Latent Model Research Articles

Coping Strategies of Patients With Advanced Lung or Colorectal Cancer Over Time: Insights From the International ACTION Study.

A comprehensive understanding of coping strategies of patients with advanced diseases can contribute to providing supportive care that meets patients' needs. However, insight into how coping of this population develops over time is lacking. We examined coping strategies of patients with advanced cancer over time and identified distinct trajectories and their predictors. Data from 675 patients of the control group from the ACTION cluster-randomized trial were analyzed. Patients with lung or colorectal cancer from six European countries (Belgium, Denmark, Italy, the Netherlands, Slovenia and the United Kingdom) completed questionnaires at baseline, 12 and 20weeks. Measures included Denial, Acceptance, and Problem-focused coping (COPE, Brief COPE inventory; scores 4-16 per scale). We used linear mixed models to analyze the data and latent class mixed models to identify stable (within patient change <2) coping strategies. At baseline, patients reported low use of Denial (6.6) and greater use of Acceptance (12.6) and Problem-Focused coping (12.2). These scores did not significantly change. We found four distinct trajectories for the use of Denial, three for Acceptance and five for Problem-Focused coping strategies. Stable trajectories were found in 513 (77%) patients for Denial, 645 (96%) for Acceptance and 602 (91%) for Problem-Focused coping. All coping strategies were stable in 447 (68%) patients and two were stable in 181 patients (28%). Overall, the use of coping strategies was rather stable in the majority of patients with advanced cancer. However, for each of the coping strategies subgroups of patients reported fluctuating coping trajectories.

The relationship between glucose patterns in OGTT and adverse pregnancy outcomes in twin pregnancies.

Traditional fixed thresholds for oral glucose tolerance test (OGTT) results may inadequately prevent adverse pregnancy outcomes in twin pregnancies. This study explores latent OGTT patterns and their association with adverse outcomes. This study retrospectively analyzed 2644 twin pregnancies using latent mixture models to identify glucose level patterns (high, HG; medium, MG; and low, LG) and their relationship with maternal/neonatal characteristics, gestational age at delivery, and adverse outcomes. Three distinct glucose patterns, HG, MG, and LG patterns were identified. Among the participants, 16.3% were categorized in the HG pattern. After adjustment, compared with the LG pattern, the HG pattern was associated with a 1.79-fold, 1.66-fold, and 1.32-fold increased risk of stillbirth, neonatal respiratory distress, and neonatal hyperbilirubinemia, respectively. The risk of neonatal ICU admission for MG and HG patterns increased by 1.22 times and 1.32 times, respectively, compared with the LG pattern. As gestational weeks increase, although there is an overlap in the confidence intervals between the HG pattern and other patterns in the restricted cubic splines analysis, the trend suggests that pregnant women with the HG pattern are more likely to face risks of their newborns requiring neonatal intensive care unit admission, and adverse comprehensive outcomes, compared with other patterns. In addition, with age and body mass index increasing in HG mode, gestation weeks at delivery tend to be later than in other modes. Distinct OGTT glucose patterns in twin pregnancies correlate with different risks of adverse perinatal outcomes. The HG pattern warrants closer glucose monitoring and targeted intervention.

Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos/tide analogue therapy

Background & AimsThe dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos/tide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. MethodsUtilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health Informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase (ALT), and with liver fibrosis/cirrhosis. ResultsWe retrieved data from 1885 adults on NA treatment (median follow-up 6.2 years, interquartile range (IQR) 3.7-9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5-17). Five VL classes were identified from the derivation cohort (n=1367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n=827, 60.5%), class 2 ‘timely virological suppression’ (n=254, 18.6%), class 3 ‘persistent moderate viraemia’ (n=140, 10.2%), class 4 ‘persistent high-level viraemia’ (n=44, 3.2%), and class 5 ‘slow virological suppression’ (n=102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n=518). ALT decreased variably over time in VL-suppressed groups (classes 1, 2, 5; all p<0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had 2-fold increased hazards of fibrosis/cirrhosis compared to class 1 (adjusted hazard ratio, 2.00; 95% CI, 1.33-3.02). ConclusionsHeterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression.

Change processes associated with functional improvement in a web-based version of Skills Training in Affective and Interpersonal Regulation (webSTAIR) for trauma-exposed veterans.

In spite of the evidence that both symptom reduction and functional improvement are important for supporting recovery from trauma, psychotherapy process research has largely focused on mechanisms of symptom reduction. A better understanding of how change occurs in treatments that emphasize functional improvement rather than trauma processing is critical for optimizing effective, patient-centered care. This study involved secondary analysis of data collected in three multisite trials of a 10-module web-based version of Skills Training in Affective and Interpersonal Regulation, a skills-focused transdiagnostic intervention. The sample included 314 trauma-exposed veterans (38% male; 64% non-Hispanic White) who screened positive for elevated symptoms of posttraumatic stress disorder and/or depression. Latent change score modeling examined prospective relationships between changes from pre-to-mid and mid-to-post treatment in four potential mechanisms (emotion regulation, interpersonal problems, posttraumatic stress disorder symptoms, and depression symptoms) and the association of these changes with overall functional improvement (at posttreatment and follow-up). Emotion regulation change during the first half of treatment predicted interpersonal and symptom improvements during the second half of treatment, but not vice versa. Changes in each potential mechanism were uniquely associated with functional improvement and together statistically accounted for 78% additional variance in functional improvement beyond what was associated with baseline functioning and covariates. Results support emotion regulation as an early mechanism of transdiagnostic therapeutic change in web-based version of Skills Training in Affective and Interpersonal Regulation and highlight the relevance of all four potential mechanisms to functional improvement. The study contributes to an understanding of how change occurs in skills-focused interventions for trauma-exposed individuals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trajectories of Short-Term Post-Traumatic Stress Disorder Symptoms in Patients with Post-Intensive Care Syndrome: A Longitudinal Observational Study.

Post-traumatic stress disorder (PTSD) is a major psychiatric health issue among intensive care unit (ICU) survivors with post-intensive care syndrome (PICS). Although early PTSD intervention has been demonstrated to decrease the risk of progression from acute to chronic PTSD, information on the progression trajectory of short-term PTSD symptoms and modifiable risk factors in PICS patients is limited. This study aimed to explore the clinical progression trajectories of short-term PTSD symptoms and the associated factors in PICS patients by conducting a prospective longitudinal observational study. This study was conducted at a tertiary hospital in China. The impact of event scale-revised was used to collect data on the PTSD symptoms of patients at 1, 2, 3, and 4 months post-discharge from the ICU. The latent growth mixture model was used to construct trajectory models for PTSD symptoms and multivariate logistic regression was used to determine the factors associated with the trajectories. A total of 130 ICU survivors with PICS completed the 4-month short-term follow-up. Our results showed that PTSD symptoms in PICS patients manifested as three trajectories, namely, moderate chronic (n = 17, 13.1%), recovery (n = 25, 19.2%), and resilience (n = 88, 67.7%). Compared with the resilience trajectory, age and female were identified as risk factors for the moderate chronic trajectory, while prolonged ICU stay was a risk factor for the recovery trajectory. Our study showed that short-term PTSD symptoms in PICS patients manifested as moderate chronic, recovery, and resilience trajectories. Additionally, our results showed that PTSD screening should be conducted for critically ill patients, especially younger, female, or long-term ICU patients, immediately after their discharge from the ICU.

Attention control mediates the relationship between mental imagery vividness and emotion regulation

Contradictory findings suggest mental imagery may both exacerbate and protect against negative affect. We aimed to reconcile these contradictory findings by considering individual differences (N=989) in imagery vividness, attention control, resilience, emotion regulation strategy, and negative affect (depressive, anxious, and posttraumatic stress symptomology). We hypothesized that attention control would mediate relationships between imagery vividness and emotion regulation strategy use, and psychopathology symptomology. Results revealed that imagery vividness, as mediated by attention control, predicted greater levels of healthy reappraisal and deleterious rumination. Attention control also mediated negative relationships between imagery vividness and catastrophizing, self-blame, and psychopathology symptomology. An exploratory latent structural equation model revealed that imagery vividness and attention control aggregated positively with reappraisal and resilience scores. The present investigation suggests an adaptive function of imagery vividness via the indirect effects of attention control, facilitating adaptive emotion regulation and limiting maladaptive strategy use, thereby protecting against negative affect.

Serum tumor markers and outcomes in lung cancer patients with brain metastases: a retrospective longitudinal cohort study.

Serum tumor markers (STMs) are recommended for cancer diagnosis and surveillance. However, their role in lung cancer with brain metastases (BM) is not yet clear. We aim to analyze the roles of baseline levels of STMs or ongoing STM surveillance on survival. This retrospective longitudinal cohort study included 1,169 lung cancer patients with BM. The STM data during disease course were collected. Distinct trajectory groups were identified using the latent class growth mixed model (LCGMM). The roles of STMs on survival were further analyzed using Kaplan-Meier analysis and Cox proportional hazard models. Serum levels of cytokeratin-19 fragment (CYFRA21-1) (P<0.001), carcinoembryonic antigen (CEA) (P=0.005) and neuron-specific enolase (NSE) (P<0.001) at baseline exhibited significant correlation with overall survival (OS) of patients with BM, serving as independent prognostic factors. Further analysis indicated that baseline CYFRA21-1, CEA, NSE as well as status of key driver genes were independent prognostic factors in non-small cell lung cancer (NSCLC) patients with BM, while for small cell lung cancer (SCLC) patients with BM, baseline NSE and receiving chemotherapy show independent correlations with survival. Furthermore, we delineated the dynamic trajectories of STMs based on changes in disease course. More specifically, compared to those showing a baseline-high trend in CEA levels, the survival of patients with either persistently-rising or consistently normal levels seemed to be more promising. For CYFRA21-1, both early-rising and later-rising trends were observed, indicating a prognosis inferior to that of individuals with normal-level trajectory. Likewise, for NSE, patients with persistently-rising or persistently-descending trends showed no significantly survival difference. However, in comparison with the status of driver genes, receiving radiotherapy and targeted therapy, the dynamic changes in STM levels lacked independent prognostic significance. Further analysis indicated that among BM patients lacking key driver genes, NSE trajectory (P<0.05), CYFRA21-1 trajectory (P<0.05) and receiving chemotherapy (P<0.001) were independent prognostic factors. Baseline levels of serum CYFRA21-1, CEA and NSE, as well as status of key driver genes are recommended for evaluating BM patients' outcome. Dynamic changes of STMs during disease course were not significantly associated with the final outcome of BM patients.

Academic buoyancy, hope, and behavioral engagement in learning English as a foreign language: A mediation model

With the enhancement of positive psychology movement in educational field, the contributive role of academic buoyancy on academic and well-being outcomes has been validated. However, few studies have been conducted to explore the mediation mechanisms between academic buoyancy and engagement, especially in the field of teaching English as a foreign language (EFL). Inspired by the control-value theory (CVT), this study set out to examine how EFL buoyancy related to behavioral engagement, using a latent structural equation modelling (SEM). Gender, age, and family resources are controlled while validating the hypothesized model of “academic buoyancy—hope—behavioral engagement”. The sample comprised 542 students (47.7% female) in their second year of secondary education who responded to survey items in a cross-sectional design. Our findings reveal that academic buoyancy was positively correlated with hope, and behavioral engagement in the EFL settings. Also, it was found that the emotional experience of hope fully mediated the relationships between academic buoyancy and behavioral engagement. The hypothesized model accounts for 79.0% of the variance in EFL hope and 54.5% of the variance in behavioral engagement. These findings shed light on the influence mechanism of academic buoyancy on behavioral engagement in learning English as a foreign language. Implications, limitations, and directions for future research are discussed.

Dance2Music-Diffusion: leveraging latent diffusion models for music generation from dance videos

With the rapid development of social networks, short videos have become a popular form of content, especially dance videos. In this context, research on automatically generating music for dance videos shows significant practical value. However, existing studies face challenges such as limited richness in music timbre and lack of synchronization with dance movements. In this paper, we propose Dance2Music-Diffusion, a novel framework for music generation from dance videos using latent diffusion models. Our approach includes a motion encoder module for extracting motion features and a music diffusion generation module for generating latent music representations. By integrating dance type monitoring and latent diffusion techniques, our framework outperforms existing methods in generating complex and rich dance music. We conducted objective and subjective evaluations of the results produced by various existing models on the AIST++ dataset. Our framework shows outstanding performance in terms of beat recall rate, consistency with GT beats, and coordination with dance movements. This work represents the state of the art in automatic music generation from dance videos, is easy to train, and has implications for enhancing entertainment experiences and inspiring innovative dance productions. Sample videos of our generated music and dance can be viewed at https://youtu.be/eCvLdLdkX-Y. The code is available at https://github.com/hellto/dance2music-diffusion.

Early prediction of gestational diabetes mellitus using first trimester maternal serum pregnancy-associated plasma protein-a: A cross-sectional study.

The oral glucose tolerance test with 75 g glucose is commonly regarded as the gold standard (GS) for the detection of gestational diabetes mellitus (GDM). However, one limitation of this test is its administration in the late second trimester of pregnancy in some countries (e.g., Iran). This study aimed to evaluate the accuracy of pregnancy-associated plasma protein-A (PAPP-A) for predicting GDM in the early first trimester of pregnancy using a novel statistical modeling technique. The study population consisted of 344 pregnant women who participated in the first trimester screening program for GDM. Maternal serum PAPP-A levels were measured between 11 and 13 gestational weeks for all participants. A Bayesian latent profile model (LPM) under the skew-t (ST) distribution was employed to estimate the diagnostic accuracy measures of PAPP-A in the absence of GS test outcomes. The mean (standard deviation) age of the participants was 28.87 ± 5.20 years. The median (interquartile range (IQR)) PAPP-A MoM was 0.91 (0.69-1.34). Utilizing the LPM under the ST distribution while adjusting for covariates, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of PAPP-A were 92% (95% credible interval [CrI]: 0.89, 0.98), 81% (95% CrI: 0.76, 0.91), and 0.91 (95% CrI: 0.83, 0.97), respectively. Notably, the pregnant women with GDM had significantly lower PAPP-A values ( -0.52, 95% CrI [-0.61, -0.46]). Generally, our findings confirmed that PAPP-A could serve as a potential screening tool for the identification of GDM in the early stages of pregnancy.

HiddenSinger: High-quality singing voice synthesis via neural audio codec and latent diffusion models

Recently, denoising diffusion models have demonstrated remarkable performance among generative models in various domains. However, in the speech domain, there are limitations in complexity and controllability to apply diffusion models for time-varying audio synthesis. Particularly, a singing voice synthesis (SVS) task, which has begun to emerge as a practical application in the game and entertainment industries, requires high-dimensional samples with long-term acoustic features. To alleviate the challenges posed by model complexity in the SVS task, we propose HiddenSinger, a high-quality SVS system using a neural audio codec and latent diffusion models. To ensure high-fidelity audio, we introduce an audio autoencoder that can encode audio into an audio codec as a compressed representation and reconstruct the high-fidelity audio from the low-dimensional compressed latent vector. Subsequently, we use the latent diffusion models to sample a latent representation from a musical score. In addition, our proposed model is extended to an unsupervised singing voice learning framework, HiddenSinger-U, to train the model using an unlabeled singing voice dataset. Experimental results demonstrate that our model outperforms previous models regarding audio quality. Furthermore, the HiddenSinger-U can synthesize high-quality singing voices of speakers trained solely on unlabeled data.

Commonalities and differences in strategies for regulating motivation and emotion in academic settings: A within-person approach

BackgroundMotivation and emotion form important pillars of students’ educational experiences and, while representing distinguishable constructs, are closely intertwined. Consequently, it can be assumed that their regulation may be governed by similar mechanisms as well. From a theoretical perspective, MR and ER strategy taxonomies do contain overlap, particularly among strategies involving reappraisals of personal competencies, but also unique (i.e., non-overlapping) strategies. Empirically, however, motivational regulation (MR) and emotion regulation (ER) have had little intersection in prior research and stem from rather disconnected research traditions. AimsBuilding on previous work on the functional interplay between students’ motivation and emotion, we examined similarities and differences in MR and ER strategies and tested the assumption that MR strategies are also used to regulate emotions, and ER strategies to regulate motivation, in study situations. SampleParticipants were 1,466 university students. MethodUsing a within-person design, students reported on their use of various strategies for managing regulatory problems involving either low motivation or negative emotions (anxiety, boredom). ResultsUsing CFA and latent difference modeling, we found that strategy use was strongly correlated and differed little in terms of mean levels across motivational and emotional regulation problems. These correlations were even stronger, and mean differences smaller, than those found for regulatory problem distinctions within motivational and emotional problems. ConclusionsThe findings indicate that many designated MR and ER strategies as distinguished in current taxonomies may be relevant for managing both motivational and emotional problems and underscore the need for joint theoretical perspectives on MR and ER.

Empowering Nontraditional College Students: A Self-Transformative Intervention for Promoting Positive Identity Development

ABSTRACT Identity development has been consistently linked to a variety of positive adjustment indicators and plays a critical role during times of uncertainty. Despite this, only a handful of interventions have been developed to promote positive identity development. However, these interventions have not only focused on adolescent and emerging adults, but apart from a handful of social identity interventions, have also not targeted specific domains of identity development. In the current study, we outline the development of a domain-specific identity intervention, the Making Career and Educational Choices workshop (MCEC), and evaluate pre to posttest outcomes in a sample of 58 college students (M age = 32.33, S.D.age = 10.95; 48% White; 82% female). Students were assigned to either MCEC (n = 32) or a control (n = 26) group by random assignment of selected time slots. Two-wave latent change modeling indicated intervention gains in identity commitment making, identification with commitment, and identity exploration in breadth and in depth. Although preliminary, the relatively large estimates of intervention-related change provide support for the capacity of career-focused identity interventions to support students in choosing and committing to a future life direction. Implications, challenges, and future directions are discussed to guide future research.

A Latent Space Model for Weighted Keyword Co-occurrence Networks with Applications in Knowledge Discovery in Statistics

Keywords are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic keyword co-occurrence network and propose a latent space model for analyzing it. Our model has two special characteristics. First, it is applicable to weighted networks however, most previous models were primarily designed for unweighted networks. Simply replacing the frequency of keyword co-occurrence with binary values would result in a significant loss of information. Second, our model can handle the situation where network nodes evolve over time, and assess the effect of new nodes on network connectivity. We utilize the projected gradient descent algorithm to estimate the latent positions and establish the theoretical properties of the estimators. In the real data application, we study the keyword co-occurrence network within the field of statistics. We identify popular keywords over the whole period as well as within each time period. For keyword pairs, our model provides a new way to assess the association between them. Finally, we observe that the interest of statisticians in emerging research areas has gradually grown in recent years. Supplementary materials for this article are available online

Longitudinal analysis of lower limb muscle activity and ankle tendon biosignals using structural equation modeling.

We collected biosignals from 63 participants and extracted the features corresponding to each level of exerted muscle force. Data were classified into typical and atypical patterns. Data analysis was performed using the Linear Latent Curve Model (LCM) and the Conditional Linear LCM. The typical patterns demonstrated a high degree of fit. Factors, such as ankle circumference and muscle mass, influenced the model intercept. A larger ankle circumference indicated attenuation of signal transmission from the tendon to the skin surface, leading to lower biosignal values. These results indicate that biosignals from the tendons near the ankle can be captured using piezoelectric film sensors. There are studies that define biosignals originating from tendons as mechanotendography. It has been demonstrated that the relationship between biosignals originating from tendons and the exerted muscle force can be explained linearly. Insights from this study may facilitate individualized approaches in the fields of motion control and rehabilitation. Physiological studies to elucidate the mechanisms underlying biosignal generation are necessary.

Applying the Expectation-Maximization Algorithm to Multivariate Signal Extraction

Understanding time series signal extraction is vital for federal agencies, especially as data collection accelerates. One natural extension is moving from univariate to multivariate signal extraction, which offers the promise of reducing extraction error by exploiting cross-sectional relationships. However, such an extension ushers in new computational challenges, viz. larger parameter spaces and more complicated objective functions. The Expectation-Maximization (EM) algorithm provides a methodology to implicitly compute (or approximate) maximum likelihood estimates (MLEs). This paper provides methodology for applying the EM algorithm to a class of latent component multivariate time series models that allow for a nuanced specification of the unobserved signal. We derive an explicit formula for the maximization step, which facilitates computation speed while also improving the stability of the algorithm. Numerical studies demonstrate EM’s ability to efficiently compute MLEs in low-dimensional systems, while also providing feasible estimates in moderate-dimensional systems where MLEs are infeasible to compute. Applications to monthly housing starts and daily immigration are provided.

Effects of hemodialysis adequacy on chronic kidney disease complications using latent class trajectory modeling: a real-world study based on long-term observation of Kt/V.

Previous studies on hemodialysis adequacy primarily focused on the association between Kt/V and survival, and low Kt/V is associated with increased mortality. There is a paucity of research on the correlation between Kt/V and chronic kidney disease (CKD) complications. The retrospective study was conducted in the blood purification center of a tertiary hospital in China from July 2020 to September 2022. It aimed to analyze the association between latent Kt/V trajectory categories and CKD complications (hypertension, anemia, mineral and bone disorder) and inflammatory markers. The latent class trajectory model was established to describe the different patterns of Kt/V changes over the observation period. During the 2-year study period, 93 patients on thrice-weekly hemodialysis with residual kidney function <2 mL/min were included. In the 3-class Kt/V trajectory model, 21 patients were in class 1 with a Kt/V trajectory that declined from a higher to lower levels (from >1.6 to <1.4), 59 patients were in class 2 with Kt/V consistently in a relatively low range (around 1.4), and 13 patients were in class 3 with Kt/V stabilized around 1.6. No significant difference in CKD complications or inflammation markers was observed among the three Kt/V trajectories. Under the premise of adequate Kt/V, neither a stable higher Kt/V nor a declined Kt/V significantly influenced CKD complications or inflammatory markers.

Spatio‐temporal occupancy models with INLA

Abstract Modern methods for quantifying, predicting and mapping species distributions have played a crucial part in biodiversity conservation. Occupancy models have become a popular choice for analysing species occurrence data due to their ability to separate out the observational error induced by imperfect detection of a species, and the sources of bias affecting the occupancy process. However, the spatial and temporal variation in occupancy that is not accounted for by environmental covariates is often ignored or modelled through simple spatial structures as the computational costs of fitting explicit spatio‐temporal models is too high. In this work, we demonstrate how Integrated Nested Laplace Approximation (INLA) may be used to fit complex spatio‐temporal occupancy models and how the R‐INLA package can provide a user‐friendly interface to make such complex models available to users. We show how occupancy models, provided some simplification on the detection process is assumed, can be framed as latent Gaussian models and, as such, benefit from the powerful INLA framework. A large selection of complex modelling features, and random effect models, including spatio‐temporal models, have already been implemented in R‐INLA. These also become available for occupancy models, providing the user with an efficient, reliable and flexible toolbox. We illustrate how INLA provides a flexible and computationally efficient framework for developing and fitting complex occupancy models using two complex case studies. Through these, we show how different spatio‐temporal models that include spatial‐varying trends, smooth terms and spatio‐temporal random effects can be fitted to aggregated detection/non‐detection data. At the cost of limiting the complexity of the detection model structure, INLA can incorporate a range of rather complex structures in the ecological process of interest and hence, extend the functionality of occupancy models. The limitations of occupancy models in terms of scalability for large spatio‐temporal data sets remain a challenge and an active area of research. INLA‐based occupancy models provide an alternative inferential and computational framework to fit complex spatio‐temporal occupancy models. The need for new and more flexible computationally efficient approaches to fit such models makes INLA an attractive option for addressing complex ecological problems, and a promising area of research.

Pain mechanistic networks: the development using supervised multivariate data analysis and implications for chronic pain

Abstract Chronic postoperative pain is present in approximately 20% of patients undergoing total knee arthroplasty. Studies indicate that pain mechanisms are associated with development and maintenance of chronic postoperative pain. The current study assessed pain sensitivity, inflammation, microRNAs, and psychological factors and combined these in a network to describe chronic postoperative pain. This study involved 75 patients with and without chronic postoperative pain after total knee arthroplasty. Clinical pain intensity, Oxford Knee Score, and pain catastrophizing were assessed as clinical parameters. Quantitative sensory testing was assessed to evaluate pain sensitivity and microRNAs, and inflammatory markers were likewise analyzed. Supervised multivariate data analysis with “Data Integration Analysis for Biomarker Discovery” using Latent cOmponents (DIABLO) was used to describe the chronic postoperative pain intensity. Two DIABLO models were constructed by dividing the patients into 3 groups or 2 defined by clinical pain intensities. Data Integration Analysis for Biomarker discovery using Latent cOmponents model explained chronic postoperative pain and identified factors involved in pain mechanistic networks among assessments included in the analysis. Developing models of 3 or 2 patient groups using the assessments and the networks could explain 81% and 69% of the variability in clinical postoperative pain intensity. The reduction of the number of parameters stabilized the models and reduced the explanatory value to 69% and 51%. This is the first study to use the DIABLO model for chronic postoperative pain and to demonstrate how different pain mechanisms form a pain mechanistic network. The complex model explained 81% of the variability of clinical pain intensity, whereas the less complex model explained 51% of the variability of clinical pain intensity.

Is the clique a pond? The big-fish-little-pond effect and the relative meaning of clique and classroom

BackgroundAcademic self-concept (ASC) is a key predictor of learning behaviors and educational outcomes. In adolescence, the evaluation of academic abilities is mainly shaped by the social environment and comparisons with various reference groups. The effect of making social comparisons with the academic achievement of a reference group is known as the big-fish-little-pond effect (BFLPE). AimsBased on social comparison theory and the local dominance effect (LDE), the present study aimed to investigate a pivotal adolescent reference group beside the classroom: the clique. We investigated to what extent students’ social comparison with the mathematics achievement of clique members was related to the ASC of individual students more than general classroom comparisons. SampleThe sample comprised 743 German secondary students in sixth and eighth grade (93 cliques in 40 classrooms). MethodsWe estimated a three-level structural equation model using data from a two-wave longitudinal study in one school year. The change in students’ ASC was determined using latent change modeling. ResultsThe average academic achievement of the clique negatively predicted ASC development. Classroom academic achievement had no effect. ConclusionsThe results underline the importance of informal peer groups like cliques as important reference groups for social comparisons and ASC development during adolescence.