Decoding the complete organelle genomic architecture of Stewartia gemmata: an early-diverging species in Theaceae.

BACKGROUND: Theaceae, comprising 300 + species, holds significance in biodiversity, economics, and culture, notably including the globally consumed tea plant. Stewartia gemmata, a species of the earliest diverging tribe Stewartieae, is critical to offer insights into Theaceae's origin and evolutionary history. RESULT: We sequenced the complete organelle genomes of Stewartia gemmata using short/long reads sequencing technologies. The chloroplast genome (158,406 bp) exhibited a quadripartite structure including the large single-copy region (LSC), a small single-copy region (SSC), and a pair of inverted repeat regions (IRs); 114 genes encoded 80 proteins, 30 tRNAs, and four rRNAs. The mitochondrial genome (681,203 bp) exhibited alternative conformations alongside a monocyclic structure: 61 genes encoding 38 proteins, 20 tRNAs, three rRNAs, and RNA editing-impacting genes, including ATP6, RPL16, COX2, NAD4L, NAD5, NAD7, and RPS1. Comparative analyses revealed frequent recombination events and apparent rRNA gene gains and losses in the mitochondrial genome of Theaceae. In organelle genomes, the protein-coding genes exhibited a strong A/U bias at codon endings; ENC-GC3 analysis implies selection-driven codon bias. Transposable elements might facilitate interorganelle sequence transfer. Phylogenetic analysis confirmed Stewartieae's early divergence within Theaceae, shedding light on organelle genome characteristics and evolution in Theaceae. CONCLUSIONS: We studied the detailed characterization of organelle genomes, including genome structure, composition, and repeated sequences, along with the identification of lateral gene transfer (LGT) events and complexities. The discovery of a large number of repetitive sequences and simple sequence repeats (SSRs) has led to new insights into molecular phylogenetic markers. Decoding the Stewartia gemmata organellar genome provides valuable genomic resources for further studies in tea plant phylogenomics and evolutionary biology.

Detecting influential subjects in intensive longitudinal data using mixed-effects location scale models.

BACKGROUND: Collection of intensive longitudinal health outcomes allows joint modeling of their mean (location) and variability (scale). Focusing on the location of the outcome, measures to detect influential subjects in longitudinal data using standard mixed-effects regression models (MRMs) have been widely discussed. However, no existing approach enables the detection of subjects that heavily influence the scale of the outcome. METHODS: We propose applying mixed-effects location scale (MELS) modeling combined with commonly used influence measures such as Cook's distance and DFBETAS to fill this gap. In this paper, we provide a framework for researchers to follow when trying to detect influential subjects for both the scale and location of the outcome. The framework allows detailed examination of each subject's influence on model fit as well as point estimates and precision of coefficients in different components of a MELS model. RESULTS: We simulated two common scenarios in longitudinal healthcare studies and found that influence measures in our framework successfully capture influential subjects over 99% of the time. We also re-analyzed data from a health behavior study and found 4 particularly influential subjects, among which two cannot be detected by influence analyses via regular MRMs. CONCLUSION: The proposed framework can help researchers detect influential subject(s) that will be otherwise overlooked by influential analysis using regular MRMs and analyze all data in one model despite influential subjects.

Defining R-squared measures for mixed-effects location scale models.

Ecological momentary assessment and other modern data collection technologies facilitate research on both within-subject and between-subject variability of health outcomes and behaviors. For such intensively measured longitudinal data, Hedeker et al extended the usual two-level mixed-effects model to a two-level mixed-effects location scale (MELS) model to accommodate covariates' influence as well as random subject effects on both mean (location) and variability (scale) of the outcome. However, there is a lack of existing standardized effect size measures for the MELS model. To fill this gap, our study extends Rights and Sterba's framework of R 2 $$ {R}^2 $$ measures for multilevel models, which is based on model-implied variances, to MELS models. Our proposed framework applies to two different specifications of the random location effects, namely, through covariate-influenced random intercepts and through random intercepts combined with random slopes of observation-level covariates. We also provide an R function, R2MELS, that outputs summary tables and visualization for values of our R 2 $$ {R}^2 $$ measures. This framework is validated through a simulation study, and data from a health behaviors study and a depression study are used as examples to demonstrate this framework. These R 2 $$ {R}^2 $$ measures can help researchers provide greater interpretation of their findings using MELS models.

Reliability of additional reported seizure manifestations to identify dissociative seizures.

PURPOSE: Descriptions of seizure manifestations (SM), or semiology, can help localize the symptomatogenic zone and subsequently included brain regions involved in epileptic seizures, as well as identify patients with dissociative seizures (DS). Patients and witnesses are not trained observers, so these descriptions may vary from expert review of seizure video recordings of seizures. To better understand how reported factors can help identify patients with DS or epileptic seizures (ES), we evaluated the associations between more than 30 SMs and diagnosis using standardized interviews. METHODS: Based on patient- and observer-reported data from 490 patients with diagnoses documented by video-electoencephalography, we compared the rate of each SM in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic seizure-like events (PSLE), mixed DS and ES, and inconclusive testing. RESULTS: In addition to SMs that we described in a prior manuscript, the following were associated with DS: light triggers, emotional stress trigger, pre-ictal and post-ictal headache, post-ictal muscle soreness, and ictal sensory symptoms. The following were associated with ES: triggered by missing medication, aura of déjà vu, and leftward eye deviation. There were numerous manifestations separately associated with mixed ES and DS. CONCLUSIONS: Reported SM can help identify patients with DS, but no manifestation is pathognomonic for either ES or DS. Patients with mixed ES and DS reported factors divergent from both ES-alone and DS-alone.

Objective score from initial interview identifies patients with probable dissociative seizures.

OBJECTIVE: To develop a Dissociative Seizures Likelihood Score (DSLS), which is a comprehensive, evidence-based tool using information available during the first outpatient visit to identify patients with "probable" dissociative seizures (DS) to allow early triage to more extensive diagnostic assessment. METHODS: Based on data from 1616 patients with video-electroencephalography (vEEG) confirmed diagnoses, we compared the clinical history from a single neurology interview of patients in five mutually exclusive groups: epileptic seizures (ES), DS, physiologic nonepileptic seizure-like events (PSLE), mixed DS plus ES, and inconclusive monitoring. We used data-driven methods to determine the diagnostic utility of 76 features from retrospective chart review and applied this model to prospective interviews. RESULTS: The DSLS using recursive feature elimination (RFE) correctly identified 77% (95% confidence interval (CI), 74-80%) of prospective patients with either ES or DS, with a sensitivity of 74% and specificity of 84%. This accuracy was not significantly inferior than neurologists' impression (84%, 95% CI: 80-88%) and the kappa between neurologists' and the DSLS was 21% (95% CI: 1-41%). Only 3% of patients with DS were missed by both the fellows and our score (95% CI 0-11%). SIGNIFICANCE: The evidence-based DSLS establishes one method to reliably identify some patients with probable DS using clinical history. The DSLS supports and does not replace clinical decision making. While not all patients with DS can be identified by clinical history alone, these methods combined with clinical judgement could be used to identify patients who warrant further diagnostic assessment at a comprehensive epilepsy center.

Identifying Witnessed Suicides in National Violent Death Reporting System Narratives.

There is increasing attention to suicides that occur in view of others, as these deaths can cause significant psychological impact on witnesses. This study illuminates characteristics of witnessed suicides and compares characteristics of these deaths to non-witnessed suicides. We develop a codable definition of what constitutes witnessed (vs. non-witnessed) suicide. Our data include a sample of 1200 suicide descriptions from the 2003-2017 National Violent Death Reporting System (NVDRS). We first developed criteria to identify probable cases of witnessed suicide. The coding scheme achieved 94.5% agreement and identified approximately 10% (n = 125) of suicides as witnessed. Next, we examined differences between witnessed and non-witnessed suicides in demographics, manner of death, and social/environmental factors using bivariate Chi-squared tests, multivariate logistic regression, and ANOVA. Witnessed suicide decedents were significantly more likely than non-witnessed suicide decedents to be male, younger, and members of a sexual minority, and to have died in living spaces by means of a firearm. Two thirds of witnesses were strangers to the decedents, while 23.2% were romantic partners or ex-partners of the decedents. Our coding method offers a reliable approach to identify witnessed suicides. While witnessed suicides are relatively infrequent, these deaths have profound impact on witnesses. Articulating the features of witnessed suicides may contribute to identifying potential risk mitigation strategies.

Social Vulnerability and Risk of Suicide in US Adults, 2016-2020.

Importance: There were over 45 000 suicides in the US in 2020, making suicide the 12th leading cause of death. If social vulnerability is associated with suicide rates, targeted interventions for at-risk segments of the population may reduce US suicide rates. Objective: To determine the association between social vulnerability and suicide in adults. Design, Setting, and Participants: This cohort study analyzed 2 county-level social vulnerability measures (the Social Vulnerability Index [SVI] and the Social Vulnerability Metric [SVM]) and US Centers for Disease Control and Prevention-reported county-level suicides from 2016 to 2020. Data were analyzed November and December 2022. Exposures: County-level variability in social vulnerability. Main Outcomes and Measures: The primary outcome measure was number of county-level adult suicides from 2016 to 2020, offset by county adult population during those years. The association between social vulnerability (measured using the SVI and the newly created SVM for 2018) and suicide was modeled using a bayesian-censored Poisson regression model to account for the CDC's suppression of county-level suicide counts of less than 10, adjusted for age, racial and ethnic minority, and urban-rural county characteristics. Results: From 2016 to 2020, there were a total of 222 018 suicides in 3141 counties. Comparing the least socially vulnerable (0% to 10%) to the most socially vulnerable (90% to 100%) counties, there was a 56% increase in suicide rate (17.3 per 100 000 persons to 27.0 per 100 000 persons) as measured by the SVI (incidence rate ratio, 1.56; 95% credible interval, 1.51-1.60) and an 82% increase in suicide rate (13.8 per 100 000 persons to 25.1 per 100 000 persons) as measured by the SVM (incidence rate ratio, 1.82; 95% credible interval, 1.72-1.92). Conclusions and Relevance: This cohort study found that social vulnerability had a direct association with risk for adult suicide. Reducing social vulnerability may lead to life-saving reduction in the rate of suicide.

Self-Reported Severity and Causes of Traumatic Brain Injury in Patients With Epileptic or Functional Seizures.

Background and Objectives: Although moderate and severe traumatic brain injury (TBI) can cause posttraumatic epilepsy (PTE), many patients with functional seizures (FS) also report a history of mild TBI. To determine whether features of TBI history differ between patients with epileptic seizures (ES) and FS, we compared patient reports of TBI severity, symptoms, and causes of injury. Methods: We recruited patients undergoing video-EEG evaluation for the diagnosis of ES, FS, mixed ES and FS, or physiologic seizure-like events at an academic, tertiary referral center. Patients and their caregivers were interviewed before final video-EEG diagnosis regarding their TBI histories, including concussive symptoms and causes of injury. Results: Of 506 patients, a greater percentage of patients with FS reported a history of TBI than patients with ES (70% vs 59%, aOR = 1.75 [95% CI: 1.00-3.05], p = 0.047). TBI with loss of consciousness (LOC) lasting less than 30 minutes was more frequently reported among patients with FS than with ES (27% vs 13%, aOR = 2.38 [1.26-4.47], p < 0.01). The proportion of patients reporting other neurologic symptoms immediately after TBI was not significantly different between FS and ES (40% vs 29%, p = 0.08). Causes of TBI were found to differ, with TBIs caused by falls from a height (17% vs 10%, aOR = 2.24 [1.06-4.70], p = 0.03) or motor vehicle collisions (27% vs 11%, aOR = 2.96 [1.54-5.67], p < 0.01) reported more frequently in FS than ES. Discussion: Our findings further the association of mild TBI with FS and prompt reconsideration of typical assumptions regarding the significance of a reported TBI history in patients with previously undifferentiated seizures. Although common in both groups, TBI with LOC less than 30 minutes and causes of injury that are commonly believed to be more severe were reported more frequently in FS than ES. This suggests that a patient or caregiver reporting of these features does not imply that PTE is a more probable diagnosis than FS. Although a history of TBI with LOC and presumed high-risk causes of injury intuitively raises suspicion for PTE, clinicians should be cautioned that these historical factors also were a frequent finding in patients with FS.

Factors associated with delay to video-EEG in dissociative seizures.

PURPOSE: While certain clinical factors suggest a diagnosis of dissociative seizures (DS), otherwise known as functional or psychogenic nonepileptic seizures (PNES), ictal video-electroencephalography monitoring (VEM) is the gold standard for diagnosis. Diagnostic delays were associated with worse quality of life and more seizures, even after treatment. To understand why diagnoses were delayed, we evaluated which factors were associated with delay to VEM. METHODS: Using data from 341 consecutive patients with VEM-documented dissociative seizures, we used multivariate log-normal regression with recursive feature elimination (RFE) and multiple imputation of some missing data to evaluate which of 76 clinical factors were associated with time from first dissociative seizure to VEM. RESULTS: The mean delay to VEM was 8.4 years (median 3 years, IQR 1-10 years). In the RFE multivariate model, the factors associated with longer delay to VEM included more past antiseizure medications (0.19 log-years/medication, standard error (SE) 0.05), more medications for other medical conditions (0.06 log-years/medication, SE 0.03), history of physical abuse (0.75 log-years, SE 0.27), and more seizure types (0.36 log-years/type, SE 0.11). Factors associated with shorter delay included active employment or student status (-1.05 log-years, SE 0.21) and higher seizure frequency (0.14 log-years/log[seizure/month], SE 0.06). CONCLUSIONS: Patients with greater medical and seizure complexity had longer delays. Delays in multiple domains of healthcare can be common for victims of physical abuse. Unemployed and non-student patients may have had more barriers to access VEM. These results support earlier referral of complex cases to a comprehensive epilepsy center.

Epilepsy, dissociative seizures, and mixed: Associations with time to video-EEG.

PURPOSE: Video-electroencephalographic monitoring (VEM) is a core component to the diagnosis and evaluation of epilepsy and dissociative seizures (DS)-also known as functional or psychogenic seizures-but VEM evaluation often occurs later than recommended. To understand why delays occur, we compared how patient-reported clinical factors were associated with time from first seizure to VEM (TVEM) in patients with epilepsy, DS or mixed. METHODS: We acquired data from 1245 consecutive patients with epilepsy, VEM-documented DS or mixed epilepsy and DS. We used multivariate log-normal regression with recursive feature elimination (RFE) to evaluate which of 76 clinical factors interacting with patients' diagnoses were associated with TVEM. RESULTS: The mean and median TVEM were 14.6 years and 10 years, respectively (IQR 3-23 years). In the multivariate RFE model, the factors associated with longer TVEM in all patients included unemployment and not student status, more antiseizure medications (current and past), concussion, and ictal behavior suggestive of temporal lobe epilepsy. Average TVEM was shorter for DS than epilepsy, particularly for patients with depression, anxiety, migraines, and eye closure. Average TVEM was longer specifically for patients with DS taking more medications, more seizure types, non-metastatic cancer, and with other psychiatric comorbidities. CONCLUSIONS: In all patients with seizures, trials of numerous antiseizure medications, unemployment and non-student status was associated with longer TVEM. These associations highlight a disconnect between International League Against Epilepsy practice parameters and observed referral patterns in epilepsy. In patients with dissociative seizures, some but not all factors classically associated with DS reduced TVEM.

We are all in this together: The role of individuals' social identities in problematic engagement with video games and the internet.

Individuals' engagement with video games and the internet features both social and potentially pathological aspects. In this research, we draw on the social identity approach and present a novel framework to understand the linkage between these two aspects. In three samples (Nstudy1  = 304, Nstudy2  = 160, and Nstudy3  = 782) of young Chinese people from two age groups (approximately 20 and 16 years old), we test the associations between relevant social identities and problematic engagement with video games and the internet. Across studies, we demonstrate that individuals' identification as 'gamers' or 'frequent internet users' predicts problematic engagement with video games and the internet through stronger perceived social support from such groups. Moreover, we demonstrate that individuals' identification as 'students' (Studies 2-3) is negatively associated with problematic engagement via social support from other students. Finally, in Study 3, we examine the articulation between social support from these three groups and subjective sense of loneliness. Findings indicate that, whereas perceived support from students is negatively associated with loneliness, the association between perceived support from gamers and internet users and loneliness is weaker and positive. Theoretical implications and directions for future research are discussed. Taken together, the studies highlight the importance of considering the social context of individuals' problematic engagement with technologies, and the role of different group memberships.