Regression Coefficients Research Articles

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

Increases in amyloid-β42 slow cognitive and clinical decline in Alzheimer's disease trials.

Positive effects of new anti-amyloid-β (Aβ) monoclonal antibodies in Alzheimer's disease (AD) have been attributed to brain amyloid reduction. However, most anti-Aβ antibodies also increase the CSF levels of the 42-amino acid isoform (Aβ42). We evaluated the associations of changes in CSF Aβ42 and brain Aβ-PET with cognitive and clinical end points in randomized trials of anti-Aβ drugs that lowered (β- and γ-secretase inhibitors) or increased CSF Aβ42 levels (anti-Aβ monoclonal antibodies) to test the hypothesis that post-treatment increases in CSF Aβ42 levels are independently associated with cognitive and clinical outcomes. From long-term (≥12 months) randomized placebo-controlled clinical trials of anti-Aβ drugs published until November 2023, we calculated the post-treatment versus baseline difference in ADAS-Cog (cognitive subscale of the Alzheimer's Disease Assessment Scale) and CDR-SB (Clinical Dementia Rate-Sum of Boxes) and z-standardized changes in CSF Aβ42 and Aβ-PET Centiloids (CL). We estimated the effect size [regression coefficients (RCs) and confidence intervals (CIs)] and the heterogeneity (I2) of the associations between AD biomarkers and cognitive and clinical end points using random-effects meta-regression models. We included 25 966 subjects with AD from 24 trials. In random-effects analysis, increases in CSF Aβ42 were associated with slower decline in ADAS-Cog (RC: -0.55; 95% CI: -0.89, -0.21, P = 0.003, I2 = 61.4%) and CDR-SB (RC: -0.16; 95% CI: -0.26, -0.06, P = 0.002, I2 = 34.5%). Similarly, decreases in Aβ-PET were associated with slower decline in ADAS-Cog (RC: 0.69; 95% CI: 0.48, 0.89, P < 0.001, I2 = 0%) and CDR-SB (RC: 0.26; 95% CI: 0.18, 0.33, P < 0.001, I2 = 0%). Sensitivity analyses yielded similar results. Higher CSF Aβ42 levels after exposure to anti-Aβ drugs are independently associated with slowing cognitive impairment and clinical decline. Increases in Aβ42 may represent a mechanism of potential benefit of anti-Aβ monoclonal antibodies in AD.

The relationship between executed cut angle and speed with lower extremity joint angles during unanticipated side-step cutting in soccer players

BackgroundCertain movements patterns have been indicated in knee injuries during cutting while running tasks. Differences in the executed cut angle (ECA) could partially account for group differences in joint kinematics previously observed, including sex differences. Research QuestionAre there relationships between joint angles with entry speed and ECA during side-step cutting in soccer players? MethodsThis cross-sectional study recruited 21 (10 females) soccer players. Participants completed 45o unanticipated side-step cuts in both directions. Kinematic data were captured with a 12 camera motion capture system with 46 reflective markers placed on the participants. Peak joint angles were determined during the stance phase of the cutting task. Entry speed and ECA were determined from pelvis markers. Hierarchical linear models examined relationships between angles, entry speed, and ECA, after accounting for age, sex, and leg preference. Regression coefficients with 95% confidence intervals were reported and statistical significance (p<0.05) were examined using the Wald statistic. ResultsThe mean ECA (24.6o) was lower than the intended 45o angle. Peak joint angles were significantly related to both ECA and entry speed. Specifically, an increase in ECA by 10o (i.e., sharper cuts) would increase hip internal rotation and ankle plantarflexion by 1.8 to 2.1o, and decrease hip adduction, knee abduction and ankle eversion by 1.3 to 2.4o. Faster entry speeds by 0.5m/s would increase hip flexion, hip internal rotation and knee extension angles by 1.8 to 3.8o, and decrease knee abduction by 2.6o. SignificanceStudies evaluating cutting while running should consider ECA and entry speed in their design. Potential differences could confound between-group comparisons of joint angles, including when comparing sexes, and impact interpretations of injury risk.

Multimodal laser opto-ultrasonic spectroscopy (LOUS) Approach to investigate the impact of elemental composition on the engineering properties of high-density tungsten alloys'

The impact of elemental composition on the engineering properties of high-density tungsten alloy is crucial, particularly in relation to grain size, hardness, elastic modulus, and surface degradation. This study introduces a new multimodal laser opto-ultrasonic spectroscopy (LOUS) technique, which simultaneously integrates the benefits of laser-induced breakdown spectroscopy (LIBS) and laser ultrasonic testing to evaluate the engineering properties of tungsten alloys. The findings indicate that the increasing tungsten concentration significantly enhances the samples' hardness, grain size and elastic modulus. The composition of elements and hardness were assessed using a calibration curve derived from the emission intensity ratio (W-II/W-I) and plasma electron temperature (Te) in optical emission. The correlation results of (W-II/W-I) and Te showed significant enhancement with the increase of hardness with a regression coefficient (R2 ≥ 0.996), validating the Saha-Eggert relation and underscoring model effectiveness. Additionally, the correlation of the laser ultrasonic testing parameters (attenuation coefficient and velocities) in assessing grain size and elastic modulus showed good reliability (R2≥0.993) when compared to the results obtained from conventional optical microscopy and tensile testing. The results underscore the accuracy and predictive ability of the LOUS method for in-situ characterization.

Associations of Mental, Social and Pathophysiological factors with Pain Intensity and Capability in Patients with Shoulder Osteoarthritis Prior to Shoulder Arthroplasty: A Dutch Arthroplasty Register study

BackgroundMounting evidence suggests that mental health accounts for greater variation in levels of comfort and capability than pathophysiology severity across a range of musculoskeletal conditions. Using nationwide Dutch Arthroplasty Register (LROI) data, we tested the null hypothesis that none of the available mental, social, and pathophysiological factors are associated with variation in levels of comfort and capability among people with shoulder osteoarthritis prior to arthroplasty. MethodsWe included all adult patients who underwent primary total shoulder arthroplasty for osteoarthritis in the period 2014-2021 with complete measures of shoulder specific capability (Oxford Shoulder Score), pain intensity (10-point Numeric Rating Scale), general wellbeing (the EQ-5D 3-L), the grade of pathophysiology (Walch classification) and categorized social health based on a social deprivation index. In total, 1342 patients with shoulder osteoarthritis preparing for shoulder arthroplasty were included in a regression analysis to seek factors associated with variation in levels of pain intensity and capability. ResultsGreater pain intensity at rest was associated with greater symptoms of anxiety and depression (Regression Coefficient [RC] = -0.41; 95% Confidence Interval [CI] = 0.17 to 0.64; P = <0.01), and women (RC = 0.38; 95% CI = 0.11 to 0.66; P = <0.01). Greater incapability was associated with greater symptoms of anxiety and depression (RC = -3.2; 95% CI = -4.0 to -2.4; P = <0.01), an ASA score of III and IV (RC = -3.1; 95% CI = -4.8 to -1.4; P = <0.01), older age (RC = -0.098; 95% CI = -0.15 to -0.047; P = <0.01), and women (RC = -3.9; 95% CI = -4.9 to -3.0; P = <0.01). Neither comfort nor capability were associated with Walch classification or social deprivation index. ConclusionThe confirmation that variation in levels of comfort and capability among people preparing for shoulder arthroplasty are associated with mindset rather than a measure of glenoid pathophysiology points to the potential benefits of addressing mental health in musculoskeletal health strategies.

Economic impacts of tourism demand on the European food and accommodation industry

Sustainability strategies have become central topics in the literature on tourism development, given the significant increase in tourism demand in Europe. This study examines the economic (cost) impact of increased tourism demand on the European Food and Accommodation Industry. We employed three distinct models — pooled linear regression, flexible nonparametric, and semiparametric smooth coefficients — using data spanning European countries from 2005 to 2020. Our findings reveal the heterogeneous impacts of tourist arrivals and length of stay on costs. Although the direct effects were predominantly positive, the indirect effects were found to be negative, resulting in a minor overall negative effect on the average. Furthermore, we found that the proportion of domestic tourists has a notable impact on costs, revealing that higher proportions are linked to lower direct costs but higher indirect costs on average. These findings provide valuable insights for industry stakeholders and policymakers alike.

Decreases in serum PFAS are associated with decreases in serum lipids: A longitudinal study on a highly exposed population

IntroductionPerfluoroalkyl substances (PFAS) are widely used, ubiquitous and highly persistent man-made chemicals. Previous cross-sectional studies have consistently linked PFAS exposure to alterations in lipid profiles. However, longitudinal investigations are preferred to mitigate issues related to reverse causation and confounding. Hence, we aimed to investigate the association between changes in serum PFAS and changes in serum lipids, while shedding light on potential modifiers of the examined relationships. MethodsWe used data from a health surveillance program offered to residents of a vast area of the Veneto Region (North-Eastern Italy), who had been exposed to PFAS via contaminated drinking water until 2013. We included subjects aged ≥20 years who provided two blood samples over an average 4-year interval (n = 8101). We examined the relationships between changes in PFOA, PFOS and PFHxS and changes in total cholesterol (TC), high-density lipoprotein cholesterol (HDLC) and low-density lipoprotein cholesterol (LDL-C). Linear models were fitted for change in the natural logarithm (ln) of each lipid in relation to the change in the ln of PFAS. From the estimated regression coefficients, we calculated the predicted percentage change in the response for a ln-decrease in PFAS serum concentrations. ResultsOverall concentrations of PFOA, PFOS, PFHxS fell by 62.1 %, 24.4 % and 35.4 % from baseline, while small increases in lipids were observed. Declines in PFAS concentrations were associated with decreases in all lipids. For a ln-decrease in PFOA HDL-C decreased by 1.99 % (95 % CI: 1.28, 2.70), TC by 1.49 % (95 % CI: 0.88, 2.10), and LDL-C by 1.40 % (95 % CI: 0.45, 2.37). ConclusionsWe found a positive association between changes in PFAS concentrations and changes in cholesterol levels, observing the most marked contrasts across sexes and age groups. Our findings support the reversibility of the associations identified in cross-sectional analyses, emphasizing the importance of water treatment measures in mitigating adverse health effects.

Optical Based Surface Plasmon Resonance Sensor for the Detection of the Various Kind of Cancerous Cell.

This research explores a novel biosensor design that exhibits much higher sensitivity compared to conventional biosensors. The biosensor's uniqueness originated from its innovative structure, which incorporates N-FK51A/Ag/AlON/BlueP materials, as well as its cutting-edge fabrication method. The refractive index component was considered when designing the SPR biosensor, which was developed from the angular analysis of the attenuated total reflection (ATR) approach for cancer detection. For instance, the resonance angle shifts by 15.57 deg when the refractive index changes from 1.360 to 1.401, demonstrating the sensor's responsiveness to variation in the refractive index. The sensitivities for skin (basal), cervical (HeLa), blood (Jurkat), adrenal gland (PC12), and breast (MDA-MB-231 and MCF-7) cancer cells were 197.65, 243.66, 255.36, 302.71, 372.57, and 416.85 deg/RIU, respectively. Also, the detection accuracy (DA), the figure of merit (FoM), and the quality factor were 0.37/deg, 155.94 (deg/RIU), and 26.71 RIU-1. We also examine the effects of substituting the noble, dielectric, 2D material layer with conventional biosensor materials for six cancers. Each time, the Ag/AION/BlueP layered structure performed best in distinguishing cancer cells from healthy cells. We also study the prism effects. The proposed biosensor, with a RI of 1.29-1.40, has a linear regression coefficient of R2 of 0.96094.

Seeking the optimal gestational weight gain according to the pre-pregnancy body mass index: a cross-sectional study from Shanghai, China.

Maternal nutritional status is closely related to fetal intrauterine development and an abnormal birth weight increases various disease risks across life stages. To better guide pregnancy weight gain, we aimed to explore the optimal weight gain for pregnant women with different body mass indexes (BMIs). This retrospective cohort study included 68,981 women with singleton live birth between January 2017 and October 2021 in maternity centres in Shanghai, China. The fluctuations of the incidence of small and large for gestational age (small for gestational age (SGA) and LGA, respectively) were recorded at different maternal pre-pregnancy BMI (p-BMI) and different gestational weight gain (GWG) groups to find the lowest point of abnormal fetal weight incidence. The optimal GWG was then determined using a linear regression equation. The lowest risk of LGA/SGA was associated with a maternal p-BMI of 19.46 kg/m2. For pregnant women with maternal p-BMI below 24 kg/m2, we confirmed an optimal GWG linear equation: opt GWG (kg) = -1.94 × p-BMI (kg/m²) + 51, which showed an excellent degree of fit. Women who were overweight and obese could not achieve the lowest risk of LGA/SGA despite controlling their GWG; hence, their BMI should be normalized before pregnancy. By merely using the pre-pregnancy BMI, this study has established the optimal GWG equation, with the goal of achieving the appropriate fetal gestational age. It is a practical measure to ensure desirable pregnancy outcomes and meet the health economics requirements.

Regression Models for Tree Volume Prediction in Pinus Wallichiana Stands of South Western Himalayan Region of Kashmir

This study focuses on assessing tree species diversity and evaluating the efficacy of both linear and non-linear regression models for predicting tree volume. The research was conducted in the slopes of the Pir Panjal range within the Shopian Forest Division, situated in the South-Western Himalayan Region of Kashmir. Data on Pinus wallichiana stands, including diameter at breast height (D) and tree height (H), were meticulously collected using appropriate measurement instruments. Employing a multi-stage sampling technique, ten plots of uniform size (10m x 10m) were selected across 20 blocks, and subsequently, 25 trees were randomly chosen from each plot. Six different linear and non-linear regression equations were fitted to the data, and the most suitable equation was identified for volume estimation. To evaluate the performance of the fitted regression models, metrics such as R-squared (R²), adjusted R², root-mean-square error (RMSE), and Theil’s U statistic were employed. Additionally, validation procedures involved using the half-split approach and the Chow test. Upon analysis, it was determined that when employing diameter (D) and considering the joint effectof diameter and height (D2H (I)) as independent variables, the linear model (V=-1.41+15.12D) and power model (V=2.301 I0.502), quadratic model (V=1.8726+0.663 I- 0.011 I²) with highest R², lowest RMSE and Theil’s U statistic respectively, emerged as the most suitable for volume estimation, demonstrating superior accuracy compared to alternative models. Consequently, our findings extend beyond academic inquiry, offering practical implications for sustainable forest management and planning in the Western Himalayan Regions of Kashmir. By providing robust volume estimation models tailored to Pinus wallichiana stands, our study equips forest managers and policymakers with essential tools for informed decision-making.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (&gt;80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Childhood Physical Fitness as a Predictor of Cognition and Mental Health in Adolescence: The PANIC Study.

Cognitive and mental health problems are highly prevalent in adolescence. While higher levels of physical fitness may mitigate these problems, there is a lack of long-term follow-up studies on the associations of physical fitness from childhood with cognition and mental health in adolescence. We investigated the associations of physical fitness from childhood to adolescence over an 8-year follow-up with cognition and mental health in adolescence. The participants were 241 adolescents (112 girls), who were 6-9years at baseline and 15-17years at 8-year follow-up. Average and change scores for cardiorespiratory fitness (maximal power output [Wmax]; peak oxygen uptake [VO2peak]), motor fitness (10 × 5-m shuttle run), and muscular fitness (standing long jump; hand grip strength) were calculated. Global cognition score was computed from six individual cognitive tasks, and perceived stress and depressive symptoms were assessed at the 8-year follow-up. The data were analysed using linear regression models adjusted for age, sex, and parental education. Average motor fitness was positively associated with global cognition score (standardised regression coefficient [β] - 0.164, 95% confidence interval [CI] - 0.318 to - 0.010) and inversely with perceived stress (β = 0.182, 95% CI 0.032-0.333) and depressive symptoms (β = 0.181, 95% CI 0.028-0.333). Average cardiorespiratory fitness was inversely associated with perceived stress (Wmax: β = - 0.166, 95% CI - 0.296 to - 0.036; VO2peak: β = - 0.149, 95% CI - 0.295 to - 0.002) and depressive symptoms (Wmax: β = - 0.276, 95% CI - 0.405 to - 0.147; VO2peak: β = - 0.247, 95% CI - 0.393 to - 0.102). A larger increase in cardiorespiratory fitness was associated with lower perceived stress (Wmax: β = - 0.158, 95%CI - 0.312 to - 0.003; VO2peak: β = - 0.220, 95%CI - 0.395 to - 0.044) and depressive symptoms (Wmax: β = - 0.216, 95% CI - 0.371 to - 0.061; VO2peak: β = - 0.257, 95%CI - 0.433 to - 0.080). Higher levels of motor fitness in childhood and adolescence were associated with better cognition in adolescence. Higher levels of and larger increases in cardiorespiratory fitness from childhood to adolescence were associated with better mental health in adolescence.

Pengaruh Kualitas Produk dan Pelayanan terhadap Keputusan Pembelian Spare Part di PT. Apexindo Perkasa

This research aims to determine the influence of product and service quality on spare part purchasing decisions at PT. Apexindo Perkasa. The population in this study were all customers at PT. Apexindo Perkasa. Data collection techniques use questionnaires and literatur study. The data analysis techniquen is quantitative. The results of this research show that there is no partial positive and significant effect between product quality on purchasing decisions as evidenced by the regression coefficient value of 0.118, t value of 0.970 and significance value of 0.336; there is a partial positive and significant influence between service on purchasing decisions as evidenced by a regression coefficient value of 0.623, a calculated t value of 3.305 and a significance value of 0.002; there is a simultaneous positive and significant influence between product quality and service on purchasing decisions, proven by The calculated f value is 45.570 and the significance value is 0.000. Purchasing decisions are influenced by product and service quality by 56.4% and the remaining 43.6 are influenced by other variables not examined in this research.

Associations between changes in leg extensor muscle power and physical function after supervised exercise in patients with hip osteoarthritis. Secondary analysis from the hip booster trial

ObjectiveTo investigate associations between changes in leg extensor muscle power of the affected limb (ΔLEP) and changes in physical function after 12 weeks of progressive resistance training (PRT) or neuromuscular exercise (NEMEX) in patients with hip osteoarthritis. DesignSecondary analyses of a randomized controlled trial. From 160 participants enrolled in the clinical trial and cluster randomized to PRT (n ​= ​82) or NEMEX (n ​= ​78), a total of 147 (92%) had complete follow-up data and were included in the analyses. Simple linear and multivariate linear regression models estimated the crude and adjusted associations between ΔLEP normalized to body weight (watt/kg) and changes in performance-based and patient-reported measures of physical function. ResultsAdjusted estimates [95% confidence intervals] showed associations between ΔLEP (watt/kg) and changes in 30-s chair stand test (β: 2.34 [1.33; 3.35], R2: 0.13), 9-step timed stair climb test (β: −1.47 [-2.09; −0.85], R2: 0.38), 40-m fast paced walking test (β: −2.20 [-3.30; −1.11], R2: 0.09), Activities of Daily Life function (β: 8.63 [3.16; 14.10], R2: 0.23) and Sport and Recreation function (β: 10.57 [2.32; 18.82], R2: 0.21) subscales from the Hip disability and Osteoarthritis Outcomes Score. Group allocation to PRT did not lead to greater regression coefficients than in NEMEX. ConclusionsChanges in leg extensor muscle power after supervised exercise are consistently associated with changes in physical function across performance-based and patient-reported measures in patients with hip osteoarthritis. These associations seem to be independent of allocation to PRT or NEMEX.

Estimating Unknown Parameters and Disturbance Term in Uncertain Regression Models by the Principle of Least Squares

In the field of statistics, uncertain regression analysis occupies an important position. It can thoroughly analyze data sets contained in complex uncertainties, aiming to quantify and reveal the intricate relationships between variables. It is worth noting that the traditional least squares method only takes into account the reduction in the deviations between predictions and observations, and fails to fully consider the inherent characteristics of the correlation uncertainty distributions under the uncertain regression framework. In light of this, this paper constructs a statistical invariant with symmetric uncertainty distribution based on the observations and the disturbance term. It also proposes the least squares estimation of unknown parameters and disturbance term in the uncertain regression model based on the least squares principle and, combined with the mathematical properties of the normal uncertainty distribution, gives a numerical algorithm for solving specific estimates. Finally, in order to verify the effectiveness of the least squares estimation method proposed in this paper, we also design two numerical examples and an empirical study of forecasting of electrical power output.

The Prevalence of Arrhythmias, Including Premature Supraventricular and Ventricular Beats and Other Electrocardiographic Patterns, in 24-Hour Holter Monitoring in Patients with Overweight and Obesity

Objectives: this study aims to evaluate the prevalence of various arrhythmias and other electrocardiographic patterns within the group of individuals with overweight and obesity. Methods: One hundred eighty-one adults (90 females and 91 males) were qualified for inclusion in the experimental group. All participants had a body mass index (BMI) exceeding 25 kg/m2 (98 patients with obesity and 83 with overweight). The mean BMI in the obesity group was 33.6 kg/m2, and all participants had class 1 obesity. The control group comprised 69 individuals (56 females and 13 males) with normal BMI. The basic measurements were performed, and the participants filled out questionnaires describing their health conditions and lifestyles. Each participant underwent an electrocardiographic (ECG) examination and a 24 h Holter ECG examination. Results: In patients with class 1 obesity compared to the control patients, the average numbers of premature ventricular beats (PVBs) and premature supraventricular beats (SPBs) were statistically significantly higher (p = 0.030 and p = 0.042). There was a positive correlation between body weight and PVB (p = 0.028) and between body weight and SPB (p = 0.028). Moreover, BMI and waist circumference were correlated with SPB (p = 0.043 and p = 0.031). In the backward stepwise multivariate regression model considering 24 h Holter ECG monitoring, concerning SPB as the dependent variable, it was observed that BMI (especially obesity class 1), type 2 diabetes, and thyroid disease exhibited the highest regression coefficients. Conclusions: obesity, even in class 1, might be a factor in a more frequent occurrence of abnormalities in electrocardiographic tests.

Left ventricular structure and function in relation to sodium dietary intake and renal handling in untreated Chinese patients.

Whether left ventricular structure and function is associated with sodium dietary intake and renal handling while considering blood pressure (BP) remains unclear. Consecutive untreated patients referred for ambulatory BP monitoring were recruited. Standard echocardiography was performed to measure left ventricular structure and function. Fractional excretion of lithium (FELi) and fractional distal reabsorption rate of sodium (FDRNa) were calculated as markers of proximal and distal tubular sodium handling, respectively. The 952 participants (51.0% women; mean age, 50.8 years) included 614 (64.5%) ambulatory hypertension and 103 (10.8%) left ventricular hypertrophy. There were significant interactions of urinary sodium excretion with FELi (P ≤ 0.045), but not FDRNa (P ≥ 0.36), in relation to left ventricular posterior wall thickness (LVPW), mass (LVM) and mass index (LVMI), but not functional measurements. Only in tertile 1 of FELi, the multivariate-adjusted regression coefficients for urinary sodium excretion reached statistical significance (P ≤ 0.049), being 0.16 ± 0.05 mm, 4.32 ± 1.48 g, and 1.64 ± 0.83 g/m2 for LVPW, LVM and LVMI, respectively. In mutually adjusted analyses, the regression coefficient for LVMI was statistically significant for FELi, FDRNa and 24-h systolic BP, being -2.17 ± 0.49, -1.95 ± 0.54, and 2.99 ± 0.51 g/m2, respectively (P < 0.001). Multivariable analysis of variance showed that sodium renal handling indexes (P ≥ 0.14), but not sodium urinary excretion (P = 0.007), were similarly as 24-h BP associated with LVMI. Heat maps on left ventricular hypertrophy provided a graphical confirmation of the findings. Sodium dietary intake and renal handling interact to be associated with left ventricular structure. Renal handling indexes were similarly in size as, jointly in action with and independently of 24-h BP.

A useful mTORC1 signaling-related RiskScore model for the personalized treatment of osteosarcoma patients by using the bulk RNA-seq analysis

AimsThis research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature.BackgroundThe mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear.ObjectiveThe datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected.MethodsThe mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package.ResultsThe mTORC1 score and WGCNA with β = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability.ConclusionWe developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature.

Optimization Design of Redundant Parallel Posture Adjustment Mechanism for Solar Wing Docking Based on Response Surface Methodology

The parallel mechanism exhibits high stiffness and excellent dynamic response, making it ideal for high-precision applications. In our early work, a novel 6-DOF redundant parallel posture mechanism with four limbs for solar wing docking has been proposed; each limb consists of three links and four joints. This paper primarily focuses on optimization design of the mechanism. The calculation of workspace volume reveals that factors influencing the range of posture adjustment include dynamic platform parameters, static platform parameters, the drive trajectory of each kinematic pair, and the angles between each kinematic pair. A sensitivity analysis was conducted to examine the impact of each parameter on the range of posture adjustment. To reduce computational complexity and improve analysis efficiency, a combined approach of single-factor analysis and response surface methodology (RSM) is used in the paper. Single-factor analysis is utilized to evaluate the effect of each parameter on the posture adjustment range. Based on these results, RSM is used to establish a regression model for parameters; thereby, the optimal parameter combination for the mechanism is determined. The regression coefficient R2 = 0.9374 attests to the validity of the proposed model. Finally, a comparison of the posture adjustment range before and after optimization is presented, providing a foundation for the practical application of the redundant parallel mechanism. This paper introduces a novel structural design concept aimed at resolving the conflict between heavy loads and compact sizes in redundant parallel mechanisms while providing valuable insights for miniaturized design.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.