Full Information Research Articles

Correlated play in weakest-link and best-shot group contests

We explore public randomization (Harris et al., 1995) in group contests and introduce group public randomization equilibria (GPRE). We consider group all-pay auctions with weakest-link and best-shot impact functions. While best-shot contests without public randomization are known for their multiplicity of equilibria, introducing public randomization results in a unique GPRE in which only one of the strongest players in each group is active. However, in the weakest-link case, the well-known multiplicity of equilibria becomes even more pronounced with public randomization. Despite this multiplicity, a refinement that selects GPRE immune to coalitional deviations reduces the gamut of GPRE to a unique equilibrium group-effort distribution, which, for identical groups composed of identical agents, features the highest expected total effort among all equilibria of the full information game without correlation devices. The (weak) increase in expected efforts due to correlated strategies is also observed in the best-shot case.

APL-like Subset within NPM1-Mutated Acute Myeloid Leukemia: A Distinct Phenotypic Signature Correlating with Early-Onset Vascular Complications

Background: NPM1 mutations represent the most common genetic alteration in adult acute myeloid leukemia (AML), often in co-occurrence with FLT3-ITD and mutations in genes influencing DNA methylation ( DNMT3A, IDH1, IDH2 and TET2). Whether these genetic alterations correlate with distinct immunophenotypic and clinical features is still a matter of debate. There is growing evidence of distinct immunophenotypic subsets driven by co-occurring genetic mutations in NPM1-mutated AML, potentially accounting for the heterogeneity of clinical presentations and outcome. Mason et alhave described a subtype of NPM1-mutated AML, displaying an immunophenotypic profile resembling that of acute promyelocytic leukemia, specifically, the negativity for both CD34 and HLA-DR, and as such defined APL-like. Aims: We studied the characteristics of a series of patients with APL-like NPM1-mutated AML, focusing on the incidence of vascular events at disease onset, and investigated the impact of some markers (blood counts, coagulation parameters and LDH) reported to correlate with coagulopathy in APL. Methods: The study cohort included patients diagnosed with NPM1-mutated AML at our Centre according to conventional morphological, immunophenotypic, cytogenetic and molecular criteria. Vascular events were defined according to the revised World Health Organization (WHO) bleeding scale and to the CTCAE grading of thromboembolic events. Results: From April 2007 to May 2023, 139 patients with a diagnosis of NPM1-mutated AML were enrolled, of whom 31 (22.3%) featured by APL-like phenotype. Their characteristics are detailed in Table 1. APL-like patients were older (64 y) compared to non-APL-like (57 y, P=0.002) NPM1-mutated patients; no further difference emerged for baseline blood count parameters. Vascular complications (n=22 bleeding events and n=2 thrombotic events) were significantly more frequent in the APL-like (n=10, 34.5%) than non-APL-like (n=16, 13.6%, P=0.015) group. There was a trend for more severe (G3-G4) vascular events in APL-like (3/31, 9.7%) vs non-APL-like (3/108, 2.8%, P=0.12). Also, abnormal coagulopathy-related parameters, including INR ≥1.5 and/or fibrinogen below normal level) were more frequent in APL-like patients (27.6% versus 15.7%,). D-dimer levels resulted significantly higher in APL-like patients (median 5998 ng/ml versus 2287 ng/ml, P=.005). The D-dimer/fibrinogen ratio (DD/FBG) showed significantly higher level in APL-like (median 16.84) vs non-APL-like (4.4, P=.017) patients. Of note, in multivariate analysis, APL-like phenotype maintained a value (OR=2.67, P=0.007) on DD levels from WBC count (OR=2.37, P=0.017), thus suggesting an effect independent from leukocytosis. Also, APL-like subset maintained an age-independent impact (OR=2.77, P=0.041) on the rate of vascular events. Among the 85 patients with full molecular information, there was a significant enrichment IDH1 (36.8%, P=0.002) and IDH2 (47.6%, P=.001) mutations in APL-like vs non-APL-like (6.1% and 8.6%, respectively) patients. No difference in the incidence of TET2 mutations was observed (18.8% and 25% in APL-like and non-APL-like, respectively, P=0.744). As regards outcome, complete remission rate was 85.7% and 78.9% in APL- and non-APL-like (P=0.43). We did not observe any significant difference between APL- and non-APL-like patients in disease-free (13.2 vs 22.0 months; P=0.83) or overall (15.9 vs 15.1 months, respectively; P=0.38) survival. Conclusions: Our findings suggest APL-like signature to be a potential predictor of susceptibility to vascular events within NPM1-mutated AML. Our results deserve validation in a larger patient set and might indicate the utility of an intensive monitoring and supportive care to prevent early vascular, especially hemorrhagic, events in this patient category.

Compact reverse time migration: A real-time approach for full waveform ultrasound imaging for breast.

We present compact reverse time migration (CRTM), a real-time ultrasound imaging method that can exploit the full waveform information of ultrasonic wave records for imaging breast tissue. Conventional reverse time migration (RTM) computes the gradient of the reflective ultrasound data with respect to the perturbation of the velocity model of the soft tissues and the gradient can indicate the interface between different types of body tissue. In contrast to conventional reflection ultrasound (B-mode), which is based on the high-frequency approximation to the wave equation, the RTM algorithm is based on the complete wave equation, and can thus exploit the full waveform (wide-spectrum) information of the data and provide an image with higher resolution. Unfortunately, the computational burden of RTM is noticeably higher than the ray-based B-mode. This precludes real-time applications, one of the most important features of ultrasound imaging. The proposed CRTM algorithm can significantly reduce the computational costs of RTM, such that it can be applied for real-time imaging. We demonstrate the performance of CRTM through a synthetic experiment of ultrasound breast imaging. CRTM can be potentially adapted to related signal-processing fields, such as seismic imaging, acoustic camera systems, and radar imaging.

The Binary Distributed Observer Approach to the Cooperative Output Regulation of Linear Multiagent Systems

In this note, the cooperative output regulation problem of general heterogeneous linear multi-agent systems with binary constraints is studied. To speed up the convergence rate, the binary distributed observer is proposed only utilizing sign of the relative state instead of using full relative information. Under some mild conditions, the binary distributed observer can estimate the state of the leader system exponentially in finite time sense. The leader system can generate various reference signals such as step functions, sinusoidal functions or their combinations. Furthermore, both state and output feedback control protocols are synthesized to solve the problem. An illustrative example is presented to support the feasibility of the proposed framework.

Endogeneity in weakly separable models without monotonicity

We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general settings with multiple indices. We provide examples where our approach can identify treatment effect parameters of interest whereas existing methods would fail. These include models where potential outcomes depend on multiple unobserved disturbance terms, such as a Roy model, a multinomial choice model, as well as a model with endogenous random coefficients. We establish consistency and asymptotic normality of our estimators.

Concept of Kshara and Its Sources in Ayurveda- A Critical Review

Kshara’ is related to ‘Paka’ (preparation) and ‘vidhi’ (application). Because of its superiority among the sharper andauxiliary instruments, caustic alkali performs excision, incision, and scarification, alleviating three doshas and being used forspecific functions. Among the para-surgical measures, all three- Agni (cauterization), Kshara (caustic alkali), and Jalauka (leeches)are important. Still, kshara is the chief because operations excision, incision, etc., cannot be performed by Agni and Jalauka. It isalso applied for specific purposes, such as in Arsha caused by Pitta. This article will be essential in containing from the smallest todeepest knowledge of Akshara, which is available in classics & articles in scattered forms. This study intends to gather allinformation related to Kshara and elaborate on their properties as mentioned in classics, especially in Sushruta Samhita andRasashashtra. The method followed here thoroughly analyzes Sushruta Samhita, its commentaries, and various research articlesthat surfed online. It is found that Sushruta Samhita explains full information regarding Kshara, types, indications, andcontraindications.In contrast, certain dravya, which are not explained as Kshara but possess Kshara properties, are also discussed.The research provides thorough literature & critical review of Kshara, its sources, and their properties which can benefit manydiseases in terms of application in different forms. In this study, the aim is to gather information about Kshara mentioned inclassics and articles. This study aims to collect information about Kshara from traditional and scholarly sources.

1 Executive Functioning and Anxiety/Depressive Symptoms as Mediators Between ADHD and Quality of Life: A 10-year Longitudinal Study

Objective:Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with impaired executive functioning and anxiety/depressive symptoms, which may contribute to reduced quality of life. We tested the hypotheses that (1) a childhood diagnosis of ADHD is related to reduced quality of life in emerging adulthood and that (2) this relationship is mediated by impaired executive functioning and anxiety/depressive symptoms.Participants and Methods:We assessed 85 children and adolescents with ADHD and 50 typically developing peers at baseline (Mean age = 11.6 years, 58% males), two-year follow-up, and 10-year follow-up with neuropsychological tests of executive functioning, the Behavioral Rating Inventory of Executive Function (BRIEF), the Short Moods and Feelings Questionnaire (SMFQ), and the Revised Children’s Manifest Anxiety Scale, second edition (RCMAS-2), as well as the Perceived Quality of Life scale (PQoL) at the 10-year follow-up. Four neuropsychological tests of executive functioning, the Letter-Number Sequencing test (working memory), the Color-Word Interference test condition 3 (response inhibition), the Color-Word Interference test condition 4 (shifting), and the trail making test condition 4 (cognitive flexibility) were combined into a neuropsychological executive functioning composite score based on high factor loadings (> .73). the parent-reported BRIEF indexes, the behavioral regulation index, and the metacognition index were used as measures of everyday executive functioning. We conducted independent samples t-tests to compare the groups and simple and parallel mediation analyses with full information maximum likelihood estimation to examine whether the different executive functioning measures and anxiety/depressive symptoms at two-year follow-up mediated the relationship between baseline ADHD and self-reported quality of life at 10-year follow-up.Results:Baseline ADHD was associated with more difficulties with executive functioning on a composite measure of neuropsychological tests (p < .001, Hedges g = .79) and parent-reported everyday metacognitive (p < .001, Hedges g = 2.37) and behavioral regulation executive functioning (p < .001, Hedges g = 1.55), as well as self-reported anxiety (p < .001, Hedges g = 1.05) and depressive symptoms (p < .001, Hedges g = 1.00) at two-year follow-up. Baseline ADHD was associated with lower self-reported quality of life (p = .001, Hedges g = -.67) at 10-year follow-up. The mediation analysis showed that everyday metacognitive executive functioning (p = .011, ß = -.497) and an anxiety/depressive symptoms composite (p < .001, ß = -.404) parallelly mediated the relationship between ADHD and quality of life.Conclusions:Impaired everyday executive functioning and anxiety/depressive symptoms may represent two distinct pathways to reduced quality of life in emerging adults with a childhood diagnosis of ADHD. These findings stress the importance of targeting both cognitive and emotional aspects in interventions for children and adolescents with ADHD to improve later quality of life.

LagunAR: A City-Scale Mobile Outdoor Augmented Reality Application for Heritage Dissemination.

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest.

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery.

Reconstruction of Femtosecond Laser Pulses from FROG Traces by Convolutional Neural Networks

We report on the reconstruction of ultrashort laser pulses from computer-simulated and experimental second harmonic generation-frequency resolved optical gating (SHG-FROG) spectrograms. In order to retrieve the spectral amplitude and phase we use a convolutional neural network trained on simulated SHG-FROG spectrograms and the corresponding spectral-domain fields employed as labels for the network, which is a complex field encompassing the full information about the amplitude and phase. Our results show excellent retrieval capabilities of the neural network in case of the simulated pulses. Although trained only on computer generated data, the method shows promising results regarding experimentally measured pulses.

Information provision and decision-making in the treatment of abdominal aortic aneurysm: A qualitative study of patient experience.

Shared decision making (SDM) refers to patients and health care professionals working together to reach a decision about treatment/care. In abdominal aortic aneurysm (AAA) treatment options are influenced by patients' clinical characteristics, their preferences, and potential trade-offs between alternative interventions. This is a prime example of where SDM is essential to ensure the right decision is made for the right patient, yet we have little understanding of what happens in practice. This study explored patient experiences to understand SDM practice in AAA surgery. We used a qualitative approach to describe, and identify improvements to, current treatment decision making in abdominal aortic aneurysm (AAA) surgery. Two groups of patients were interviewed: those at the point of discussing treatment options (with corresponding digitally recorded consultation data) and following surgical intervention from one hospital. Framework analysis was used. Fifteen patients were interviewed, seven at the point of discussing treatment options and eight following surgical intervention. Timing, format and sources of information, verbal framing of interventions and level of patient engagement were key themes. Four areas for improvement were identified: earlier provision and more detailed written information along with signposting to quality on-line information; both intervention options, risks, benefits, and consequences, were not always discussed; some clinicians were somewhat directive in the decision-making process; and patients' treatment values/preferences were not explored-the only example was in one of the eight recorded consultations. Patients could feel overwhelmed by the information and decision and fearful of the impending surgery. More emphasis should be placed on the provision of full information and the exploration of patient values and preferences for treatment. Clinician training and support for patients, including decision aids, could facilitate the decision-making process. Providing written information earlier and guidance on reliable on-line resources would benefits patients and their families.

A Digital Twin-based on-site quality assessment method for aero-engine assembly

The assembly of aero-engine is characterized and challenged by complex components, multiple operating processes and multi-physical environment. Previous analysis techniques are mainly focused on the assembly designing stage while the variables in operating stage are rarely considered, which is against to individual accurate modeling faced with differential deviation distribution, back and forth assembly processing and various combination status. Therefore, an aero-engine assembly quality assessment method based on cumulative block information modeling and process-oriented assembly twinning is proposed in this paper. An analysis Digital Twin platform is established to integrate modules consisted of measurements, digital design models, geometric deviation analysis models, information model and databases to enable quality analysis during the assembly operating stage. Then, a prototype system is developed featured by full assembly cycle information blocks, dynamic modeling method based on Vector Loop and on-site data driven assembly quality assessment method. Finally, the proposed method and system is verified by an on-site aero-engine assembly experiment.

Multiview feature co‐factorization based dictionary selection for video summarization

AbstractRecently, video summarization (VS) has emerged as one of the most effective tools for rapidly understanding video big data. Dictionary selection based on self‐representation and sparse regularization is consistent with the requirement of VS, which aims to represent the original video with little reconstruction error by a small number of video frames. However, one crucial issue is that the existing methods mainly use a single view feature, which is not sufficient enough for acquiring the full pictorial details and affects the quality of the produced video summary. Although a few methods use more than one features, they only directly concatenate the features, which does not take advantage of the relationship of different features. Considering the complementarity of shallow and deep features, multiview feature co‐factorization based dictionary selection for VS is proposed in this paper to use the common information of both view features for VS. Specifically, two view features are used to fully exploit the full pictorial information of video frames, then the common information of two different views is extracted through coupled matrix factorization to conduct the dictionary selection for VS. Experiments have been carried out on two benchmark datasets, and results have demonstrated the effectiveness and superiority of the proposed method.

Resolving prime modules: The structure of pseudo-cographs and galled-tree explainable graphs

The modular decomposition of a graph G is a natural construction to capture key features of G in terms of a labeled tree (T,t) whose vertices are labeled as “series” (1), “parallel” (0) or “prime”. However, full information of G is provided by its modular decomposition tree (T,t) only, if G is a cograph, i.e., G does not contain prime modules. In this case, (T,t) explains G, i.e., {x,y}∈E(G) if and only if the lowest common ancestor lcaT(x,y) of x and y has label “1”. Pseudo-cographs, or, more generally, GaTEx graphs G are graphs that can be explained by labeled galled-trees, i.e., labeled networks (N,t) that are obtained from the modular decomposition tree (T,t) of G by replacing the prime vertices in T by simple labeled cycles. GaTEx graphs can be recognized and labeled galled-trees that explain these graphs can be constructed in linear time.In this contribution, we provide a novel characterization of GaTEx graphs in terms of a set FGT of 25 forbidden induced subgraphs. This characterization, in turn, allows us to show that GATEX graphs are closely related to many other well-known graph classes such as P4-sparse and P4-reducible graphs, weakly-chordal graphs, perfect graphs with perfect order, comparability and permutation graphs, murky graphs as well as interval graphs, Meyniel graphs or very strongly-perfect and brittle graphs. Moreover, we show that every GATEX graph as twin-width at most 1.

A correlated traits correlated (methods - 1) multitrait-multimethod model for augmented round-robin data.

We didactically derive a correlated traits correlated (methods - 1) [CTC(M - 1)] multitrait-multimethod (MTMM) model for dyadic round-robin data augmented by self-reports. The model is an extension of the CTC(M - 1) model for cross-classified data and can handle dependencies between raters and targets by including reciprocity covariance parameters that are inherent in augmented round-robin designs. It can be specified as a traditional structural equation model. We present the variance decomposition as well as consistency and reliability coefficients. Moreover, we explain how to evaluate fit of a CTC(M - 1) model for augmented round-robin data. In a simulation study, we explore the properties of the full information maximum likelihood estimation of the model. Model (mis)fit can be quite accurately detected with the test of not close fit and dynamic root mean square errors of approximation. Even with few small round-robin groups, relative parameter estimation bias and coverage rates are satisfactory, but several larger round-robin groups are needed to minimize relative parameter estimation inaccuracy. Further, neglecting the reciprocity covariance-structure of the augmented round-robin data does not severely bias the remaining parameter estimates. All analyses (including data, R scripts, and results) and the simulation study are provided in the Supporting Information. Implications and limitations are discussed.

Comprehensive mining of information in Weakly Supervised Semantic Segmentation: Saliency semantics and edge semantics

In the studies of Weakly Supervised Semantic Segmentation (WSSS) with image-level labels, there is an issue of incomplete semantic information, which we summarize as insufficient saliency semantic mining and neglected edge semantics. We proposes a two-stage framework, Saliency Semantic Full Mining-Edge Semantic Mining (SSFM-ESM), which views WSSS from the perspective of comprehensive information mining. In the first stage, we rely on SSFM to address the insufficient saliency semantic mining. The network learns feature representations consistent with salient regions via the proposed pixel-level class-agnostic distance loss. Then, the full saliency semantic information is mined by explicitly receiving pixel-level feedback. The initial pseudo-label with complete saliency semantic information can be obtained after the first stage. In the second stage, we focus on the mining of edge semantic information through the proposed edge semantic mining module. Specifically, we guide the initial pseudo-label avoid learning about false semantic information and obtain high-confidence edge semantics. The self-correction ability of the segmentation network is also fully utilized to obtain more edge semantic information. Extensive experiments are conducted on the PASCAL VOC 2012 and MS COCO 2014 datasets to verify the feasibility and superiority of this approach.

Blockchain Tech System: Optimization of the Use and Audit of Village Funds

The central government has moved to provide capital support in the form of village funds to every administrative area in Indonesia. According to ICW (2022), the entire budget allotted for village funding in 2022 will be IDR 68 trillion. According to Indonesia Corruption Watch (ICW) monitoring data from 2015 to 2022, there were 155 instances of village fund corruption, which caused state losses totaling IDR 381,947,508,605 (ICW, 2022). Village monies were deposited in regional accounts as a result of issues with village fund realization reports. By ensuring that village funds have a reporting system that adheres to the value for money standards, village funds will be spent as efficiently as possible. Qualitative descriptive writing is the style used. The blockchain that is being used has a transaction database as well as superior features like smart contracts and full information sharing between peers and outside, which can make it simpler for auditors to examine village money. In addition, this technology provides superior data distribution and security capabilities. Budget realization reports are made immediately by an accounting information system using data from a transaction database. The ideal usage and management of local funds are encouraged by this technology, it is hoped.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). A retrospective cohort of 104 patients (60.3 ± 10.7y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Mental Health During Early Adolescence and Later Cardiometabolic Risk: A Prospective Study of US Latinx Youth

PurposeRising rates of cardiometabolic risk and mental health problems are serious public health concerns for US adolescents, particularly those of Latinx origin. This research examines how Latinx youth's internalizing symptoms during early adolescence are related to sleep problems, overweight/obesity, sedentary behavior, physical activity, healthy diet, and hypertension or diabetes risk during middle and late adolescence. MethodsParticipants included 547 adolescents listed as “Hispanic” on 2017-18 middle school enrollment lists in a suburban Atlanta, GA school district. Survey data collected at baseline (2018) and four years later (2022) were analyzed using Structural Equation Model. Path estimates from baseline internalizing symptoms to later health behaviors and physical health outcomes adjusted for demographics, the follow-up measure of internalizing symptoms, and correlations among outcome variables. Missing data were handled using Full Information Maximum Likelihood. ResultsAt baseline, the 244 (44.6%) male and 303 (55.4%) female participants had a mean (standard deviation) age in years of 13.31 (0.97). Early adolescent internalizing symptoms were associated positively with later sleep problems (ß = 0.36 [95% confidence interval (CI), 0.24–0.48]), overweight/obesity (adjusted odds ratio, 2.57; 95% CI, 1.29–5.15), sedentary behavior (ß = 0.19 [95% CI, 0.09–0.30]), and internalizing symptoms (ß = 0.48 [95% CI, 0.39–0.56]) and inversely with later physical activity (ß = −0.16 [95% CI, −0.27 to −0.05]) and a healthy diet (ß = −0.21 [95% CI, −0.32 to −0.09]). DiscussionLatinx youth's internalizing symptoms during early adolescence not only track into later adolescence, but they also relate to health behaviors and outcomes underlying cardiometabolic risk during middle and late adolescence.

B - 58 Cognitive Emotion Regulation as a Mediator between Childhood Trauma and Parenting Stress.

Determine the relationship between adverse childhood events (ACE), emotion regulation (ER) during the perinatal period, and parenting stress. Pregnant individuals were recruited through an electronic medical record database and word of mouth as part of the COVID-19 and Perinatal Experiences Study to complete online surveys about their experiences during the COVID-19 pandemic. Longitudinal data were collected from 349 participants from pregnancy through 12months postpartum. A structural equation modeling approach was utilized to assess ACEs and ER's direct and indirect effects on parenting stress. A path mediation model was utilized. Models were estimated using Mplus 8.8. Missing data were handled using full information maximum likelihood. Latent factors of cognitive reappraisal (CR) and expressive suppression were independently modeled as the mediators between ACES and parenting stress. The expressive suppression model showed good fit (CFI = 0.982; TLI = 0.966; SRMR = 0.039), and the CR model showed adequate fit (CFI = 0.911; TLI = 0.86; SRMR = 0.047). There was a significant positive effect of ACEs on expressive suppression (β= 0.104, 95% CI [0.048, 0.159], p = 0.002) and expressive suppression on parenting stress (β= 0.098, 95% CI [0.032, 0.164], p = 0.015). We found a significant negative effect of ACEs on CR (β= -0.061, 95% CI [-0.110, -0.013], p = 0.039) (Fig. 1). and of CR on parenting stress (β= -0.101, 95% CI [-0.167, -0.013], p = 0.013) (Fig. 2). This analysis supports the hypothesis that experiencing adverse events during childhood can impact parenting stress through a change in both adaptive and maladaptive cognitive emotion regulation strategies.