Stepwise Linear Regression Model Research Articles

Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa

Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.

Effects of Heavy Alcohol Use on Fasting Glucose and Insulin Resistance in Mid-Life Adults

Introduction: As our population ages, type 2 diabetes has become more prevalent in the United States and one in five mid-life adults (50-64 years) have type 2 diabetes. Unhealthy and heavy alcohol use (that exceeds recommended drinking limits) causes dysfunction of the liver and pancreas that can impact glucose control and thereby increase risks of type 2 diabetes. In young adults, we previously found that fasting glucose and insulin resistance were not associated with heavy alcohol use. However, it remains unclear if heavy alcohol use contributes to the development of type 2 diabetes in an aged population. Therefore, the purpose of this study is to determine the effect of heavy alcohol use on fasting glucose and insulin resistance in apparently healthy mid-life adults. Methods: Mid-life men (n=21) and postmenopausal women (n=28), non-smokers, free of obesity and major clinical diseases, were recruited from the community around the Dallas—Fort Worth metroplex. All participants underwent physical exam and fasting blood sampling. Alcohol use was assessed by the US Alcohol Use Disorders Identification Test (USAUDIT) and a dried blood spot phosphatidylethanol (PEth) test. A PEth cutoff of 20 ng/mL was used to define heavy drinkers. Insulin resistance was assessed by calculating HOMA-IR (Homeostatic model assessment for insulin resistance) as insulin (μU/L) x glucose (nmol/L)/22.5. Results: A total of 24 participants had PEth levels of <20 ng/mL (non-heavy drinkers; PEth ranged from <limit of quantification to 13 ng/mL; mean ± SEM for age: 58±1 years and body mass index: 25.2±0.6 kg/m2) and the other 25 participants had PEth levels ≥20 ng/mL (heavy drinkers; PEth ranged from 20 to 665 ng/mL; age: 57±1 years and body mass index: 25.7±0.5 kg/m2). Heavy drinkers had higher USAUDIT scores than non-heavy drinkers (11.3±1.4 vs. 3.3±0.5, p<0.001). No significant difference between these two groups were found in lipid values (P≥0.1) as well as renal (P≥0.3) and liver function values (P≥0.08). Compared to non-heavy drinkers, heavy drinkers had a higher level of fasting blood glucose (96±2 vs. 91±1 mg/dL, P=0.02), but similar blood insulin (9.2±2.5 vs. 5.5±0.5 μIU/L, P=0.2) and HOMA-IR (2.23±0.64 vs. 1.23±0.11, P=0.1). The results for stepwise linear regression model indicate that only USAUDIT score was an independent factor of fasting glucose (B=0.59, p<0.001). Conclusion: Our results indicate that compared to mid-life adult non-heavy drinkers, heavy drinkers had higher fasting blood glucose levels, despite similar renal and liver function. Our results also indicate that higher risks of alcohol use disorder were associated with higher levels of fasting glucose. These data suggest that heavy alcohol use may have negative effects on glucose regulation in aged populations and increase risks of type 2 diabetes. This work was supported by NIAAA AA028537 to CLH. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Differential influence of quadriceps rate of torque development on single- and double-leg landing mechanics in anterior cruciate ligament reconstructed and control females.

The capacity to explosively contract quadriceps within the critical timeframe associated with anterior cruciate ligament (ACL) injury, quantified by the rate of torque development, is potentially essential for safe landing mechanics. This study aimed to investigate the influence of explosive quadricepsstrength on ACL-related sagittal-plane landing mechanics in females with and without ACL reconstruction (ACLR). Quadricepsexplosive strength and landing mechanics were assessed in 19 ACLR and 19 control females during isometric contractions and double-and single-leg jump landings. A stepwise multiple linear regression model determined the variance in each of the landing biomechanics variables for the ACLR limb and nondominant limb of controls that could be explained by the group, rate of torque developmentand/or their interaction. If peak kinetic variables could be predicted by the rate of torque development or interaction, additional analyses were conducted, accounting for knee flexion as a covariate in the regression model. During single-leg landings, ACLR females exhibited greater knee flexion at initial contact than controls (p = 0.04). Greater quadricepsrate of torque development predicted higher peak posterior ground reaction force and anterior tibial shear force in both groups (p = 0.04). However, after controlling for knee flexion angle at those peak forces, quadriceps rate of torque development was not predictive. In double-leg landings, greater explosive quadriceps strength was associated with quicker attainment of peak knee extension moment and posterior ground reaction force in the ACLR limb (p = 0.03). Regardless of ACL injury status, females with greater explosive quadriceps strength adopted safer single-leg landings through increased knee flexion, potentially mitigating ACL loading despite encountering higher peak forces. During double-leg landings, a greater explosive quadriceps strength of the ACLR limb is associated with faster achievement of peak force upon landing. Incorporating explosive quadriceps strengthening into post-ACLR rehabilitation and injury prevention programmes may enhance landing mechanics for reducing primary and subsequent ACL injury risks. Level II.

Micro-needling versus acellular dermal matrix in RT1 gingival recession coverage: A randomized clinical trial.

This randomized trial assessed for the first time the efficacy of coronally advanced flap (CAF) followed by micro-needling (MN) in contrast to CAF with acellular dermal matrix (ADM) on gingival thickness (GT, primary outcome), keratinized tissue width (KTW), clinical attachment level (CAL), probing depth (PD), recession depth (RD), recession width (RW), recession reduction (Rec-Red), complete root coverage (CRC) and percentage of root coverage (all secondary outcomes) in management of RT1 gingival recession in patients with thin gingival phenotype. A total of 24 patients (n = 24) with a thin gingival phenotype and single RT1 gingival recession in the aesthetic zone were randomly allocated to test- (CAF + MN; n = 12) or control group (CAF + ADM; n = 12). All clinical parameters were evaluated at baseline, 3 and 6 months. Both groups independently demonstrated significant gain in GT, RW, RD, CAL, PD, Rec-Red, CRC and percentage of root coverage, with reduced PI and BOP (p < .05) at 3 and 6 months, without intergroup differences (p > .05). At 6 months, KTW gain was significantly higher in CAF + MN (5.08 ± 0.9 mm) than in CAF + ADM-group (4.25 ± 1.06 mm; p < .05). Stepwise linear regression model with GT as dependent variable showed that base-line GT was the only statistically significant predictor for GT with a direct correlation between base-line GT and GT after 6 months. CAF followed by MN could represent a promising graft-less approach for increasing gingival thickness, comparable to CAF with ADM, with superior keratinized tissue width improvement, in the treatment of RT1 recession defects in patients with thin gingival phenotype.

Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.

Emotional regulation and self-perceived quality of life in high-performance mountain sports athletes.

Emotional regulation is an indispensable capacity for human beings, so that alterations in it can lead to the appearance of psychological, social and/or cognitive disorders. Therefore, possessing adequate emotional strategies is intimately related to the quality of life that a person presents. In this sense, high-level athletes suffer constant setbacks and frustrations due to the performance of their sporting activity, in addition to continuous modifications of their daily life activities. Thus, the objective of this research is to explore the emotional regulation and self-perceived quality of life of high-level athletes in mountain sports, analyzing the possible influences of gender, demographic location, body mass index and age. Fifty-four athletes belonging to the High Performance Technification Center of Cáceres (Extremadura, Spain) completed a sociodemographic questionnaire, as well as the Cognitive Emotion Regulation Questionnaire and the WHOQOL-BREF. The Shapiro-Wilkins test was used to analyze the normality of the variables collected and nonparametric statistics were used since the assumption was not met. Both gender and demographic location showed significant differences in the dimensions of the two questionnaires. Likewise, age was associated with the dimensions of both scales, but not body mass index, which was only associated with self-perceived quality of life. In addition, the stepwise linear regression model predicted self-perceived quality of life with a value of 60% across self-culpability, gender body mass index and planning. Therefore, it appears that gender, demographic location, age and body mass index could exert modifications on the levels of emotional regulation and self-perceived quality of life of high-level mountain athletes.

Testing the Relationship of Linguistic Complexity to Second Language Learners’ Comparative Judgment on Text Difficulty

AbstractThis study examined the relationship of linguistic complexity, captured using a set of lexical richness, syntactic complexity, and discoursal complexity indices, to second language (L2) learners’ perception of text difficulty, captured using L2 raters’ comparative judgment on text comprehensibility and reading speed. Testing materials were 180 texts abridged from college English coursebooks, and raters were 90 advanced Chinese learners of L2 English. Forty‐five raters read paired texts and determined which text was harder to understand in each pair, and another 45 raters read paired texts and determined which text they read faster in each pair. Two stepwise linear regression models containing lexical, syntactic, and discoursal features explained 48.1% and 54.6% of the variance in L2 learners’ estimates of text comprehensibility and reading speed, respectively, outperforming four commonly used language readability models. These findings contribute useful insights into the relationship between linguistic complexity and L2 learners’ perception of text difficulty.

Bikeshare–Metrorail Integration in Washington, D.C.: What are the Characteristics of Neighborhoods that Encourage Capital Bikeshare Trips to and from the Metrorail?

Bikeshare has gained popularity worldwide and can serve as a first mile/last mile feeder for public transport. In many bikeshare systems, docking stations near a metrorail station are popular trip origins and destinations and bikeshare can help increase the ridership of the metrorail system as well. The goal of this study is to identify the determinants of bikeshare trips between neighborhood stations and bikeshare docking stations near the metrorail in Washington, D.C. For this study, we divided the bikeshare stations in Washington, D.C., into two categories: docking stations near a metrorail station (defined as bikeshare docking stations within a quarter mile radius of metrorail stations) and neighborhood stations (bikeshare docking stations outside the quarter mile radius of metrorail stations). We used two stepwise linear regression models of bikeshare trips between neighborhoods and metrorail stations stratifying the analysis by the determinants of neighborhood docking stations into four categories: socio-demographic conditions, transportation related, land use, and points of interests. In the backward elimination model, expanding the bike trail supply by an additional 100 m was associated with an increase in 70 annual bikeshare trips between a neighborhood station and a docking station near the metrorail. The forward selection model showed that a 1% increase in the poverty rate of the neighborhood stations was associated with 52 fewer annual such bikeshare trips. This study also found that adding bikeshare docking stations near national parks and neighborhoods with higher population density can promote bikeshare that starts/ends near metrorail stations.

Motivation to change predicts college students’ utilization of self-help resources

Background: Stress and mental health difficulties are common burdens on college students. College counseling centers have been overrun by demand. Thus, self-help interventions may offer a promising alternative to traditional college counseling services. Methods: The current study examined the influence of perceived stress, motivation to change, and self-efficacy beliefs on college students’ use of self-help resources, to determine whether these factors would predict follow-up with self-help recommendations. Results: Results of stepwise linear regression models suggest that perceived stress and self-efficacy did not predict self-help resource utilization. Different types of self-help recommendations did not predict different levels of follow-up. However, motivation to change was a significant predictor college students’ self-help resource use, and this variable was particularly important when self-help recommendations were less structured. Conclusions: These findings can help to predict which college students are likely to make use of self-help resources, and inform targeted individual treatment.

Abstract MP15: Developing and Validating VO 2 Prediction From 6-Minute Walk Test (6MWT) and Association With Cardiovascular Health and Risk Scores: Coronary Artery Risk Development in Young Adults (CARDIA)

Introduction: Cardiorespiratory fitness (CRF) is a valuable diagnostic and prognostic measure. Yet, assessing VO 2 max (indicator of CRF) in population-based studies can be challenging due to requiring sophisticated equipment and trained personnel. Thus, we built and validated a model to estimate VO 2 max from a 6MWT, which is less burdensome for participants (submaximal effort). We tested the hypothesis that our model provides valid estimates of VO 2 max that are associated with cardiovascular health - AHA’s Life’s Simple 7 (LS7) and ASCVD scores. Methods: Data are from 576 CARDIA participants [60% female; 40% Black; mean ± SD age = 61.5 ± 3.6; BMI = 28.6 ± 5.7 kg/m 2 ] who attended the Year 35 follow-up exam (2020-22) and completed VO 2 max and 6MWT tests. The study sample was split randomly into a training (2/3) or test (1/3) set. The prediction equation was developed using stepwise multiple linear regression models in the training set. We evaluated accuracy of the model using student's t-test and Pearson correlation coefficients (r) by examining the difference between the observed and predicted VO 2 max in the test dataset. Linear regression models adjusted for age, and sex were used to examine associations of VO 2 max estimates with cardiovascular health and risk indices. Results: The prediction equation included 6MWT distance, sex, age, BMI, physical activity status, and smoking status (R 2 adj = 0.55, RMSE = 4.84). No significant difference was observed between predicted and measured VO 2 max (26.2 ± 5.4 vs 26.0 ± 7.5 mL/kg/min; p =0.76) and the measures were strongly correlated (r=0.80, p &lt;.001) with Bland-Altman plot is presented (Figure). Predicted VO 2 max explained more variance in LS7 score (R 2 adj = 0.55) compared to measured (R 2 adj = 0.37), while explaining similar variance in ASCVD risk (R 2 adj = 0.40 vs 0.38). Conclusions: This prediction equation provides a convenient and useful estimate of CRF estimate of CRF from 6MWT and may provide utility in clinical practice to screen and monitor CRF when direct measurement is not feasible.

Associations of dual-task walking costs with cognition in Parkinson’s disease

BackgroundGait and cognition are closely associated in Parkinson's disease (PD), with specific cognitive domains being associated with different motor symptoms. By identifying gait parameters affected by cognition, clinicians can develop targeted interventions that address cognitive impairment, improve gait, and reduce the risk of injury in PD patients. Research questionWhat gait parameters are affected by cognition in PD patients during dual-task walking, and how are these parameters related to cognitive function as measured by the Montreal Cognitive Assessment (MoCA)? Methods36 patients with available gait data and cognitive assessments were enrolled. Gait data of usual and dual-task walking sessions were recorded using lightweight wireless wearable sensors attached to trunk, lower, and upper extremities. Dual-task costs were calculated from usual and dual-task measures. Statistical analysis included non-parametric tests, Wilcoxon signed-rank test, Spearman’s correlation, and stepwise linear regression models. ResultsWalking speed, cadence, asymmetry in arm swing (ASA), between arms’ amplitude symmetry (BAS), average stride time, and jerk of the acceleration movement of the legs were found to be affected during the dual-task walking session (P<0.05). Spearman’s correlation showed significant correlations between MoCA scores and ASA (ρ=-0.469, P=0.036) and BAS (ρ=-0.448, P=0.036) costs. Stepwise linear regression models found that MoCA scores were significant predictors of BAS and ASA costs (P<0.05). SignificanceThis study found a significant association between global cognitive ability and several gait parameters costs under cognitive load caused by dual-task walking in PD patients. The study identified the gait parameters that were affected by cognitive load and found that MoCA scores were significant predictors of those gait parameters. Identifying gait parameters affected by cognition can lead to more targeted interventions for improving gait and reducing injury risk in PD patients.

Prevalence of emotional-behavioral disorders with and without comorbidity in 6-8 year old children: the role of mother's reaction to child's negative emotions

Introduction: In the early years of schooling, factors such as being far from parents for 5 hours, academic and social demands can be stressful for children and provide the conditions for creating or intensify some mental disorders. Therefore, it is important to investigate the prevalence of emotional-behavioral disorders and their related factors at this age. Aim: The purpose of the present study was to investigate the prevalence of emotional-behavioral disorders with and without comorbidity in children aged 6 to 8 years, and also to predict the severity of these disorders based on the mother's reaction to the child's negative emotions. Method: The research method was descriptive-analytical. The population included all the first and second grade primary school students of Malayer city in the academic year 2017-2018, and 454 people were selected by cluster sampling method. In order to collect data, the Child's Behavior Checklist (2002) and the Child's Reaction to Negative Emotions Questionnaire (1990) were used. Data were analyzed through SPSS version 22 software and stepwise linear regression model was employed. Results: The findings of the present study revealed that, the pure prevalence of emotional and behavioral disorders in these children was 8.6% and prevalence with comorbidity of these disorders was 7.3%. In addition, mother's reactions to children's negative emotions could predict the severity of children's emotional-behavioral problems (P=0.001). Conclusion: In the age range of 6 to 8, the comorbidity of emotional and behavioral disorders of these children is high and the mother's supportive or unsupportive reactions to the child's negative emotions play a significant role in it, so it is suggested that child psychologists and psychiatrists pay special attention to these findings in diagnostic interviews and treatment strategies.

Immersive virtual reality and computer vision for heritage: visual evaluation and perception of the industrial heritage sites along the Yunnan–Vietnam railway (Yunnan section)

The visual composition and human perception are found to relate to the reuse and tourism of heritage railways. Previous studies have used either environmental audits and on-site interviews that have limitations in terms of cost, time, and measurement scale, or virtual perception base on two-dimension images but with gaps in interactivity, virtual immersion and field of view. This study developed an “objective + subjective” visual evaluation and perception framework integrating Computer Vision (CV) and Immersive Virtual Reality (IVR) to assess the visual quality of industrial heritage sites along the Yunnan-Vietnam Railway (Yunnan Section). The stepwise multiple linear regression models were carried out to investigate the relationship between objective evaluation and subjective perception. The results showed that 16 landscape elements of the heritage sites were successfully segmented. According to the visual perception score bands, the 120 industrial heritage sites were classified as 39 high-score sites, 66 medium-score sites, and 15 low-score sites. In general, although the sky and hard ground accounted for a higher proportion, they had little effect on the sum scores, while the vegetation, water, and buildings played a significant role in the perception of visual quality. The results can help researchers, planners, and government departments clarify the visual quality to scientifically specify bottom-up planning and management solutions for railway industrial heritage sites. Moreover, the simplicity, accuracy, and effectiveness of this framework make it suitable for large-scale visual evaluation of other railway industrial heritage sites and linear heritage sites.

Population Dynamics of Pod Borer &lt;i&gt;Helicoverpa armigera&lt;/i&gt; (Hubner) on Chickpea

A field experiment was carried out to study the population dynamics of pod borer Helicoverpa armigera (Hubner) on chickpea at the Agronomy Farm, College of Agriculture, Swami Keshwanad Rajasthan Agricultural University, Bikaner during two consecutive seasons i.e. rabi 2021-22 and 2022-23. The incidence commenced from first and second week of December i.e. 49th and 50th Standard Metrological Week (SMW) and disappeared in the first fortnight of January. Incidence again started from the third week of January (3rd SMW) which increased gradually and reached peak (6.4 and 5.2 larvae/ m row) in the third and second week of February (8th and 9th SMW) during first and second season. These peaks coincided with the vegetative, flowering, and podding stages. The correlation studies showed that incidence has a positive significant correlation with temperatures (p&lt;0.01) while, morning (r = -0.609 and -0.640, p&lt;0.05) and evening relative humidity (r = -0.728, p&lt;0.01 &amp; -0.666, p&lt;0.05) showed negative significant correlation. Further, stepwise linear regression model indicated that maximum temperature as single predicator independent variable had the strongest relationship with incidence based on high R2 value (61% and 63.40%). This forewarning regression model may help the farmers to take advance decision so as to minimize the damage caused by pod borer on chickpea.

Bayesian optimization algorithm‐based Gaussian process regression for in situ state of health prediction of minorly deformed lithium‐ion battery

AbstractAccurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications.

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China.

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Rapid Start of Antiretroviral Therapy in a Large Urban Clinic in the US South: Impact on HIV Care Continuum Outcomes and Medication Adherence.

Rapid start of antiretroviral therapy (ART) has been associated with improvement in several HIV-related outcomes in clinical trials as well as demonstration projects, but how regional and contextual differences may affect the effectiveness of this intervention necessitates further study. In this study of a large, urban, Southern US clinic-based retrospective cohort, we identified 544 patients with a new diagnosis of HIV during 2016 to 2019 and compared HIV care continuum outcomes for the first 12 months of care before and after rapid start implementation. Kaplan-Meier time-to-event curves were used to summarize time to virologic suppression, and stepwise Cox, linear, and logistic regression models were used to create multivariate models to evaluate the association between rapid start and time to virologic suppression, medication adherence, and retention in care and sustained virologic suppression, respectively. We found that rapid start was significantly associated with improved medication adherence scores (+15.37 points, 95% confidence interval [CI] 9.36-21.39, P < .01) and retention in care (adjusted odds ratio = 1.51, 95% CI 1.05-2.19, P = .03). Time to virologic suppression (median 2.46 months before, 2.56 months after rapid start) and sustained virologic suppression were not associated with rapid start in our setting. Though rapid start was associated with improved medication adherence and retention in care, more support may be needed to achieve the same outcomes seen in other studies and sustained over the entire HIV care continuum, especially in settings with significant patient and systemic barriers to care such as unstable housing, lack of Medicaid expansion, and frequent coverage interruptions.

Spatial and variety distributions, risk assessment, and prediction model for heavy metals in rice grains in China.

In this study, 6229 brown rice grains from three major rice-producing regions were collected to investigate the spatial and variety distributions of heavy metals in rice grains in China. The potential sources of heavy metals in rice grains were identified using the Pearson correlation matrix and principal component analysis, and the health risks of dietary exposure to heavy metals via rice consumption were assessed using the hazard index (HI) and total carcinogenic risk (TCR) method, respectively. Moreover, 48 paired soil and rice samples from 11 cities were collected to construct a predicting model for Cd accumulation in rice grains using the multiple linear stepwise regression analysis. The results indicated that Cd and Ni were the main heavy metal pollutants in rice grains in China, with approximately 10% of samples exceeding their corresponding maximum allowable limits. The Yangtze River basin had heavier pollution of heavy metals than the Southeast Coastal Region and Northeast Plain, and the indica rice varieties had higher heavy metal accumulation abilities compared with the japonica rice. The Cu, Pb, and Cd mainly originated from anthropogenic sources, while As, Hg, Cr, and Ni originated from both natural and anthropogenic sources. The mean HI and TCR values of dietary exposure to heavy metals via rice consumption ranged from 2.92 to 4.31 and 9.74 × 10-3 to 1.44 × 10-2, respectively, much higher than the acceptable range, and As and Ni were the main contributor to the HI and TCR for Chinese adults and children, respectively. The available Si (ASi), total Cd (TCd), available Mo (AMo), and available S (AS) were the main soil factors determining grain Cd accumulation. A multiple linear stepwise regression model was constructed based on ASi, TCd, AMo, and AS in soils with good accuracy and precision, which could be applied to predict Cd accumulation in rice grains and guide safe rice production in contaminated paddy fields.

Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an

At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces.

Correlates of frailty in older female cancer survivors

IntroductionCancer survivors are at risk of frailty because of cancer and its treatment. Understanding the factors that increase the risk of frailty is an important aspect of cancer care for the development of interventions to prevent or manage frailty, thus improving cancer survival and overall quality of life of cancer survivors. This study aimed to identify demographic, clinical, and psychosocial correlates of frailty in older, female cancer survivors. Materials and MethodsThis is a sub-study focusing on the exploratory aim of a larger cross-sectional study (NURS-IIR-IUSCC-0748). A total of 213 female cancer survivors aged 59–87 years old were included from the parent study in the current analysis. Frailty, the primary outcome, was measured using the Tilburg Frailty Indicator scale. The independent variables were age, relationship status, clinical stage of cancer, treatment type, comorbidity, depression, affect, optimism, stress, and social support. Stepwise linear regression modeling identified the independent variables that were significantly associated with frailty. ResultsThe final regression model revealed that high patient-reported stress and depression, comorbidity, not being married or living with a partner, and low positive affect were significantly associated with worsening frailty in this population. DiscussionUnderstanding the context of frailty is important for the design of interventions that target factors known to be associated with frailty in older cancer survivors. Further validation with a larger and a more diverse sample from a broad spectrum of sociodemographic and clinical population would fully account for the multiple independent variables influencing frailty in cancer survivors.