Robust PCA with Lw,∗ and L2,1 Norms: A Novel Method for Low-Quality Retinal Image Enhancement.

Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to improve retinal image quality. However, these methods often require large datasets and lack robustness in clinical settings. Conversely, the inherent stability and adaptability of traditional unsupervised learning methods, coupled with their reduced reliance on extensive data, render them more suitable for real-world clinical applications, particularly in the limited data context of high noise levels or a significant presence of artifacts. However, existing unsupervised learning methods encounter challenges such as sensitivity to noise and outliers, reliance on assumptions like cluster shapes, and difficulties with scalability and interpretability, particularly when utilized for retinal image enhancement. To tackle these challenges, we propose a novel robust PCA (RPCA) method with low-rank sparse decomposition that also integrates affine transformations τi, weighted nuclear norm, and the L2,1 norms, aiming to overcome existing method limitations and to achieve image quality improvement unseen by these methods. We employ the weighted nuclear norm (Lw,∗) to assign weights to singular values to each retinal images and utilize the L2,1 norm to eliminate correlated samples and outliers in the retinal images. Moreover, τi is employed to enhance retinal image alignment, making the new method more robust to variations, outliers, noise, and image blurring. The Alternating Direction Method of Multipliers (ADMM) method is used to optimally determine parameters, including τi, by solving an optimization problem. Each parameter is addressed separately, harnessing the benefits of ADMM. Our method introduces a novel parameter update approach and significantly improves retinal image quality, detecting cataracts, and diabetic retinopathy. Simulation results confirm our method's superiority over existing state-of-the-art methods across various datasets.

The impact of enhanced Milieu teaching with phonological emphasis (EMT + PE) on the speech and language outcomes for toddlers with cleft palate in Brazil and the United States of America.

PURPOSE: The purpose of this study was to compare the speech and language outcomes of children with cleft palate with or without cleft lip (CP+/-L) in the USA to children with CP+/-L in Brazil who underwent intervention with enhanced Milieu teaching with phonological emphasis (EMT + PE), as there are few cross-country intervention comparisons for children with CP+/-L. METHOD: This is a retrospective analysis of 29 participants from the USA and 24 participants from Brazil who were matched on age. The US participants were between the ages of 13-35 months (M = 23.76), spoke Standard American English in the home, and were recruited from East Tennessee State University and Vanderbilt University. The Brazilian participants were between the ages of 20-34 months (M = 25.04), spoke Brazilian Portuguese in the home, and were recruited from the Hospital de Reabilitação de Anomalias Craniofaciais-Universidade de São Paulo. All treatment participants received EMT + PE from trained speech-language pathologists in hospital-university clinics. RESULT: The treatment groups demonstrated greater gains than comparison groups in percent consonants correct, number of different words, and expressive/receptive vocabulary. There was no main effect nor interaction by country. CONCLUSION: The application of EMT + PE in a second culture and language is a viable early intervention option for participants with CP+/-L.

A novel nonparametric time-dependent precision-recall curve estimator for right-censored survival data.

In order to assess prognostic risk for individuals in precision health research, risk prediction models are increasingly used, in which statistical models are used to estimate the risk of future outcomes based on clinical and nonclinical characteristics. The predictive accuracy of a risk score must be assessed before it can be used in routine clinical decision making, where the receiver operator characteristic curves, precision-recall curves, and their corresponding area under the curves are commonly used metrics to evaluate the discriminatory ability of a continuous risk score. Among these the precision-recall curves have been shown to be more informative when dealing with unbalanced biomarker distribution between classes, which is common in rare event, even though except one, all existing methods are proposed for classic uncensored data. This paper is therefore to propose a novel nonparametric estimation approach for the time-dependent precision-recall curve and its associated area under the curve for right-censored data. A simulation is conducted to show the better finite sample property of the proposed estimator over the existing method and a real-world data from primary biliary cirrhosis trial is used to demonstrate the practical applicability of the proposed estimator.

Performance of diagnostic tests based on continuous bivariate markers.

In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers' accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures.

Time-dependent receiver operating characteristic curve estimator for correlated right-censored time-to-event data.

In clinical trials, evaluating the accuracy of risk scores (markers) derived from prognostic models for prediction of survival outcomes is of major concern. The time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve are appealing measures to evaluate the predictive accuracy. Several estimation methods have been proposed in the context of classical right-censored data which assumes the event time of individuals are independent. In many applications, however, this may not hold true if, for example, individuals belong to clusters or experience recurrent events. Estimates may be biased if this correlated nature is not taken into account. This paper is then aimed to fill this knowledge gap to introduce a time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve estimation method for right-censored data that take the correlated nature into account. In the proposed method, the unknown status of censored subjects is imputed using conditional survival functions given the marker and frailty of the subjects. An extensive simulation study is conducted to evaluate and demonstrate the finite sample performance of the proposed method. Finally, the proposed method is illustrated using two real-world examples of lung cancer and kidney disease.

Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling.

Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population.

Correlates of illness uncertainty in cancer survivors and family caregivers: a systematic review and meta-analysis.

PURPOSE: Illness uncertainty is widely recognized as a psychosocial stressor for cancer survivors and their family caregivers. This systematic review and meta-analysis aimed to identify the sociodemographic, physical, and psychosocial correlates that are associated with illness uncertainty in adult cancer survivors and their family caregivers. METHODS: Six scholarly databases were searched. Data synthesis was based on Mishel's Uncertainty in Illness Theory. Person's r was used as the effect size metric in the meta-analysis. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Of 1116 articles, 21 articles met the inclusion criteria. Of 21 reviewed studies, 18 focused on cancer survivors, one focused on family caregivers, and 2 included survivors and family caregivers. Findings identified distinct correlates for illness uncertainty in cancer survivors, including sociodemographic factors (e.g., age, gender, race), stimuli frame (e.g., symptom, family history of cancer), structure providers (e.g., education), coping, and adaptation. Notable effect sizes were observed in the correlations between illness uncertainty and social support, quality of life, depression, and anxiety. Caregivers' illness uncertainty was associated with their race, general health, perception of influence, social support, quality of life, and survivors' prostate-specific antigen levels. Insufficient data precluded examining effect size of correlates of illness uncertainty among family caregivers. CONCLUSION: This is the first systematic review and meta-analysis to summarize the literature on illness uncertainty among adult cancer survivors and family caregivers. Findings contribute to the growing literature on managing illness uncertainty among cancer survivors and family caregivers.

Biological insights from multi-omics analysis strategies: Complex pleotropic effects associated with autophagy.

Research strategies that combine molecular data from multiple levels of genome expression (i.e., multi-omics data), often referred to as a systems biology strategy, has been advocated as a route to discovering gene functions. In this study we conducted an evaluation of this strategy by combining lipidomics, metabolite mass-spectral imaging and transcriptomics data from leaves and roots in response to mutations in two AuTophaGy-related (ATG) genes of Arabidopsis. Autophagy is an essential cellular process that degrades and recycles macromolecules and organelles, and this process is blocked in the atg7 and atg9 mutants that were the focus of this study. Specifically, we quantified abundances of ~100 lipids and imaged the cellular locations of ~15 lipid molecular species and the relative abundance of ~26,000 transcripts from leaf and root tissues of WT, atg7 and atg9 mutant plants, grown either in normal (nitrogen-replete) and autophagy-inducing conditions (nitrogen-deficient). The multi-omics data enabled detailed molecular depiction of the effect of each mutation, and a comprehensive physiological model to explain the consequence of these genetic and environmental changes in autophagy is greatly facilitated by the a priori knowledge of the exact biochemical function of the ATG7 and ATG9 proteins.

Correction: Mugume et al. Complex Changes in Membrane Lipids Associated with the Modification of Autophagy in Arabidopsis. Metabolites 2022, 12, 190.

In the original article [...].

Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data.

In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology.

[Randomized Controlled Trial of the Effects of Repetitive Transcranial Magnetic Stimulation and Mirror Therapy on Phantom Limb Pain in Amputees].

Objective: To investigate the effect of repetitive transcranial magnetic stimulation (rTMS) on phantom limb pain (PLP) in amputees, and to compare the therapeutic effect with that of mirror therapy (MT). Methods: The study was designed as a randomized controlled trial. The evaluators were blinded, while the subjects and the therapists were unblinded. Subjects were randomly assigned to either the rTMS group or the MT group with a computer-generated random number table. From June 2018 to December 2020, from out of 45 amputee patients screened for the study, 30 who met the inclusion criteria were recruited for the study. All patients were recruited from the Rehabilitation Medicine Center, West China Hospital, Sichuan University. In the end, 4 patients withdrew from the study and 26 patients (12 in the rTMS group and 14 in the MT group) completed the prescribed treatment and evaluation. The rTMS group was given rTMS (1 Hz, 15 min, 5 d/week) for 2 weeks in addition to conventional rehabilitation therapy, while the MT group received MT (corresponding movements of limbs, 15 min, 5 d/week) for 2 weeks in addition to conventional rehabilitation therapy. PLP was evaluated by the Visual Analogue Scale (VAS) and Douleur Neuropathique 4 Questions (DN-4). Subjects were assessed before treatment ( t 0), immediately after the completion of the treatment ( t 1) and 3 months after the completion of the treatment ( t 2). Results: The mean age of the 26 patients was 39.73±12.64. There were 15 males and 11 females. According to the reported description of the characteristics of the PLP by the patients, the characteristics with the highest incidence were tingling, stabbing, numbing, electric shocks and burning in descending order. There was no significant difference in the incidence of PLP characteristics between the two groups ( P>0.05). The two groups had comparable baseline data, showing no significant difference in VAS and DN-4 between the two groups at t 0 ( P>0.05). At t 1 and t 2, the VAS and DN-4 scores were decreased from those of t 0, showing statistically significant difference in both groups ( P<0.01 for both scores). In the rTMS group, there was no significant difference between VAS and DN-4 scores at t 1 and those at t 2 ( P>0.05). In the MT group, the VAS and DN-4 scores at t 2 were significantly lower than those of t 1 ( P<0.05). There was no statistically significant difference between the rTMS group and MT group in the changes in pain measurements, i.e., VAS and DN-4 scores, before and after the intervention ( P>0.05). The 26 patients who completed the experiment showed no dizziness, headache, or other abnormalities during the study. Conclusion: The results of this study indicate that repetitive transcranial magnetic stimulation could improve PLP in amputees, and the improvement effect was comparable to that of mirror therapy.

Complex Changes in Membrane Lipids Associated with the Modification of Autophagy in Arabidopsis.

Autophagy is a conserved mechanism among eukaryotes that degrades and recycles cytoplasmic components. Autophagy is known to influence the plant metabolome, including lipid content; however, its impact on the plant lipidome is not fully understood, and most studies have analyzed a single or few mutants defective in autophagy. To gain more insight into the effect of autophagy on lipid concentrations and composition, we quantitatively profiled glycerolipids from multiple Arabidopsis thaliana mutants altered in autophagy and compared them with wild-type seedlings under nitrogen replete (+N; normal growth) and nitrogen starvation (-N; autophagy inducing) conditions. Mutants include those in genes of the core autophagy pathway, together with other genes that have been reported to affect autophagy. Using Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry (MALDI-MS), we imaged the cellular distribution of specific lipids in situ and demonstrated that autophagy and nitrogen treatment did not affect their spatial distribution within Arabidopsis seedling leaves. We observed changes, both increases and decreases, in the relative amounts of different lipid species in the mutants compared to WT both in +N and -N conditions, although more changes were seen in -N conditions. The relative amounts of polyunsaturated and very long chain lipids were significantly reduced in autophagy-disrupted mutants compared to WT plants. Collectively, our results provide additional evidence that autophagy affects plant lipid content and that autophagy likely affects lipid properties such as chain length and unsaturation.

Measurement invariance of the Kessler Psychological Distress Scale (K10) among children of Chinese rural-to-urban migrant workers.

INTRODUCTION: Kessler Psychological Distress Scale (K10) is a 10-item screening tool designed for nonspecific psychological distress. The current study aims to identify a best-fitting factor structure of the K10, and to test its cross-gender measurement invariance based on the structure. METHODS: Using convenience sampling, we included 339 (n = 192 for boys and 135 for girls) children of Chinese rural-to-urban migrant workers in Hangzhou, China. RESULTS: Confirmatory factor analysis for ordered-categorical measures revealed a two-factor structure as the best-fitting model, in which five items (hopeless, depressed, effort, severely depressed, and worthless) loaded on depression and the other five items loaded on anxiety (tired, nervous, severely nervous, restless, and severely restless). The model held at different levels of the measurement invariance testing, that is, full measurement invariance was not rejected in our sample, suggesting that gender differences as assessed with K10 reflect true differences. Structural invariance testing showed that girls in our sample showed significantly higher levels of depression and anxiety than boys. CONCLUSION: These findings support that the K10 is suitable for gender-comparative research among children of Chinese migrant workers. Using the K10 as a screening tool among this population should be promoted. Limitations and directions for future research were discussed.

Accelerated failure-time model with weighted least-squares estimation: application on survival of HIV positives.

BACKGROUND: Survival analysis is the most appropriate method of analysis for time-to-event data. The classical accelerated failure-time model is a more powerful and interpretable model than the Cox proportional hazards model, provided that model imposed distribution and homoscedasticity assumptions satisfied. However, most of the real data are heteroscedastic which violates the fundamental assumption and consequently, the statistical inference could be erroneous in accelerated failure-time modeling. The weighted least-squares estimation for the accelerated failure-time model is an efficient semi-parametric approach for time-to-event data without the homoscedasticity assumption, which is developed recently and not often utilized for real data analysis. Thus, this study was conducted to ascertain the better performance of the weighted least-squares estimation method over the classical methods. METHODS: We analyzed a REAL dataset on Antiretroviral Therapy patients we recently collected. We compared the results from classical methods of estimation for the accelerated failure-time model with the results revealed from the weighted least-squares estimation. RESULTS: We found that the data are heteroscedastic and indicated that the weighted least-square method should be used to analyze this data. The weighted least-squares estimation revealed more accurate, and efficient estimates of covariates effect since its confidence intervals were shorter and it identified more significant covariates. Accordingly, the survival of HIV positives was found to be significantly linked with age, weight, functional status, CD4 (Cluster of Differentiation agent 4 glycoproteins), and clinical stages. CONCLUSIONS: The weighted least-squares estimation performed the best in providing more significant effects and precise estimates than the classical accelerated failure-time methods of estimation if data are heteroscedastic. Thus, we recommend future researchers should utilize weighted least-squares estimation rather than the classical methods when the homoscedasticity assumption is violated.

Risk of bias assessments in systematic reviews and meta-analyses of behavioral interventions for substance use outcomes.

OBJECTIVE: This study aims to explore the current practice of risk of bias assessment in systematic reviews of behavioral clinical trials published in substance use journals and how assessment results were incorporated into meta-analysis. STUDY DESIGN AND SETTING: The authors searched for systematic reviews and meta-analyses of behavioral interventions published from 2016 to 2020 in 40 substance use journals. Two authors independently screened and extracted relevant information from each review. Different tools for risk of bias assessment and approaches of incorporating the risk of bias assessment results into meta-analysis were summarized. RESULTS: The study identified 35 systematic reviews and meta-analyses of behavioral clinical trials. Among the 35 reviews, 31 (89%) assessed the risk of bias of their included studies. Twelve (39%) of the 31 reviews incorporated these assessment findings into their meta-analysis of intervention effects (e.g., conducted meta-regression or subgroup analysis, sensitivity analysis, limited the synthesis only to the "high quality" studies). CONCLUSION: Performing and reporting risk of bias assessment remain inconsistent in published systematic reviews. Future systematic reviews and meta-analyses are encouraged to connect their risk of bias assessment findings with meta-analysis and follow the most updated PRISMA guidelines in reporting the methods and results of risk of bias assessment.

Influencing Factors of Inflammatory Bowel Disease-Fatigue: A Path Analysis Model.

BACKGROUND: Fatigue is a common symptom in adults with inflammatory bowel disease (IBD) and is influenced by many physiological, psychological, and situational factors. However, the influencing factors of fatigue associated with IBD have not been evaluated. OBJECTIVE: This study aims to examine factors associated with fatigue during IBD and develop a parsimonious model that describes the influencing factors of fatigue. METHODS: The study was a secondary analysis of cross-sectional data obtained from IBD Partners, an online cohort of adults with the disease, including 12,053 eligible participants. Data were collected using the Patient-Reported Outcomes Measurement Information System short-form scales measuring fatigue, sleep disturbances, pain interference, anxiety, depression, and satisfaction with social roles. Physical activity was measured using a single question. Demographic and clinical variables were collected. Path analysis was computed to identify the direct and indirect effects of situational, physiological, and psychological factors on IBD-fatigue based on the middle range theory of unpleasant symptoms' conceptual framework. RESULTS: Most of the participants were White females. The data best fit a model with situational factors (physical activity and satisfaction with social roles as the mediators). The direct effect of IBD activity, age, sleep disturbances, pain interference, anxiety, and depression on IBD-fatigue was significant. Significant indirect effects were noted on IBD-fatigue from sleep disturbances, pain interference, and depression via physical activity and satisfaction with social roles. DISCUSSION: The study identified two important intervening variables from the tested model. In addition, other symptoms such as sleep, pain, anxiety, and depression are essential and also influence IBD-fatigue.

Identifying treatment moderators of a trauma-informed parenting intervention with children in foster care: Using model-based recursive partitioning.

BACKGROUND: Trauma-informed parenting interventions have been used in child welfare to help caregivers respond to children in trauma-informed ways that can mitigate the effects of maltreatment and build strong caregiver-child relationships. Existing studies support their effectiveness with children and youth involved in the child welfare system. However, to further advance the effectiveness of evidenced-based intervention for child welfare populations, one key step is to identify subgroups of individuals who have different intervention responses or outcomes. OBJECTIVE: To identify pre-treatment moderators that can distinguish subgroups of caregivers and children who benefit differently from an intervention. PARTICIPANTS AND SETTING: 414 children in foster care (age 3 or younger) and their caregivers (birth, adoptive, kin, and nonkin) were randomly assigned to receive a trauma-informed parenting intervention in the Illinois Birth through Three Title IV-E waiver demonstration or foster care services as usual. METHODS: Model-based Recursive Partitioning (MOB) was used to identify treatment moderators and moderator interactions. MOB fits a parametric model and uses a data-driven method to find subgroups for which the specified parametric model has different parameters. Two parametric models (logistic and linear regression) were used in accordance with two outcomes: reunification (binary) and caregiver-child attachment (continuous). We examined 21 potential pre-treatment moderators in both models. RESULTS: For the reunification outcome, the MOB produced the following three treatment moderators, which identified subgroups of participants who responded differently to the intervention: (a) caregivers' relationship with the child (kin vs. non-kin/permanent caregivers), (b) caregiver-child attachment, and (c) case history of physical abuse. For the attachment outcome, caregivers' age was found to be a treatment moderator. Future developments of trauma-informed interventions should consider these moderators.

Stochastic cusp catastrophe model and its Bayesian computations.

This paper revitalizes the investigation of the classical cusp catastrophe model in catastrophe theory and tackles the unsolved statistical inference problem concerning stochastic cusp differential equation. This model is challenging because its associated transition density hence the likelihood function is analytically intractable. We propose a novel Bayesian approach combining Hamiltonian Monte Carlo with two likelihood approximation methods, namely, Euler approximation and Hermite expansion. We validate this novel approach through a series of simulation studies. We further demonstrate potential application of this novel approach using the real USD/EUR exchange rate.

Illness Uncertainty and Its Antecedents for Patients With Prostate Cancer and Their Partners.

OBJECTIVES: Guided by Mishel's uncertainty in illness theory, patterns of change in uncertainty were explored over time for patients with prostate cancer and their partners. In addition, the relationships between uncertainty and its antecedents were examined, and the role effects (patient versus partner) on these relationships were assessed. SAMPLE & SETTING: This study is a secondary analysis of the longitudinal data collected from a randomized clinical trial. METHODS & VARIABLES: The current authors fitted multiple-level models that included time-invariant baseline variables (sociodemographics and cancer factors) and time-varying variables (uncertainty, symptoms, and social support) measured at baseline and at 4, 8, and 12 months thereafter. RESULTS: No statistically significant patterns of change in uncertainty over time were detected. Partners reported greater uncertainty than patients. Higher uncertainty was associated with more general and prostate cancer-specific symptoms, recurrent and advanced prostate cancer, higher prostate-specific antigen level, and lower social support. More urinary symptoms were associated with greater uncertainty in patients than in partners. IMPLICATIONS FOR NURSING: Uncertainty management can be tailored for and target symptom management and social support.

Analysis of correlated data with feedback for time-dependent covariates in psychiatry research.

In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.