytical approximations by Lima Neto et al. (1999, MNRAS, 309, 481; LGM) for these profiles, relative to the numerical estimates. Our LGM-based polynomial fits have typical relative precision better than 0.2% for both density and mass profiles, for Sérsic indices 0.5 ≤ n ≤ 10 and radii 0.001 < r/Re < 1000. Our approximation is much more precise than previously published approximations (except, in some models, for a few discrete values of the index). An appendix compares the deprojected Sérsic profiles with those of other popular simple models.

Micro-texturing on free-form surfaces using flexible-electrode through-mask electrochemical micromachining

Abstract Surface micro-texturing deals with the issues pertaining to various fields of engineering for enhancing essential functions such as tribological, wetting, biocompatibility, sustainability, cleanliness, etc. Among all the aforementioned sectors, micro-texturing of curved/free-form large areas is getting huge attention, e.g. micro-textures on artificial biomedical implants enhance sustainability and life cycle by better implant-tissue interface, cell-adhesion, and cell proliferation. Due to growing awareness towards the significances of micro-texturing, presently a lot of thrusts are being given on innovations employing surface textures on free-form/curved surfaces. The paper proposes a porous and flexible electrode based through-mask electrochemical micromachining (TMECMM) in which surface of any curvature can be textured by adapting the shape of the surface. A 3-D numerical simulation of the TMECMM texturing executed in COMSOL to analyze the effect of current density and topology of micro-textures. The minimum pitch of the micro-features in mask is determined through analysis of overlap of current density with the help of developed model. High resolution-reusable masks with micro-features arrays are fabricated using a two-step laser beam machining process. These masks are employed in-between the flexible electrode and target metal surfaces for localized anodic dissolution for texturing. The comparison of response surface methodology (RSM) based experimental and simulated results shows a close fit (and same trends) with respect to diameter and depth of micro-dimples array. The capabilities of the developed device are showcased by machining various types of texture arrays of micro-dimples, straight micro-channels, and turbulated micro-channels on flat, cylindrical, spherical, and free-form surfaces of SS304.

Quantifying Model Error in P-technique Factor Analysis

P-technique factor analysis is an exploratory factor model for multivariate time series data. Assessing model fit of P-technique factor models is non-trivial because time series data are correlated at nearby time points. We present a test statistic that is appropriate for P-technique factor analysis. In addition, the test statistic allows researchers to quantify the amount of model error. We explore the statistical properties of the test statistic with simulated data and we illustrate its use with an empirical study of personality states. Results of the simulation study include (1) the empirical distributions of the test statistic approximately followed their respective theoretical chi-square distributions, (2) the empirical Type I error rates of the test of perfect fit are close to the nominal level and the empirical Type I error rates of the test of close fit are slightly lower than the nominal level, and (3) the empirical power rates of the test of perfect fit are satisfactory but the empirical power rates of the test of close fit are only satisfactory for small models.

Kinetic Study in the Extraction of Essential Oil from Clove (Syzgium aromaticum) Stem using Microwave Hydrodistillation

On understanding the potential significance of the microwave-assisted extraction, studying the kinetics mechanisms and modeling of the extraction method is necessary. The aim of this study was to evaluate the suitable kinetic models for microwave hydrodistillation (MHD) of the essential oil from clove ( Syzgium aromaticum ) stem. MHD was performed of 100 g dried clove stem in 200 mL aquadest, heated at various power settings of 300, 450, 600 and 800 W. The extraction conducted for a total of 120 min with 10 min intervals. The experimental data (extract yields versus time) were fitted to four-two parameter empirical kinetic models. The total extract yields from clove stem oil exhibited a close fit to the hyperbolic model and Weibull’s exponential. The result also suggested that the increase in microwave irradiation power significantly affected some of the parameter of MHD kinetic models.

Polygenic Risk, Fitness, and Obesity in the Coronary Artery Risk Development in Young Adults (CARDIA) Study

Obesity is a major determinant of disease burden worldwide. Polygenic risk scores (PRSs) have been posited as key predictors of obesity. How a PRS can be translated to the clinical encounter (especially in the context of fitness, activity, and parental history of overweight) remains unclear. To quantify the relative importance of a PRS, fitness, activity, parental history of overweight, and body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) in young adulthood on BMI trends over 25 years. This population-based prospective cohort study at 4 US centers included white individuals and black individuals with assessments of polygenic risk of obesity, fitness, activity, and BMI in young adulthood (in their 20s) and up to 25 years of follow-up. Data collected between March 1985 and August 2011 were analyzed from April 25, 2019, to September 29, 2019. Body mass index at the initial visit and 25 years later. This study evaluated an obesity PRS from a recently reported study of 1608 white individuals (848 women [52.7%]) and 909 black individuals (548 women [60.3%]) across the United States. At baseline (year 0), mean (SD) overall BMI was 24.2 (4.5), which increased to 29.6 (6.9) at year 25. Among white individuals, the PRS (combined with age, sex, self-reported parental history of overweight, and principal components of ancestry) explained 11.9% (at year 0) and 13.6% (at year 25) of variation in BMI. Although the addition of fitness increased the explanatory capability of the model (24.0% variance at baseline and up to 18.1% variance in BMI at year 25), baseline BMI in young adulthood was the strongest factor, explaining 52.3% of BMI in midlife in combination with age, sex, and self-reported parental history of overweight. Accordingly, models that included baseline BMI (especially BMI surveillance over time) were better in predicting BMI at year 25 compared with the PRS. In fully adjusted models, the effect sizes for fitness and the PRS on BMI were comparable in opposing directions. The added explanatory capacity of the PRS among black individuals was lower than among white individuals. Among white individuals, addition of baseline BMI and surveillance of BMI over time was associated with improved precision of predicted BMI at year 25 (mean error in predicted BMI 0 kg/m2 [95% CI, -11.4 to 11.4] to 0 kg/m2 [95% CI, -8.5 to 8.5] for baseline BMI and mean error 0 kg/m2 [95% CI, -5.3 to 5.3] for BMI surveillance). Cardiorespiratory fitness in young adulthood and a PRS are modestly associated with midlife BMI, although future BMI is associated with BMI in young adulthood. Fitness has a comparable association with future BMI as does the PRS. Caution should be exercised in the widespread use of polygenic risk for obesity prevention in adults, and close clinical surveillance and fitness may have prime roles in limiting the adverse consequences of elevated BMI on health.

Metaheuristics optimized machine learning modelling for estimation of exergetic emissions of a propulsion system

You This study offers a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a business jet aircraft’s exergo-emission parameters, such us exergy destruction ratio (rex,dest) and waste exergy ratio (rwex), at different flight stages. In consideration of this, the development of hybrid genetic algorithm (GA)-ANN models has been achieved by considering real databases of rex, dest and rwex at various power levels. Implementing a metaheuristics-based optimization on the generated multilayer perceptron (MLP) ANN models has produced the most favorable initial network weights, step-size, biases as well as training algorithm’s back-propagation (BP) momentum rate in addition to optimal quantity of neurons in the hidden layer(s) with regard to the topology design. In accordance with an error assessment approach, there exists a close fit linking the reference real data and rwex (linear correlation ratio, R, value of 0.999851) as well as rex,dest (R value of 0.999985) predicted values.

Biogenic volatile organic compound ambient mixing ratios and emission rates in the Alaskan Arctic tundra.

Rapid Arctic warming, a lengthening growing season, and the increasing abundance of biogenic volatile-organic-compound-emitting shrubs are all anticipated to increase atmospheric biogenic volatile organic compounds (BVOCs) in the Arctic atmosphere, with implications for atmospheric oxidation processes and climate feedbacks. Quantifying these changes requires an accurate understanding of the underlying processes driving BVOC emissions in the Arctic. While boreal ecosystems have been widely studied, little attention has been paid to Arctic tundra environments. Here, we report terpenoid (isoprene, monoterpenes, and sesquiterpenes) ambient mixing ratios and emission rates from key dominant vegetation species at Toolik Field Station (TFS; 68°38′ N, 149°36′ W) in northern Alaska during two back-to-back field campaigns (summers of 2018 and 2019) covering the entire growing season. Isoprene ambient mixing ratios observed at TFS fell within the range of values reported in the Eurasian taiga (0–500 parts per trillion by volume – pptv), while monoterpene and sesquiterpene ambient mixing ratios were respectively close to and below the instrumental quantification limit (~ 2 pptv). Isoprene surface emission rates ranged from 0.2 to 2250 μgC m−2 h−1 (mean of 85 μgC m−2 h−1) and monoterpene emission rates remained, on average, below 1 μgC m−2 h−1 over the course of the study. We further quantified the temperature dependence of isoprene emissions from local vegetation, including Salix spp. (a known isoprene emitter), and compared the results to predictions from the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1). Our observations suggest a 180 %–215 % emission increase in response to a 3–4°C warming, and the MEGAN2.1 temperature algorithm exhibits a close fit with observations for enclosure temperatures in the 0–30°C range. The data presented here provide a baseline for investigating future changes in the BVOC emission potential of the under-studied Arctic tundra environment.

<i>Passage</i> of the Federal Reserve’s Third Mandate

The American Federal Reserve’s Board of Governors has been negotiating with the Congress in order to add a third mandate to their longstanding missions of reducing unemployment and inflation. Chairman Powell described this mandate as the mitigation of shrinking GDP. We model this mandate by two axioms, which reveal American GDP as the sole driver of global GDP. The close fit of each model demonstrates that axiomatic venture can discover and corroborate truth via distinct pathways. In view of trade-war and pandemic shocks to GDP worldwide, this third mandate is now compelling. Congressional inaction will reduce global livelihood in a post-pandemic era (https://www.federalreserve.gov/newsevents/testimony/powell20190710a.htm).

Amphoteric Soy Protein-Rich Fibers for Rapid and Selective Adsorption and Desorption of Ionic Dyes.

Uniquely amphoteric soy protein (SP)-rich ultra-fine fibers (231 nm average diameter) have been facilely electrospun from aq. colloids and rendered water-insoluble by heating (150 °C, 12 h) to be highly stable over 14 d (pH 7) as well as under extremely acidic to basic (pH 0–10, 2 d) or at boil (2 h) conditions. The SP-rich fibrous membranes are easily tuned to be charged either negatively by deprotonation above or positively by protonation below the 4.5 PI of SPs. This pH-responsive amphoterism has been demonstrated for rapid adsorption of either cationic or anionic dyes, selective adsorption of either dye from their mixtures, and repetitive adsorption/desorption to recover and reuse both dyes and membranes. Chemisorption and heterogeneous adsorption of ionic dyes was confirmed by close fitting to the pseudo-second-order kinetic model (R2 = 0.9977–0.9999) and Freundlich adsorption isotherm (R2 = 0.9879). This is the first report of water-resilient and pH-robust ultrafine fibrous membranes fabricated from aqueous colloids of neat globular SPs, the major byproducts of under-utilized edible oil and biodiesel. The natural polyampholyte origin, amphoterism, and green processing make these fibrous materials unique and versatile for many potential applications involving both anionic and cationic species.

Analysis of the Voltage-Dependent Plasticity in Organic Neuromorphic Devices

The bias-dependent signal transmission of flexible synaptic transistors is investigated. The novel neuromorphic devices are fabricated on a thin and transparent plastic sheet, incorporating a high-performance organic semiconductor, dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene, into the active channel. Upon spike emulation at different synaptic voltages, the short-term plasticity feature of the devices is substantially modulated. By adopting an iterative model for the synaptic output currents, key physical parameters associated with the charge carrier dynamics are estimated. The correlative extraction approach is found to yield the close fits to the experimental results, and the systematic evolution of the timing constants is rationalized.

Evaluation of Well-Being in Adolescence: Proposal of an Integrative Model with Hedonic and Eudemonic Aspects

The evaluation of well-being can be conducted according to distinct traditions, among which the hedonic and eudemonic traditions stand out. The hedonic focuses on happiness, emphasizing the presence of positive affects and the absence of negative affects. The eudemonic emphasizes living well and fully, and the realization of human potential. This study aims to test a hypothetical model of well-being in adolescents, seeking to assess the dimensional structure of well-being, including measures of hedonic (subjective) and eudemonic (psychological) well-being. The sample consisted of 1248 Brazilian adolescents between 12 and 18 years of age (M = 15.09, SD = 1.77). The instruments employed were the Psychological Well-Being Scale, the Brief Multidimensional Students’ Life Satisfaction Scale and the Core Affects Scale. Three hypothetical models of relationships between the two well-being perspectives were tested using structural equation modeling. The results show that the model with subjective well-being and psychological well-being as two uncorrelated factors presented the worst values for model fit. The Oblique Model, with subjective well-being and psychological well-being as two correlated factors (r = .90) and the Single Factor Model, with well-being measures as observed variables of a single well-being latent factor, exhibited close fits. We conclude that a second-order factor encompasses the complexity of the well-being construct across different dimensions, while offering an integrative perspective of those dimensions.

Enhanced defect luminescence due to fast Auger process in cuprous iodide structures produced by solvent/anti-solvent crystallization

Photoluminescence spectra of self-assembled cuprous iodide structures produced by solvent-antisolvent crystallization were investigated over eight orders of magnitude of excitation power density. Through an analysis of the effects of different annealing conditions on the emission spectra two defects-related emissions, associated to copper and iodine vacancies, and a free exciton recombination peak were identified. At higher power densities, an enhanced increase in the broad defects-related emission band accompanied by a decrease in other contributions was observed. A fast, non-radiative Auger process is proposed as the main mechanism allowing high energy electrons to be trapped at deep level defects associated to the broad defects-related emission band while simultaneously reducing the amount of free charge carriers, which results in the observed behavior. With a rate-equation model developed for the proposed mechanism a close fit between simulated and experimental data was achieved.

Fractional factorial design modelling on degradation of Direct Red 81 dye by advanced oxidation process - ozonation: reaction kinetics.

In the present work, the degradation of Direct Red 81 by ozonation was studied. The interactive effects of the influencing factors (dye concentration = 500-2,000 mg/L; time = 10-30 min; pH = 7.0-11.0) on degradation efficiency was critically examined through experimental design optimization by central composite design under the response surface methodology. The high correlation coefficients (R2 = 0.976 & adjusted R2 = 0.958) obtained by analysis of variance (ANOVA) demonstrated close fit between the experimental and the predicted values. Optimized conditions under specified cost-driven restraints were obtained for the highest desirability (i.e. degradation of 1,210.59 mg/L dye) at pH = 11.0, initial dye concentration = 2,000 mg/L and ozone exposure time of 27.16 min. The degradation of Direct Red 81 was confirmed through Fourier transform infrared spectroscopy (FTIR) analysis and UV-Vis spectrometry.

Metaheuristics Optimized Machine Learning Modelling of Environmental Exergo-Emissions for an Aero-Engine

Abstract The purpose of this study is to develop a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a cargo aircraft’s exergo-emissions (exergy destruction ratio, rex,dest , and waste exergy ratio, rwex ) at different flight stages. Hybrid genetic algorithm (GA)-ANN models have been accomplished utilizing real databases of rex,dest and rwex at various powers. Implementing a metaheuristics-based optimization on multilayer perceptron (MLP)-ANNs has produced the most favourable initial weights, step-size, biases, and training algorithm’s back-propagation (BP) momentum rate in addition to optimum number of neurons in the hidden layer(s). In accordance with an error assessment, a close fit linking real data and rwex (linear correlation ratio, R, value of 0.999851) as well as rex,dest (R value of 0.999985) predicted values is found. In the rex, dest estimation model, the accuracy among single-hidden-layer networks has been confirmed to be higher; whereas, highly accurate testing outcomes have been obtained in two-hidden-layer networks as far as modeling of rwex is concerned. ANN models’ optimization by GAs has increased the accuracy of the resulting models (R value of 0.999987 and 0.999869 for rex,dest and rwex , in that order ascertaining a drop-off in the testing stage errors).

Baker's Yeast Catalyzed Multicomponent Reactions: A New Hope?

AbstractBaker's yeast was primarily used as a leavening agent in the food processing industry and later as biocatalyst in the brewing industry. Ready availability and low cost has made baker's yeast a primary research object among the scientific community. It is well known to reduce carbonyl groups. Several other simple single step transformations have also been carried out using baker's yeast. Multicomponent reactions are a close fit to the concept of “ideal” synthesis and, in recent years, they have become a rapid platform for the drug discovery process. Due to the promiscuous nature of enzymes, they were not explored as potential biocatalyst for multicomponent reactions. In this review, the application of baker's yeast in multicomponent reactions has been discussed.