Nonlinear Regression Models Research Articles

Metropolitan Construction Carbon Emission Efficiency: An Integrative Static-Dynamic Analytical Framework

Abstract Taking urgent action to combat climate change and promote sustainable cities are part of the 17 Sustainable Development Goals, which demand immediate action from all countries. Enhancing the carbon emission efficiency of the construction industry (CICEE) from the "static-dynamic" perspective is crucial in addressing climate change. This study utilized the super slack-based measure model to analyze static efficiency. The Malmquist-Luenberger index model, multiple non-linear regression model, and principal component analysis were used to research the driving factors dynamically. Factors included technical change (TC), pure technical efficiency change (PEC), and scale efficiency change (SEC), which were subdivided into 21 variables. The panel data was from 11 cities in Zhejiang from 2007 to 2019. The results indicated that the static efficiency values were generally effective, but in 2019, more than half of the cities experienced an average decrease of 52%. Efficiency levels were higher in northern Zhejiang and lower in the southeastern region. CICEE values over one were primarily attributed to high TC levels and stable PEC and SEC conditions. Additionally, a generalized framework for improving CICEE was constructed, assisting policymakers in identifying reasons for low CICEE and providing targeted strategies to reduce emissions and increase efficiency.

Prediction of manhole-cover skid-resistance performance using decision trees and regression analysis

This study conducts skid-resistance coefficient tests on 3,600 manhole covers and collects data on various influencing factors, such as surface conditions, pattern types, starting zones, turning zones, usage duration, wheel track locations, and surrounding traffic volume. A preliminary classification analysis of the data is performed using decision trees to identify key factors affecting manhole-cover skid resistance. Subsequently, a multivariate nonlinear regression model is applied to examine the impact of these factors on the target variables. By integrating decision trees and nonlinear regression in a hybrid modeling approach, the study aims to reveal complex patterns in the data and generate reliable predictive results. The decision-tree analysis using non-continuous variables reveals that manhole covers located at turns and starting zones, and those with worn surfaces are more likely to fail to meet safety standards for skid resistance. The multivariate nonlinear regression model, which compares the predicted values against the actual British Pendulum Number (BPN) test results, achieves an accuracy of 82.9%.

Threshold Effects of Economic Policy Uncertainty on Corporate Investment

Existing literature vigorously underlines the negative impact of economic policy uncertainty (EPU) on corporate investment. However, earlier empirical studies mostly examined this relationship using linear econometric models. Current study aims to revisit this relationship employing a non-linear Threshold Auto Regressive (TAR) model of Hansen (1999). The study incorporates data from 25 countries ranging 2008 to 2021 comprising 10020 firm-level observations. The study's findings emphasize the nonlinear relationship between the EPU and corporate investment. The results also find out two EPU thresholds in the regression relationship. Beyond the thresholds firms’ investments behaviors significantly change. The robustness checks also confirm that our results remain consistent even upon utilizing two step system GMM model. The findings hold important implications for policymakers and businesses, emphasizing the need of robust economic policies for corporate investment.

Phytotoxicity risk assessment of diuron residues in sands on wheat, chickpea, and canola.

While diuron residues are being detected more frequently in agricultural soils, there is limited information available regarding their potential phytotoxicity to non-target grain crops. This study aims to determine robust phytotoxicity thresholds for three common, but contrasting, crop species (canola, chickpea, and wheat) exposed to a range of diuron concentrations and to determine how loamy sand soil can change the toxicity thresholds relative to an inert sand. The log-logistic non-linear regression model proved most effective in determining toxicity thresholds by analysing crop responses to diuron. Canola was the most sensitive to diuron in sand followed by wheat and chickpea. Diuron exhibits higher phytotoxicity in sand compared to loamy sand, with ED50 values (which is the dose at which diuron causes a 50% decrease in plant growth) of 0.03 mg kg-1 and 0.07 mg kg-1 for canola shoot biomass inhibition and 0.01 mg kg-1 and 0.06 mg kg-1 for root dry weight reduction, respectively. The ED50 values for wheat shoot biomass (0.11 and 0.24 mg kg-1) in sand and loamy sand, respectively, and the ED50 values for root growth inhibition are 0.14 mg kg-1 in sand and 0.19 mg kg-1 in loamy sand. These values were lower than label concentrations and previously estimated average and maximum diuron residue loads (0.17 and 0.29 mg kg-1) in Western Australia paddocks. The larger ED50 values of diuron in the loamy sand can be attributed to higher soil organic matter and cation exchange capacity that decreased bio-available diuron levels. Average diuron residue loads in Western Australia crop fields exceed the ED50 value emphasizes the need for careful planning of crop rotations to avoid crop phytotoxicity from soil-borne diuron residues. Further study is needed to determine the effect of a wider range of soil properties such as pH, clay content, and soil organic matter on the phytotoxicity risk of diuron to rotational crops.

Sustainable Economic Growth And Industry Insights: Nonlinear Interaction Analysis of GDP Vs FDI in Malaysia Using SCILAB Programming

Objective: To investigate the nonlinear relationship between Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) in Malaysia, with the aim of providing insights into their bidirectional interactions. Theoretical Framework: The main concepts and theories that underpin the research are nonlinear regression techniques and economic growth models. These frameworks provide a solid basis on understanding the dynamic interaction between GDP and FDI in the context of Malaysia’s economic. Method: The methodology comprises writing Scilab coding to analyze nonlinear regression models. Malaysia's economic data on GDP and FDI were utilized as inputs for the analysis. The study involves modeling the dynamics between these indicators and evaluating their relationship over time. Results and Discussion: The results obtained revealed a significant nonlinear relationship between GDP and FDI in Malaysia. These results are contextualized within the theoretical framework, highlighting the bidirectional nature of the relationship. Possible discrepancies and limitations, including data constraints and assumptions of the nonlinear models, are also considered. Research Implications: This research offers valuable insights for policymakers, helping shape economic planning,and investment strategies. It highlights the importance of FDI in promoting sustainable economic growth and industrial development towards United Nation SDG 8 and SDG 9. Originality/Value: This study contributes by using nonlinear regression techniques and Scilab programming to analyze the complex relationship between GDP and FDI, an approach not widely applied to Malaysia's context. The relevance and value of this research are evidenced by its potential impact on economic policy and its contributions to a deeper understanding of Malaysia's economic development.

English

Pedotransfer function (PTFs) is widely developed using machine learning algorithms (MLA) and non-linear regression (NLR). Saturated hydraulic conductivity (Ks) is obviously used to regulate water movement in the soil. The objective of the work is to evaluate the accuracy of MLA to predict Ks. Moreover, is to determine the best class of predictors that can be used to predict Ks with little estimation error. Saturated hydraulic conductivity was measured by direct method using the constant head method. In addition, Ks was predicted using PTFs developed by machine learning algorithms including Random Forest Algorithm (RFA), and Gradient Boosting Algorithm (GBA) models with three categories of soil physical predictors. Furthermore, PTFs developed by RFA, and GBA were compared with PTFs developed by NLR models using the same three categories of soil physical predictors. The three categories of soil physical predictors were the following: category-1 refers to sand, silt, and clay “SSC”; category-2 refers to category-1, in addition to bulk density “SSC+BD”; and category-3 refers to category-2, in addition to organic matter “SSC+BD+OM”. The results of error analysis observed that PTFs developed by RFA were more accurate than GBA and NLR models, respectively, for Ks prediction with the three categories of soil physical predictors. Moreover, the category-3 which takes into account sand, silt, clay, bulk density, and organic matter was the best category used to predict Ks using RFA, and NLR models. PTFs developed by machine learning algorithms including RFA, and GBA models can be utilized to predict Ks with little calculation error. However, RFA was greater than GBA for calculating Ks. The efficiency of RFA was associated with the number of soil variables predictors and building classification and regression trees which is more robust than decision trees such as GBA to optimize Ks prediction.

Experimental study on solid–liquid equilibrium behavior of methimazole (Form Ⅰ) in twelve mono-solvents: Measurement, modeling, Hansen solubility parameters and thermodynamic analysis

Methimazole is an active pharmaceutical ingredient, which is commonly used as an imidazole antithyroid drug. Solubility research is one of the key steps affecting the crystal quality of methimazole, which can give essential fundamental data and theoretical guidance for its downstream industries, such as preparation form, storage, transportation and clinical application. Researchers have found that there are two crystal forms of methimazole, different crystal forms may result in differences in the efficacy, pharmacology, physicochemical properties, etc. Further supplementation and improvement are urgently needed regarding the solid–liquid equilibrium data, dissolution thermodynamic properties and dissolution mechanism of methimazole. In the present work, by using a gravimetric method, the solubility of methimazole (Form Ⅰ) in twelve selected mono-solvents including 2-propoxyethanol, isopropyl acetate, anisole, 2-ethoxyethanol, 1-hexanol, 1-pentanol, ethylene glycol, butyl acetate, 2-butoxyethanol, propyl acetate, 2-methoxyethanol and ethyl butyrate was investigated from 283.15 K to 323.15 K under pressure of 0.1 MPa. The regression result between solubility and temperature showed that they were positively correlated. At 298.15 K, the obtained mole fraction solubility of methimazole (Form Ⅰ) decreased as: 2-methoxyethanol (0.2203) > 2-ethoxyethanol (0.1766) > ethylene glycol (0.1432) > 2-propoxyethanol (0.1425) > 2-butoxyethanol (0.1268) > 1-pentanol (0.0394) > 1-hexanol (0.0349) > propyl acetate (0.0223) > butyl acetate (0.0195) > anisole (0.0176) > isopropyl acetate (0.0135) > ethyl butyrate (0.0120). The maximum solubility was found in 2-methoxyethanol at 323.15 K with the mole fraction solubility value of 0.2984, while the minimum obtained value was occurred in ethyl butyrate at 283.15 K with the mole fraction solubility of 0.00786. Besides, four thermodynamic nonlinear regression models including Yaws model, UNIQUAC model, Two-Suffix Margules model and NRTL-SAC model were used to correlate the experimental solubility of methimazole (Form Ⅰ). Furthermore, to further explore the dissolution mechanism of methimazole (Form Ⅰ) in the selected twelve target solvents, the HSPs, Hirshfeld surface analysis and dissolution thermodynamic properties were calculated and analyzed.The related research in this study is of great significance for understanding the solubility behavior and dissolution mechanism of methimazole Form I in the selective solvent system, which can provide foundation data for the optimization of its crystallization conditions. At the same time, this study is helpful to provide theoretical guidance for the research of other similar drugs.

Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data

Objective.Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control.Approach.Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings.Main results.Dataset aggregation reduced the root-mean-squared error (RMSE) of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets.Significance.Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.

Buckling behavior of orthotropic thin plates using analytical and machine learning methods

Designing plate structures poses challenges due to potential instabilities and the complexities involved in obtaining accurate analytical solutions, especially for non-Lévy-type boundary conditions. This study employs the finite integral transform method to analyze the buckling behavior of orthotropic thin plates with complex boundary conditions. In solution procedure, the governing high-order partial differential equations are transformed into a system of linear algebraic equations, yielding exact and rapidly converging analytical solution. The method is simple and general and does not need to predetermine the deflection function. The correctness of the method is validated by comparison with numerical simulations performed using the ABAQUS software. Furthermore, Gene Expression Programming (GEP) is utilized to develop empirical models that predict buckling coefficients of isotropic and orthotropic plates under classical and non-classical boundary conditions. Material properties, aspect ratio, rotating fixed coefficient, and boundary conditions are used as input parameters, with simplified mathematical formulations to predict the buckling coefficient. Model performance is assessed using parametric analysis and statistical tests to ensure accuracy and generalization. Further investigation shows aspect ratio and rotating fixed coefficient are significant variables influencing buckling coefficients under classical and non-classical boundary conditions, respectively, followed by boundary conditions and material property. The performance of the GEP model is further assessed by comparing with linear and non-linear regression models. The results show that GEP outperforms the regression models, showing its higher prediction accuracy. This study not only addresses the challenges of designing thin plates with complex boundary conditions, but it also presents an effective machine-learning method for predicting buckling behavior. The analytical solution can be used as a benchmark for validating new analytical and numerical methods. The equations developed using GEP to predict the buckling coefficient of plates provide a useful tool for extrapolating results beyond the scope of this study.

Associations between body mass index and physical fitness indicators among Chinese university students: a multicenter cross-sectional study

BackgroundIncreasing body mass index (BMI) has many negative effects on the physical and mental health of university students and has become a common public health problem in all countries. However, there are few studies on the association between BMI and physical fitness indicators among Chinese university students in developing countries, and the findings are inconsistent. The present study aims to analyze the association between BMI and physical fitness indicators among Chinese university students, to assist in university students’ health development and intervention.MethodsIn this study, we conducted a cross-sectional assessment of BMI of 29,371 university students and a cross-sectional assessment of physical fitness indicators of vital capacity, sit and reach, pull-up(boys), sit-up(girls), standing long jump, 50 m dash, 1000 m run(boys ), 800 m run(girls). One-way analysis of variance (ANOVA), Pearson’s correlation analysis, and nonlinear regression modeling were used to analyze the associations that existed between BMI and various physical fitness indicators.ResultsThe percentages of Chinese university students in emaciated, normal, overweight, and obese were 17.8%, 65.3%, 14.9%, and 2.0%, respectively. Comparing between different BMI groups (emaciated, normal, overweight, and obesity), for boys, the effect values ranged from 1.0 to 2.0% for the items of vital capacity, sit and reach, pull-up/situp, standing long jump, 50 m dash, and 1000/800 m-run. For run events, the range of effect sizes ranged from 0.2 to 0.9; the range of effect sizes for girls also ranged from 0.2 to 0.9.ConclusionsThe physical fitness level of emaciated and obese Chinese university students is generally lower than that of normal-weight students, and the relationship between BMI and physical fitness indicators shows a “U-shape” or “inverted U-shape” curve. The effect of BMI on physical fitness is more significant in boys than in university girls. In the future, targeted physical exercise and health education should be provided to emaciated and obese university students to maintain a reasonable body weight and promote the healthy development of physical fitness.

Non-Linear Models for Tree Aboveground Biomass and Volume Estimation in Agoi-Ibami Forest Reserve, Cross River State, Nigeria

Tree biomass and volume estimation based on allometric equations is a widely used non-destructive technique for estimating biomass, sequestered carbon, and volume worldwide. Non-linear models for the biomass and volume of individual trees in Nigeria's Cross River State's Agoi-Ibami Forest Reserve were fitted and validated in this study. In this study, two parallel lines transect of 1500 meters in length, separated by 500 meters, were established using the systematic line transects sampling method. Along each transect, ten sample plots, each measuring 50 m by 50 m, were placed alternately at 100 m intervals. Twenty sample plots in all were thus marked for the study. The estimation of biomass using a non-destructive method was used. To calculate the aboveground green biomass for each, the diameter at breast height and total height were employed. Agoi-Ibami Forest Reserve had a total value of 391N ha-1 for number of stem per hectare, 14 tree families, mean dbh of 26.04cm, height of 15.9m, and basal area of 50.21m2ha-1. Conversion factors were used to estimate stand biomass, carbon sink, and sequestered carbon dioxide (CO2). Non-linear models were fitted for volume and aboveground biomass estimation in the study area. All of the models were evaluated and validated using some assessment statistical criteria and residual graphs, and models with good fit were suggested for use. Curve Expert Software was used for the development of the non-linear regression models. According to the assessment criteria, the forest reserve's best non-linear volume and aboveground biomass models were the Ratkowsky, Weibull, and Logistic models. Fitted models should therefore be employed for the forest reserve's efficient and successful management.

Effect of temperature on transport index of fish oil-based drilling mud in high pressure high temperature well: a hybrid model approach

Diesel Oil-based drilling mud used in the oil and gas industry causes severe environmental and public health issues due to its high toxicity, especially in high-pressure, high-temperature (HPHT) wells. Therefore, easily biodegradable synthetic oil-based mud with high drilling performance is essential. Fish oil is a bio-oil known for its non-toxicity, high lubricity, thermal stability, and biodegradability. Effective hole cleaning is crucial for drilling performance and is ensured by the rheological properties of drilling mud. While rheological parameter prediction can be performed using an artificial neural network (ANN) based model, such models often fail to provide accurate predictions for unseen data. In contrast, empirical models are more robust and facilitate generalization. This study aims at assessment of the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) in HPHT wells through evaluation of hole cleaning performance (HCP) using a hybrid approach that combines an ANN with nonlinear regression (NLR) for temperatures ranging from 40 °C to 80 °C.Firstly, an ANN model was developed considering mud weight, temperature, and shear rate. Secondly, the NLR model was used in the prediction of rheological parameters, namely, Yield point (YP) and Plastic viscosity (PV), based on the predicted shear stress from the ANN model. Finally, the performance metrics of the ANN-NLR rheological model were evaluated using the root mean square error (RMSE) and correlation coefficient (R2) and comparison made with the Single ANN rheological model. The results showed the outperformance of ANN-NLR model over the ANN model for rheological parameter prediction, with R2 values of (0.99) for YP and (1) for PV. The predicted PV and YP values were then used for evaluation of transport index (TI) for HCP analysis. The effect of temperature on TI analysis showed this with increase in temperature, TI also increased, indicating the proposed mud providing good HCP under HPHT conditions.

The impact of park environmental characteristics and visitor perceptions on visitor emotions from a cross-cultural perspective

Assessing the perception of urban parks and understanding the relationship between environmental characteristics and conflicts in park usage are crucial for the design and management of urban parks. However, quantifying perceptions of parks in large spaces poses challenges, and different regional cultures also exhibit variations in park usage. Therefore, this study aims to explore from a cross-cultural perspective how the environmental characteristics and perceptions of parks affect visitors' emotions. This research collected online comment data from Weibo check-ins in Shanghai and tweets in London between 2022 and 2023. It applied natural language processing (NLP) technology using SnowNLP for sentiment semantic keyword extraction, identifying the sentiment index of each data entry to quantify visitors' sentiments towards urban parks in Shanghai and London. Simultaneously, street view panoramas were obtained for semantic segmentation using the SegNet model and ADE20K to quantify spatial perceptions as independent variables. On this basis, we used nonlinear regression models and MGWR models to explore the relationship between visitors' sentiment values in parks and the park environment. The results showed that walkability in Shanghai was found to more easily bring positive sentiments to visitors, while visitors in London paid more attention to imageability. Walkability, accessibility, and imageability are of the same importance in Shanghai and London, but the correlation and emphasis of visitor perceptions differ, indicating potential conflicts in park usage among different user groups in the two cities' urban parks. Overall, our study offers differentiated planning suggestions that can provide information for urban design decisions, ultimately contributing to the improvement of human well-being.

Towards carbon neutrality: asymmetric impact of financial development and digitalization on carbon dioxide emissions in Mediterranean countries

The current research investigates the impact of financial development, digitalization, green trade, manufacturing, and national income on carbon dioxide (CO2) emissions of six Mediterranean countries (MEDIT-6). The study uses a nonlinear panel quantile regression model with panel data of MEDIT-6 countries from 1994 to 2022. The study asserts that higher financial development will reduce CO2 emissions for MEDIT-6 countries, as it provides more financing options to invest in green energy and potentially curb excessive energy consumption which in turn reduces CO2 emissions. The study also provides evidence that digitalization in MEDIT-6 countries has led to dematerialization, thereby reducing CO2 emissions. Digitalization makes trade and commerce platforms more efficient by facilitating the smooth flow of information and enhancing the efficiency of production processes. The positive relationship between manufacturing and national income and CO2 emissions exhibits a U-shaped pattern, which supports the existence of Environmental Kuznets Curve (EKC) hypothesis. The study shows how the MEDIT-6 countries have been successful in promoting financial development and digitalization, which helps reduce their CO2 emissions. However, it also raises concerns for policymakers as promoting developmental activities such as manufacturing is inevitable, but it comes with environmental challenges such as higher CO2 emissions. The current study contributes to the reservoir of existing literature by providing fresh evidence from the Mediterranean region on the impact of financial development and digitalization on CO2 emissions.Graphical

Machine learning prediction of microhardness and corrosion current density of Ni-Co alloys produced by scanning jet electrodeposition

ABSTRACT Scanning jet electrodeposition (SJE) is a promising method for electrodeposition. Integrated with automation and machine learning, intelligent electrodeposition can be achieved. In this study, a machine learning method was used to develop the relationship between the process parameters and properties of Ni-Co alloys produced by SJE. To evaluate the various models, the coefficient of determination (R 2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated as performance metrics. The multivariate nonlinear regression (MNR) model shows a higher R 2, lower MAPE and RMSE values, and better performance in both the training and testing sets, indicating superior approximation and prediction accuracies for microhardness. A back-propagation artificial neural network (BP-ANN) shows stable R 2 value of closest to 1, and the lowest MAPE and RMSE for corrosion current density and presents an exceptional generalisation ability. Finally, under the present experimental conditions, the highest microhardness and corresponding process parameters were determined using the MNR model, and the lowest corrosion current density was optimised using the BP-ANN model.

Low Iron Diet Improves Clinical Arthritis in the Mouse Model of Collagen-Induced Arthritis.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal (51 mg/kg) or a low iron diet (5 mg/kg) starting four weeks before the first immunization. From day 4 after the second collagen booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. Concentrations of mouse anti-bovine and anti-mouse collagen type 2 IgG antibodies were measured by ELISA; blood cell counts were performed and mediators of inflammation, tissue matrix degradation, oxygenation and oxidative stress were measured in the mouse sera of both diet groups at the end of the experiment by bead-based multiplex assay. Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) were quantified in whole paw tissue by ELISA. Quantitative PCR was performed in the tissues for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to compare cross-sectional data. Nonlinear regression models were used for longitudinal data of the arthritis scores. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p < 0.001). The immune response against bovine and mouse type 2 collagen did not differ between the two diet groups. Mice fed a low iron diet exhibited significantly lower serum levels of tissue inhibitor of metalloproteinase-1 (TIMP-1), a central regulator of inflammation and tissue matrix degradation (p < 0.05). In addition, a low iron diet led to a significant reduction in red blood cell indices, indicating restricted iron uptake and latent iron deficiency, but had no effect on hemoglobin concentrations or red blood cell counts. There were no differences between the dietary groups in Fe2+ or Fe3+ content in the paws. Based on calculation of the GSH/GSSG ratio and high MDA levels, high oxidative stress and lipid peroxidation were likewise detected in the paws of both diet groups of mice. Consequently, no differences associated with gene expression of key regulators of iron metabolism and ferroptosis could be detected between the paws of both diet groups. Conclusions: Restricted dietary iron intake alleviates immune-mediated inflammation in CIA without causing anemia. This finding suggests a promising option for dietary treatment of arthritis in inflammation. The underlying mechanism causing reduced arthritis may be linked to the complex regulatory network of TIMP-1 and appears to be independent from the local iron levels, oxidative stress and ferroptosis in the synovial tissues.

Association between the cumulative excessive systolic blood pressure and left ventricular mass index

Abstract Background Ambulatory blood pressure (ABP) has been increasingly adopted in day-to-day clinical practices. Although clinicians can access an enormous amount of information regarding an individual’s blood pressures (BPs) through the ABP monitoring (ABPM), to date, only the mean BP is utilized as the diagnostic threshold of hypertension and the therapeutic target for BP control and no other indices were widely used to assess the burdens of high BP in the ABPM. Because elevated systolic BPs (SBPs) cumulatively damage the myocardium and induce hypertrophy and fibrosis, we hypothesized that the summated value of BP readings beyond a target level may be the better indicator of the overall BP loads than the mean BP value. Methods The Korean ABP registry is a multicenter prospective longitudinal cohort study performed in outpatients with hypertension. Among 5,965 patients enrolled in the registry, 951 patients with valid ABPM records and appropriate echocardiography measurements included in the study. The left ventricular mass (LVM) was normalized using the height2.7(m) (LVM index [LVMIH2.7] = LVM/Height2.7). The cumulative excessive SBP CE-SBP) was defined as the summation of the products of excessive SBPs (≥135 mmHg during wake, ≥10 mmHg during sleep) and the durations of the excessive SBP (Figure 1). Results The CE-SBP was non-linearly associated with both the office SBP and the mean SBP, but the association strength was much greater with the mean SBP than with the office SBP (R2=0.902 vs. R2=0.430). In linear regression models, the association of the CE-SBP with the LVMIH2.7 (R2=0.112 [0.074-0.149]) was greater than that of the office SBP (R2=0.072 [0.040-0.103]), the mean SBP (R2=0.082 [0.049-0.116]) or the mean SBP during sleep (R2=0.99 [0.063-0.134]). Non-linear regression models using restrictive cubic spline fits also showed that the association strength was greater with the CE-SBP (R2=0.117 [0.078-0.155]) than with the office SBP, mean SBP or mean SBP during sleep (Figure 2). Left ventricular hypertrophy (LVH) defined as the LVMIH27 ≥54 g/m2.7 was better predicted with the CE-SBP than with the other SBPs. The association between the CE-SBP and LVMIH27 was stronger in the patients not taking antihypertensive medications (R2=0.121) and strongest in those with the non-dipper pattern (R2=0.125). Combination with the variability indices (successive variation and average real variability) during sleep or the maximum SBP during sleep significantly improved the association strength of CE-SBP with LMMIH27, whereas the combination with the mean SBP or the variability indices during the entire period did not, in the linear models. Conclusion The CE-SBP was more strongly associated with the LVMIH27 than the office SBP and the mean SBP in the ABPM. This result suggests that the CE-SBP may be the better indicator of overall BP loads than the office SBP or the mean SBP in the ABPM, in patients with hypertension.Figure 1Figure 2

Unravelling the speed-going relationship: A proof of concept study from British turf flat and jump race meetings

The maximum galloping speeds of racehorses during a race are influenced by the functional performance of the ground (‘going’) amongst other factors. For turf racecourses in Britain, the ground is descriptively classified and numerically quantified on the morning of a race meeting by the clerk of the course and subsequently published to assist decision making. Importantly, this includes deciding whether a horse should or should not run. The going is also assessed and classified during the meeting by racing analysts using the normalized winning times from each race result. Differences between going assessments are regularly reported, therefore this study aimed to evaluate whether an alternative method of measuring going could better predict going measured from performances. Measurement and performance data from 25 flat and 25 jump meetings were compared using linear and nonlinear regression models. A continuous two-phase polynomial model for cushioning was found to be the best predictor of performance going for all 50 meetings (adjusted r2=0.819, P<0.001). As cushioning can provide a going related indicator of the forces that the horse will experience at gallop, this measurement may be useful when evaluating racecourse going. This initial model suggests that there is little performance advantage at maximal galloping speeds above a cushioning value of approximately10 kN, possibly due to changes in limb contact timings to manage limb forces limits as the ground becomes firmer. An expansion of objective measurements of going that relate to performance across a wider geographic region, if not internationally are needed to confirm this limit.

An investigation of the relationship between long bone measurements and stature: Implications for estimating skeletal stature in subadults.

The present study introduces new regression formulae that address several challenges of current subadult stature estimation methods by 1) using a large, contemporary, cross-sectional sample of subadult skeletal remains; 2) generating regression models using both lengths and breadths; 3) utilizing both linear and nonlinear regression models to accommodate the nonlinear shape of long bone growth; and 4) providing usable prediction intervals for estimating stature. Eighteen long bone measurements, stature, and age were collected from computed tomography images for a sample of individuals (n = 990) between birth and 20 years from the United States. The bivariate relationship between long bone measurements and stature was modeled using linear and nonlinear methods on an 80% training sample and evaluated on a 20% testing sample. Equations were generated using pooled-sex samples. Goodness of fit was evaluated using Kolmogorov-Smirnov tests and mean absolute deviation (MAD). Accuracy and precision were quantified using percent testing accuracy and Bland-Altman plots. In total, 38 stature estimation equations were created and evaluated, all achieving testing accuracies greater than 90%. Nonlinear models generated better fits compared to linear counterparts and generally produced smaller MAD (3.65 - 15.90cm). Length models generally performed better than breadth models, and a mixture of linear and nonlinear methods resulted in highest testing accuracies. Model performance was not biased by sex, age, or measurement type. A freely available, online graphical user interface is provided for immediate use of the models by practitioners in forensic anthropology and will be expanded to include bioarchaeological contexts in the future.

A comparative study on the physical fitness of college students from different grades and majors in Jiangxi province.

Research to date has not provided a clear understanding of how different grades and majors affect the physical fitness of college students. It is postulated that there are significant disparities in physical health among college students of different grades and majors. The purpose of this study was to evidence these health disparities and to engage in an extensive analysis and discussion thereof. A sample of 8,772 (2,404 boys and 6,368 girls) Chinese college students from freshman to junior years, aged 17-22, including 12 different majors in four colleges, were recruited in Jiangxi Province. All seven physical fitness indicators (body mass index (BMI), forced vital capacity, 50-m dash, standing long jump, sit and reach, upper body muscle strength, and endurance runs) were conducted for all participants. One-way ANOVA and LSD tests were conducted to compare the physical fitness scores of different grades in the same major. Independent sample t-tests were utilized to compare the differences in every physical fitness indicator for different majors. Pearson's correlations among 12 majors for every grade were conducted to study the significant corrections between the two physical fitness indicators. The body mass index (BMI) and physical fitness indicator (PFI) for college students of different grade were investigated using a nonlinear regression model. The current state of physical fitness among college students is concerning, as the majority of students were barely passing (with a passing rate of 75.3%). Specifically, junior students exhibited lower scores than freshman and sophomore students across all 12 majors. From freshman to junior year, majors of music (78.01±4.58), English (79.29±5.03), and education (76.26±4.81) had the highest scores, respectively, but major art consistently scored the lowest, which were 73.85±6.02, 74.97±5.53, and 72.59±4.84, respectively. Pairwise comparisons revealed more significant differences in individual physical fitness indicators among the three grades in humanities than in sciences. Pearson's correlations showed significant correlations among seven physical fitness indicators in all three grades. PFI had a parabolic trend with BMI both for boy and girl college students in Jiangxi province. The physical fitness indicators of college students in Jiangxi province significantly differed in grades and majors, showing undesirable phenomena. The physical fitness of senior and humanities major college students was much weaker and needs sufficient attention. The relationship between BMI and PFI presented an inverted "U"-shaped change characteristic. Continued nationwide interventions are needed to promote physical activity and other healthy lifestyle behaviors in China.