Development of an equation to predict delta bilirubin levels using machine learning.

OBJECTIVE: Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories. METHODS: A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times. RESULTS: The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv - 0.23 × direct bilirubin - 0.05 × hemoglobin - 0.04 × albumin + 0.10. CONCLUSION: The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.

Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Modeling the Determinants of Subjective Well-Being in Schizophrenia.

BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achieve subjective well-being (SWB). We aimed to identify the determinants of SWB and their interrelationships in schizophrenia. METHODS: Data were obtained from 637 patients with schizophrenia enrolled in multicenter, open-label, non-comparative clinical trials. The SWB under the Neuroleptic Treatment Scale (SWN) was utilized; a cut-off score of 80 indicated a high level of SWB at baseline and 6 months. Various machine learning (ML) algorithms were employed to identify the determinants of SWB. Furthermore, network analysis and structural equation modeling (SEM) were conducted to explore detailed relationship patterns. RESULTS: The random forest (RF) model had the highest area under the curve (AUC) of 0.794 at baseline. Obsessive-compulsive symptoms (OCS) had the most significant impact on high levels of SWB, followed by somatization, cognitive deficits, and depression. The network analysis demonstrated robust connections among the SWB, OCS, and somatization. SEM analysis revealed that OCS exerted the strongest direct effect on SWB, and also an indirect effect via the mediation of depression. Furthermore, the contribution of OCS at baseline to SWB was maintained 6 months later. CONCLUSIONS: OCS, somatization, cognition, and depression, rather than psychotic symptoms, exerted significant impacts on SWB in schizophrenia. Notably, OCS exhibited the most significant contribution not only to the current state of well-being but also to follow-up SWB, implying that OCS was predictive of SWB. The findings demonstrated that OCS management is critical for the treatment of schizophrenia.

Examining the Cycle of Physical Frailty in Falls Clinic Attendees Through Structural Equation Modeling.

In 1998, Fried and Walston introduced the Cycle of Frailty (CF) as a foundational concept for defining the physical frailty phenotype (FP). While the FP has been extensively validated, the CF hypothesis lacks equivalent support. This study aimed to internally validate the CF using structural equation modeling (SEM) in a clinical dataset of adults aged 50 or older attending an outpatient falls clinic. Measures included: age, morbidity, nutrition, sarcopenia by bioelectrical impedance, VO2max, handgrip strength, basal metabolic rate (BMR), 5-times chair stand test (5CST), physical activity, and total energy expenditure (TEE). The SEM, incorporating CF hypothesized causal pathways, was tested using IBM® SPSS® Amos 27.0.0 (maximum likelihood method) with a sample of 102 adults (mean age 69.8 years, 58.8% women). Overall, the SEM was supported by the data (χ2 = 44.4, df = 37, p = 0.189), with significant (p < 0.05) regression weights for morbidity→sarcopenia, age→sarcopenia, sarcopenia→VO2max, sarcopenia→handgrip strength, handgrip strength→5CST, physical activity→TEE, TEE→nutrition, and BMR→TEE. However, nutrition→sarcopenia, sarcopenia→BMR, VO2max→5CST, and 5CST→physical activity were not significant. Although the SEM was limited by inclusion of surrogate CF measures (e.g., 5CST instead of gait speed, VO2max based on age-predicted maximal/resting heart rate), it provided some internal support for the CF hypothesis.

A study of the relationship between social support, depression, alexithymia and glycemic control in patients with type 2 diabetes mellitus: a structural equation modeling approach.

Aim: The aim of this research was to ascertain the correlations between alexithymia, social support, depression, and glycemic control in patients diagnosed with type 2 diabetes mellitus. Additionally, this study sought to delve into the potential mediating effects of social support and depression in the relationship between alexithymia and glycemic control. Method: A purposive sampling methodology was employed to select a cohort of 318 patients afflicted with type 2 diabetes mellitus, hailing from a care establishment situated in Chengdu City. This investigation embraced a cross-sectional framework, wherein instruments such as the General Information Questionnaire, the Toronto Alexithymia Scale 20, the Social Support Rating Scale, and the Hamilton Depression Scale were judiciously administered. The primary objective of this endeavor was to unravel the interplay that exists amongst alexithymia, social support, depression, and glycemic control. The inquiry discerned these interrelationships through both univariate and correlational analyses, subsequently delving into a comprehensive exploration of the mediating ramifications engendered by social support and depression in the nexus between alexithymia and glycemic control. Results: The HbA1c level of patients diagnosed with type 2 diabetes mellitus was recorded as (8.85 ± 2.107), and their current status with regards to alexithymia, social support, and depression were measured as (58.05 ± 4.382), (34.29 ± 4.420), and (7.17 ± 3.367), respectively. Significant correlations were found between HbA1c and alexithymia (R=0.392, P<0.01), social support (R=-0.338, P<0.01), and depression (R=0.509, P<0.01). Moreover, alexithymia correlation with social support (R=-0.357, P<0.01) and with depression (R=0.345, P<0.01). Regarding the mediation analysis, the direct effect of alexithymia on HbA1c was calculated to be 0.158, while the indirect effect through social support and depression were 0.086 and 0.149, respectively. The total effect value was determined to be 0.382, with the mediating effect accounting for 59.95%, and the direct effect accounting for 40.31%. Conclusion: Alexithymia exerts both direct and indirect adverse effects on glycemic control, thereby exacerbating disease outcomes. Hence, it is imperative to prioritize the mental health status of individuals with type 2 diabetes to enhance overall well-being, ameliorate diabetes-related outcomes, elevate patients' quality of life, and alleviate the psychological distress and financial burden associated with the condition.

Driving forces of herbaceous species diversity in natural broadleaf forests from in Maoershan from Northeast China.

The understory herbaceous flora plays a pivotal role in regulating the structural stability, complexity, and ecological function of forest communities. It is crucial to investigate the impact of the intricate connections between these factors and the forces driving the diversity of herbaceous species within natural broadleaf understory forests can assist forest managers in developing optimal forest structure optimization techniques, allowing them to adjust the forest species diversity. In this study, Pearson correlation analysis, conventional correlation analysis, and multiple linear regression were employed to elucidate the relationship between stand structure, soil nutrients, and understory herbaceous species richness in natural broadleaved forests. Structural equation modeling was utilized to ascertain the influence of multiple factors on understory herbaceous species diversity and to evaluate the underlying pathways. The results indicated a significant negative correlation between stand closure and the Simpson's and Shannon-Wiener's indices, and between the mixing degree and the Pielou evenness index, Simpson's index, and Shannon-Wiener's index (p<0.05). Furthermore, a significant positive correlation was observed between soil nutrients, specifically organic matter and total phosphorus, and the Pielou evenness index and Shannon-Wiener's index (p<0.05). It was found that total phosphorus was significantly positively correlated with both the Pielou evenness index and the Shannon-Wiener index (p<0.05). The correlation coefficients of the first group of typical variables in the typical correlation analysis were 0.498 and 0.585, respectively (p<0.05). From the set of typical variables of stand structure, it can be seen that the Hegyi competition index and the canopy density affected the diversity of understory herbaceous plants. The composite index demonstrated the greatest impact, with loadings of 0.872 and -0.506, respectively. The Simpson and Shannon-Wiener indices exhibited the most sensitive loadings of -0.441 and -0.408, respectively. The soil nutrients of SOM and TN affected the understory herbaceous plant species diversity composite index, with greater loadings of -0.184 and 1.002, respectively. The path coefficient of the understory herbaceous diversity stand structure was 0.35. The path coefficient with soil nutrient content was found to be 0.23 following structural equation analysis and the path coefficient between stand structure and soil nutrient content was 0.21, which indirectly affect the diversity of understory herbaceous species. To enhance the diversity of herbaceous species, it is recommended that the canopy density and tree density of the upper forest be reduced appropriately, while the degree of mixing and the level of spatial distribution of trees be adjusted in a manner that maintains a reasonable stand structure. Furthermore, a comprehensive forest management program for improving soil nutrients should be considered.

Ageing well with tech: Exploring the determinants of e-healthcare services adoption in an emerging economy.

The adoption of e-healthcare services is critical for improving healthcare accessibility and efficiency, particularly in regions with diverse populations, such as Malaysia. Although e-healthcare services offer numerous advantages, their adoption is considerably low and requires a thorough understanding of the key factors that influence their use. This study investigated the determinants and dynamics of e-healthcare adoption among adults over 40 years by extending the unified theory of acceptance and use of technology. We employed a quantitative research approach, specifically a cross-sectional design. Data were collected from 393 Malaysian respondents through a structured survey questionnaire, using convenience sampling. They were analyzed using partial least-squares-structural equation modeling. The findings revealed that performance expectancy, effort expectancy, social influence, and perceived product value significantly influenced individuals' intentions to use e-healthcare services. Meanwhile, perceived risk had an insignificant negative effect. Facilitating conditions significantly influenced individuals' intentions and actual usage of e-healthcare services. Furthermore, individuals' intentions to use e-healthcare services significantly affected their actual use of these services. Additionally, the intention to use e-healthcare services mediated the relationship between the factors and usage of e-healthcare services, except for perceived risk. Surprisingly, perceived service accuracy had no significant moderating effect on the relationship between individuals' intention to use and their actual use of e-healthcare services. This study offers valuable insights for educators, practitioners, and policymakers, enriching the scholarly discourse in this field. For education, integrating e-healthcare topics into curricula can enhance digital health literacy. In practice, healthcare providers should focus on improving user experience and addressing barriers to technology adoption. For policy making, developing supportive policies, and infrastructure to facilitate e-healthcare adoption is crucial to enhancing public health outcomes.

Mediation Analysis with Multiple Exposures and Multiple Mediators.

A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.

Tailor-made polymer tracers reveal the role of clay minerals on colloidal transport in carbonate media.

HYPOTHESIS: Host rock weathering and incipient pedogenesis result in the exposition of minerals, e.g., clay minerals in sedimentary limestones. Once exposed, these minerals provide the surfaces for fluid-solid interactions that control the fate of dissolved or suspended compounds such as organic matter and colloids. However, the functional and compositional diversity of organic matter and colloids limits the assessment of reactivity and availability of clay mineral interfaces. Such assessment demands a mobile compound with strong affinity to clay surfaces that is alien to the subsurface. EXPERIMENT: We approached this challenge by using poly(ethylene glycol) (PEG) as interfacial tracer in limestone weathering experiments. FINDINGS: PEG adsorption and transport was dependent on the availability of clay mineral surfaces and carbonate dissolution dynamics. In addition, PEG adsorption featured adsorption-desorption hysteresis which retained PEG mass on clay mineral surfaces. This resulted in different PEG transport for experiments conducted consecutively in the same porous medium. As such, PEG transport was reconstructed with a continuum-scale model parametrized by a Langmuir-type isotherm including hysteresis. Thus, we quantified the influence of exposed clay mineral surfaces on the transport of organic colloids in carbonate media. This renders PEG a suitable model colloid tracer for the assessment of clay surface exposition in porous media.

Uncovering the reasons behind the failure of pastoralists in adopting climate change adaptation strategies.

Climate change has caused pastoralists to face serious challenges all around the world. To reduce climate change vulnerability, adaptation strategies need to be adopted by pastoralists. In this regard, the present research was done to seek the reasons for the failure of the northeastern pastoralists of Iran in adopting climate change adaptation strategies. The study is descriptive, which conducted by a field survey. The target population included 249 pastoralists from 7 pastoral units, of whom 148 people were selected as sample size using the stratified random sampling technique. The survey instrument was a researcher-made questionnaire. The content validity and face validity of the questionnaire were checked by the experts. Convergent validity was also confirmed based on the average variance extracted (AVE). Cronbach's α coefficient and composite reliability (CR) were used to evaluate the internal consistency of the questionnaire. The results showed that social and, regulatory and insurance components were the most critical internal and external weaknesses of the pastoralists' failure in adopting climate change adaptation strategies, respectively. Structural equation modeling showed that external weaknesses had positive and significant effects on internal weaknesses of the pastoralists' failure in adopting climate change adaptation strategies.

Update to the study protocol: minimisation of dialysis risk in hospital patients with chronic kidney disease (MinDial): a multicentre, stepped-wedge, cluster-randomised controlled trial.

This document reports a brief update to the previously published protocol of the MinDial study.Trial registration German Clinical Trials Register DRKS00029691. Registered on 12 Sept. 2022, https://drks.de/search/en/trial/DRKS00029691 .

Sense of coherence on the relationship between COVID-19-related stress and sleep quality among nurses.

AIM: We aimed to examine the mediating effects of the Sense of Coherence (SOC) on the relationship between stress and sleep in nurses during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A cross-sectional survey targeting nurses in Chiba and Tokyo during the summer of 2021 was conducted using Google Forms. Responses were obtained using the Pittsburgh Sleep Quality Index (PSQI), the 13-item version of the SOC scale (SOC-13), and questions regarding COVID-19-related stress. Structural equation modeling (SEM) was performed to investigate relevant relationships. RESULTS: A total of 181 valid responses were obtained from 400 surveyed nurses. SEM analysis showed that COVID-19 stress was indirectly related to PSQI via its effect on SOC-13, with higher SOC-13 negatively correlated with PSQI. CONCLUSIONS: Our findings suggest the need for interventions designed to promote SOC for nurses working under stressful conditions.

Equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting approximation: An efficient implementation.

Orbital-optimized coupled-cluster methods are very helpful for theoretical predictions of the molecular properties of challenging chemical systems, such as excited states. In this research, an efficient implementation of the equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting (DF) approach, denoted by DF-EOM-OCCD, is presented. The computational cost of the DF-EOM-OCCD method for excitation energies is compared with that of the conventional EOM-OCCD method. Our results demonstrate that DF-EOM-OCCD excitation energies are dramatically accelerated compared to EOM-OCCD. There are almost 17-fold reductions for the C 5 H 12 $$ {\mathrm{C}}_5{\mathrm{H}}_{12} $$ molecule in an aug-cc-pVTZ basis set with the RHF reference. This dramatic performance improvement comes from the reduced cost of integral transformation with the DF approach and the efficient evaluation of the particle-particle ladder (PPL) term, which is the most expensive term to evaluate. Further, our results show that the DF-EOM-OCCD approach is very helpful for the computation of excitation energies in open-shell molecular systems. Overall, we conclude that our new DF-EOM-OCCD implementation is very promising for the study of excited states in large-sized challenging chemical systems.

Evolution of psychological distress with age and its determinants in later life: evidence from 17-wave social survey data in Japan.

BACKGROUND: Psychological distress (PD) is a major risk factor for mental health among middle-aged and older adults and affects their quality of life and well-being. This study aimed to examine the evolution of PD with age and the relative importance of its determinants, issues that have been insufficiently studied. METHODS: We used longitudinal data obtained from 17-wave social surveys conducted in Japan from 2005 to 2021, to track 34,128 individuals (16,555 men and 17,573 women) born between 1946 and 1955. We defined PD as a Kessler 6 score (range: 0-24) ≥ 5 and estimated fixed-effects regression models to examine the evolution of its proportion with age. We also conducted a mediation analysis to examine the relative importance of specific mediators such as self-rated health (SRH), activities of daily living (ADL), and social participation, in the association between age and PD. RESULTS: Regression model results confirmed an increase in PD with age. Poor SRH, issues with ADL, and no social participation were key mediators of aging on PD, accounting for 34.2% (95% confidence interval [CI]: 21.0-47.3%), 13.7% (95% CI: 8.2-19.3%), and 10.5% (95% CI: 8.0-13.0%), respectively; consequently increasing PD between 50 and 75 years. CONCLUSION: The results suggest the need for policy support to encourage middle-aged and older adults to promote health and increase social participation in order to prevent depression while aging.

Digital social responsibility towards digital education of international higher education students' institutions: Digital culture as mediator.

Digital social responsibility (DSR) has emerged as a wise strategic decision for all higher education institutions during the new crown pandemic. This study explores whether the DSR dimensions (social, economic, legal, and environmental) enhance digital education (DE) in universities. Specifically, this paper intends to investigate the impact of digital culture (DC) on these relationships as a mediator variable. By adopting a cross-sectional approach, data are gathered through an online survey among a sample of 181 students from different nationalities in higher education institutions in Chang'an-China. The partial least squares-structural equation modelling has been used to perform the statistical analyses. Results revealed that all DSR dimensions directly and indirectly affect DE. DC also significantly influences students' DE during the new crown pandemic. Furthermore, our findings show that DC mediates the link between the social, economic, and environmental DSR aspects and students' DE. The study suggests Chinese universities review their DSR policies and student DC perspectives, and design social digital responsibility education programs. These measures can predict the success of student DE and provide a competitive edge as social media technology advances.

A retrospective study of resting energy expenditure in children hospitalized with different nutritional status.

Background: Resting energy expenditure (REE) refers to the energy consumption of the body in a resting state without skeletal muscle activity. This study aimed to examine the REE among children hospitalized with varying nutritional status. Methods: This was a retrospective study. We enrolled 109 pediatric cases that underwent indirect calorimetry (IC) and divided into four groups: mild malnutrition group (15 cases), moderate malnutrition group (30 cases), severe malnutrition group (32 cases), and obesity group (32 cases). We compared and analyzed the measured REE (mREE) using IC with the predicted REE (pREE) using five energy equations. The paired t-test was used to compare the results of two samples. Pearson analysis was used to assess the correlation between two values. The agreement analysis was performed using the Bland-Altman method. Results: There was no significant difference in mREE between the mild, moderate, and severe malnutrition groups, but each differed significantly from the obesity group. All populations exhibited significant correlation between the mREEs and all five energy equations, and the equation with the highest predictive accuracy was the Schofield equation, which achieved an accuracy of 47.7%. In subgroup analysis, there was no significant difference between mREE and pREE for each of the five equations in the mild, moderate malnutrition groups. Only the prediction result of the Liu equation was not significantly different from the mREE in the severe malnutrition group. The prediction accuracy of the Liu equation was relatively the highest (34.4%). However, in the obese group, there were significant differences in pREE and mREE between the Liu equation and Mifflin equation. Under different nutritional statuses, the results of the Bland-Altman analysis suggested that deviation values between REEs predicted by each equation and mREE were greater than ±10%. Conclusions: There were differences in REE among children with different nutritional status. The results obtained from the five predictive energy equations deviated from the IC results. When REE cannot be measured by IC, it is essential to choose an appropriate predictive energy equation based on the nutritional status of the individual.

Structural Equation Models to Infer Relationships between Energy-related Blood Metabolites and Milk Daily Energy Output in Holstein Cows.

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e. glucose, urea, Beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill Climbing algorithm, implemented with the posterior distribution of the residuals obtained from standard multiple trait model (MTM). These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (-0.149), dMEO → urea (0.038), glucose → BHB (-0.55), glucose → urea (-0.194), CHOL → urea (0.175), BHB → urea (-0.049), and NEFA → urea (-0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95 % probability (P) of the posterior mean being > 0 for positive means or < 0 for negative means include those between dMEO and glucose (-0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (-0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.

Using the Montgomery-Koyama-Smith equation to calculate the stomatal area per unit lamina area for 12 Magnoliaceae species.

BACKGROUND AND AIMS: The Montgomery-Koyama-Smith (MKS) equation predicts that total leaf area per shoot is proportional to the product of the sum of individual leaf widths and maximum individual leaf length, which has been validated for some herbaceous and woody plants. The equation is also predicted to be valid in describing the relationship between the total stomatal area per micrograph (AT) and the product of the sum of individual stomatal widths (denoted as LKS) and maximum individual stomatal length (denoted by WKS) in any particular micrograph. METHODS: To test the validity of the MKS equation, 69,931 stomata (from 720 stomatal micrographs from 12 Magnoliaceae species) were examined. The area of each stoma was calculated using empirical measurements of stomatal length and width multiplied by a constant. Six equations describing the relationships among AT, LKS, and WKS were compared. The root-mean-square (RMSE) and the Akaike information criterion (AIC) were used to measure the goodness of fit, and the trade-off between the goodness of fit and the structural complexity of each model, respectively. KEY RESULTS: Analyses supported the validity of the MKS equation and the power-law equation AT ∝ (LKS∙WKS)α, where a is a scaling exponent. The estimated values of α at the species level and for the pooled data were all statistically smaller than unity, which did not support the hypothesis that AT ∝ LTS∙WTS. The power-law equation had smaller RMSE and AIC values than the MKS equation for the data from the 12 individual species and the pooled data. CONCLUSIONS: These results indicate that AT tends to allometrically scale with LKS∙WKS, and that increases in AT do not keep pace with increases in LTS∙WTS. In addition, using the product of LKS and WKS is better than using only one of the two variables.

Disentangling the relationship between state self-compassion and state coping self-efficacy using dynamic structural equation modeling.

The cross-sectional association between self-compassion and coping self-efficacy has been well documented, but little is known about the extent to which self-compassion or coping self-efficacy persists in daily life. This study used dynamic structural equation modeling to explore the temporal relationship between self-compassion and coping self-efficacy through a daily diary study. Participants (N = 240, Mage = 18.98 ± 0.99 years, 44.8% female) completed 14 consecutive daily diaries on self-compassion and coping self-efficacy (for a total of 3219 observations). We found that self-compassion and coping self-efficacy demonstrated stability through autocorrelations. Self-compassion was a significant predictor of subsequent coping self-efficacy, and coping self-efficacy was a significant predictor of subsequent self-compassion. These findings suggest that there has been a virtuous cycle between state coping self-efficacy and state self-compassion.

An Eulerian formulation for the computational modeling of phase-contrast MRI.

PURPOSE: Computational simulation of phase-contrast MRI (PC-MRI) is an attractive way to physically interpret properties and errors in MRI-reconstructed flow velocity fields. Recent studies have developed PC-MRI simulators that solve the Bloch equation, with the magnetization transport being modeled using a Lagrangian approach. Because this method expresses the magnetization as spatial distribution of particles, influences of particle densities and their spatial uniformities on numerical accuracy are well known. This study developed an alternative method for PC-MRI modeling using an Eulerian approach in which the magnetization is expressed as a spatially smooth continuous function. METHODS: The magnetization motion was described using the Bloch equation with an advection term and computed on a fixed grid using a finite difference method, and k-space sampling was implemented using the spoiled gradient echo sequence. PC-MRI scans of a fully developed flow in straight and stenosed cylinders were acquired to provide numerical examples. RESULTS: Reconstructed flow in a straight cylinder showed excellent agreement with input velocity profiles and mean errors were less than 0.5% of the maximum velocity. Numerical cases of flow in a stenosed cylinder successfully demonstrated the velocity profiles, with displacement artifacts being dependent on scan parameters and intravoxel dephasing due to flow disturbances. These results were in good agreement with those obtained using the Lagrangian approach with a sufficient particle density. CONCLUSION: The feasibility of the Eulerian approach to PC-MRI modeling was successfully demonstrated.