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Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.
Methods
We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).
Results
Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.
Conclusions
Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.
Traditional approaches for evaluating the impact of scientific research – mainly scholarship (i.e., publications, presentations) and grant funding – fail to capture the full extent of contributions that come from larger scientific initiatives. The Translational Science Benefits Model (TSBM) was developed to support more comprehensive evaluations of scientific endeavors, especially research designed to translate scientific discoveries into innovations in clinical or public health practice and policy-level changes. Here, we present the domains of the TSBM, including how it was expanded by researchers within the Implementation Science Centers in Cancer Control (ISC3) program supported by the National Cancer Institute. Next, we describe five studies supported by the Penn ISC3, each focused on testing implementation strategies informed by behavioral economics to reduce key practice gaps in the context of cancer care and identify how each study yields broader impacts consistent with TSBM domains. These indicators include Capacity Building, Methods Development (within the Implementation Field) and Rapid Cycle Approaches, implementing Software Technologies, and improving Health Care Delivery and Health Care Accessibility. The examples highlighted here can help guide other similar scientific initiatives to conceive and measure broader scientific impact to fully articulate the translation and effects of their work at the population level.
Accurately quantifying all the components of the surface energy balance (SEB) is a prerequisite for the reliable estimation of surface melt and the surface mass balance over ice and snow. This study quantifies the SEB closure by comparing the energy available for surface melt, determined from continuous measurements of radiative fluxes and turbulent heat fluxes, to the surface ablation measured on the Greenland ice sheet between 2003 and 2023. We find that the measured daily energy available for surface melt exceeds the observed surface melt by on average 18 ± 30 W m−2 for snow and 12 ± 54 W m−2 for ice conditions (mean ± SD), which corresponds to 46 and 10% of the average energy available for surface melt, respectively. When the surface is not melting, the daily SEB is on average closed within 5 W m−2. Based on the inter-comparison of different ablation sensors and radiometers installed on different stations, and on the evaluation of modelled turbulent heat fluxes, we conclude that measurement uncertainties prevent a better daily to sub-daily SEB closure. These results highlight the need and challenges in obtaining accurate long-term in situ SEB observations for the proper evaluation of climate models and for the validation of remote sensing products.
A synthetic octahedral-site-vacancy-free annite sample and its progressive oxidation, induced by heating in air, were studied by powder X-ray diffraction (pXRD), Mössbauer spectroscopy, nuclear reaction analysis (NRA), Raman spectroscopy, X-ray fluorescence (XRF) spectroscopy, gas chromatography (GC), thermogravimetric analysis (TGA), differential thermal analysis (DTA), scanning electron microscopy (SEM), and size-fraction separation methods. For a set heating time and as temperature is increased, the sample first evolves along an annite-oxyannite join, until all H is lost via the oxybiotite reaction (Fe2+ + OH− ⇌ Fe3+ + O2− + H↑). It then evolves along an oxyannite-ferrioxyannite join, where ideal ferrioxyannite, KFe3+8/3□1/3AlSi3O12, is defined as the product resulting from complete oxidation of ideal oxyannite, KFe3+2Fe2+AlSi3O12, via the vacancy mechanism (3 Fe2+ ⇌ 2 Fe3+ + [6]□ + Fe↑). A pillaring collapse transition is observed as a collapse of c near the point where and all OH groups are predicted and observed to be lost. Quantitative analyses of H, using NRA, GC, and Raman spectroscopy, corroborate this interpretation and, in combination with accurate ferric/ferrous ratios from Mössbauer spectroscopy and lattice parameter determinations, allow a clear distinction to be made between vacancy-free and vacancy-bearing annite. The amount of Fe in ancillary Fe oxide phases produced by the vacancy mechanism is measured by Mössbauer spectroscopy to be 11.3(5)% of total Fe, in agreement with both the theoretical prediction of 1/9 = 11.1% and the observed TGA weight gain. The initiation of Fe oxide formation near the point of completion of the oxybiotite reaction () is corroborated by pXRD, TGA, Raman spectroscopy, and appearance of an Fe oxide hyperfine field sextet in the Mössbauer spectra. The region of Fe oxide formation is shown to coincide with a region of octahedral site vacancy formation, using a new Mössbauer spectral signature of vacancies that consists of a component at 2.2 mm/s in the [6]Fe3+ quadrupole splitting distribution (QSD). The crystal chemical behaviors of annite-oxyannite and of oxyannite-ferrioxyannite are best contrasted and compared to the behaviors of other layer-silicate series in terms of b vs. [D] (average octahedral cation to O bond length). This also leads to a diagnostic test for the presence of octahedral site vacancies in hydrothermally synthesized annite, based on a graph of b vs. Fe2+/Fe. The implications of the observed sequence of thermal oxidation reactions for the thermodynamic relevance of the oxybiotite and vacancy reactions in hydrothermal syntheses are examined and it is concluded that the oxybiotite reaction is the relevant reaction in the single-phase stability field of annite, at high hydrogen fugacity and using ideal starting cation stoichiometry. The vacancy reaction is only relevant in a multi-phase field, at lower hydrogen fugacity, that includes an Fe oxide equilibrium phase (magnetite) that can effectively compete for Fe, or when using non-ideal starting cation stoichiometries.
Children with CHD or born very preterm are at risk for brain dysmaturation and poor neurodevelopmental outcomes. Yet, studies have primarily investigated neurodevelopmental outcomes of these groups separately.
Objective:
To compare neurodevelopmental outcomes and parent behaviour ratings of children born term with CHD to children born very preterm.
Methods:
A clinical research sample of 181 children (CHD [n = 81]; very preterm [≤32 weeks; n = 100]) was assessed at 18 months.
Results:
Children with CHD and born very preterm did not differ on Bayley-III cognitive, language, or motor composite scores, or on expressive or receptive language, or on fine motor scaled scores. Children with CHD had lower ross motor scaled scores compared to children born very preterm (p = 0.047). More children with CHD had impaired scores (<70 SS) on language composite (17%), expressive language (16%), and gross motor (14%) indices compared to children born very preterm (6%; 7%; 3%; ps < 0.05). No group differences were found on behaviours rated by parents on the Child Behaviour Checklist (1.5–5 years) or the proportion of children with scores above the clinical cutoff. English as a first language was associated with higher cognitive (p = 0.004) and language composite scores (p < 0.001). Lower median household income and English as a second language were associated with higher total behaviour problems (ps < 0.05).
Conclusions:
Children with CHD were more likely to display language and motor impairment compared to children born very preterm at 18 months. Outcomes were associated with language spoken in the home and household income.
White matter hyperintensity (WMH) burden is greater, has a frontal-temporal distribution, and is associated with proxies of exposure to repetitive head impacts (RHI) in former American football players. These findings suggest that in the context of RHI, WMH might have unique etiologies that extend beyond those of vascular risk factors and normal aging processes. The objective of this study was to evaluate the correlates of WMH in former elite American football players. We examined markers of amyloid, tau, neurodegeneration, inflammation, axonal injury, and vascular health and their relationships to WMH. A group of age-matched asymptomatic men without a history of RHI was included to determine the specificity of the relationships observed in the former football players.
Participants and Methods:
240 male participants aged 45-74 (60 unexposed asymptomatic men, 60 male former college football players, 120 male former professional football players) underwent semi-structured clinical interviews, magnetic resonance imaging (structural T1, T2 FLAIR, and diffusion tensor imaging), and lumbar puncture to collect cerebrospinal fluid (CSF) biomarkers as part of the DIAGNOSE CTE Research Project. Total WMH lesion volumes (TLV) were estimated using the Lesion Prediction Algorithm from the Lesion Segmentation Toolbox. Structural equation modeling, using Full-Information Maximum Likelihood (FIML) to account for missing values, examined the associations between log-TLV and the following variables: total cortical thickness, whole-brain average fractional anisotropy (FA), CSF amyloid ß42, CSF p-tau181, CSF sTREM2 (a marker of microglial activation), CSF neurofilament light (NfL), and the modified Framingham stroke risk profile (rFSRP). Covariates included age, race, education, APOE z4 carrier status, and evaluation site. Bootstrapped 95% confidence intervals assessed statistical significance. Models were performed separately for football players (college and professional players pooled; n=180) and the unexposed men (n=60). Due to differences in sample size, estimates were compared and were considered different if the percent change in the estimates exceeded 10%.
Results:
In the former football players (mean age=57.2, 34% Black, 29% APOE e4 carrier), reduced cortical thickness (B=-0.25, 95% CI [0.45, -0.08]), lower average FA (B=-0.27, 95% CI [-0.41, -.12]), higher p-tau181 (B=0.17, 95% CI [0.02, 0.43]), and higher rFSRP score (B=0.27, 95% CI [0.08, 0.42]) were associated with greater log-TLV. Compared to the unexposed men, substantial differences in estimates were observed for rFSRP (Bcontrol=0.02, Bfootball=0.27, 994% difference), average FA (Bcontrol=-0.03, Bfootball=-0.27, 802% difference), and p-tau181 (Bcontrol=-0.31, Bfootball=0.17, -155% difference). In the former football players, rFSRP showed a stronger positive association and average FA showed a stronger negative association with WMH compared to unexposed men. The effect of WMH on cortical thickness was similar between the two groups (Bcontrol=-0.27, Bfootball=-0.25, 7% difference).
Conclusions:
These results suggest that the risk factor and biological correlates of WMH differ between former American football players and asymptomatic individuals unexposed to RHI. In addition to vascular risk factors, white matter integrity on DTI showed a stronger relationship with WMH burden in the former football players. FLAIR WMH serves as a promising measure to further investigate the late multifactorial pathologies of RHI.
The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.
Methods
In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.
Results
Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.
Conclusions
Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.
Over the past 2 decades, several categorizations have been proposed for the abnormalities of the aortic root. These schemes have mostly been devoid of input from specialists of congenital cardiac disease. The aim of this review is to provide a classification, from the perspective of these specialists, based on an understanding of normal and abnormal morphogenesis and anatomy, with emphasis placed on the features of clinical and surgical relevance. We contend that the description of the congenitally malformed aortic root is simplified when approached in a fashion that recognizes the normal root to be made up of 3 leaflets, supported by their own sinuses, with the sinuses themselves separated by the interleaflet triangles. The malformed root, usually found in the setting of 3 sinuses, can also be found with 2 sinuses, and very rarely with 4 sinuses. This permits description of trisinuate, bisinuate, and quadrisinuate variants, respectively. This feature then provides the basis for classification of the anatomical and functional number of leaflets present. By offering standardized terms and definitions, we submit that our classification will be suitable for those working in all cardiac specialties, whether pediatric or adult. It is of equal value in the settings of acquired or congenital cardiac disease. Our recommendations will serve to amend and/or add to the existing International Paediatric and Congenital Cardiac Code, along with the Eleventh iteration of the International Classification of Diseases provided by the World Health Organization.
Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
Methods
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Results
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
Conclusions
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Remotely sensed land surface temperature (LST) data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) LST thermal infrared products, are useful for monitoring surface processes on the Greenland Ice Sheet in remote areas but must be validated to ensure accuracy. Using data from the Programme for Monitoring the Greenland Ice Sheet (PROMICE), we conducted a MODIS LST validation (MOD/MYD11 C6 swath level product) using radiometric in-situ skin temperature records from 2014 to 2017 over 17 PROMICE sites mostly in the ice sheet's ablation zone. There is a significant cold bias in MODIS LST when compared to PROMICE skin temperature, particularly when PROMICE records temperatures below 0°C (mean bias: 2.4 ± 0.01°C mean ± standard error, RMSE = 3.2°C). Multiple linear regression analysis reveals the difference between MODIS LST and PROMICE skin temperature is larger at lower temperatures, lower latent heat fluxes and higher specific humidity. Our results confirm the presence of a progressive cold bias in the MODIS LST that should be considered in use of this product, and we identify and corroborate areas for ongoing algorithm development.
The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking.
Methods
Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs.
Results
The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs.
Conclusions
It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.
Effective stakeholder interviewing is a critical component of a design process. However, interviewing is a complex skill that is difficult for novice designers to learn and incorporate into their design practices. Few studies have investigated how novice designers apply recommended practices for interviewing stakeholders during the development of product requirements. In this research, we studied how novice designers elicited information to inform the development of product requirements during stakeholder interviews. Results included the establishment of a coding methodology developed from a systematic literature review of recommended interviewing practices that was used to reliably evaluate the use of recommended practices in novice designers’ interviews. A correlation existed between the use of recommended practices and the extent to which information gathered from interviews was incorporated into the requirements. Additionally, specific recommended practices, such as encouraging deep thinking and being flexible and opportunistic, differentiated performance among novice designers. The coding methodology could be adapted to guide the development of stakeholder interview protocols and assessment of design interview skills.
This chapter aims to synthesize key findings from the SURE-Farm project. We first discuss possible amendments to the framework to assess the resilience of farming systems. We then review why many of Europe’s farming systems face a formidable and structural resilience crisis. While emphasizing the diversity of resilience capacities, challenges and needs, we formulate cornerstones for possible resilience-enhancing strategies. The chapter concludes with critical reflections and suggestions for resilience-enhancing strategies that comprise the levels of farms, farming systems and enabling environments. We identify limitations of the research and suggest avenues for future research on the resilience of farming systems.
We honour a great man and a true giant. Lodewyk H.S. van Mierop (March 31, 1927 – October 17, 2021), known as Bob, was not only a Paediatric Cardiologist but also a dedicated Scientist. He made many significant and ground-breaking contributions to the fields of cardiac anatomy and embryology. He was devoted as a teacher, spending many hours with medical students, Residents, and Fellows, all of whom appreciated his regularly scheduled educational sessions. Those of us who were fortunate to know and spend time with him will always remember his great mind, his willingness to share his knowledge, and his ability to encourage spirited and fruitful discussions. His life was most productive, and he will long be remembered by many through his awesome and exemplary scientific contributions.
His legacy continues to influence the current and future generations of surgeons and all providers of paediatric and congenital cardiac care through the invaluable archive he established at University of Florida in Gainesville: The University of Florida van Mierop Heart Archive. Undoubtedly, with these extraordinary contributions to the fields of cardiac anatomy and embryology, which were way ahead of his time, Professor van Mierop was a true giant in Paediatric Cardiology. The invaluable archive he established at University of Florida in Gainesville, The University of Florida van Mierop Heart Archive, has been instrumental in teaching medical students, Residents, Medical Fellows, and Surgical Fellows. Only a handful of similar archives exist across the globe, and these archives are the true legacy of giants such as Dr. van Mierop. We have an important obligation to leave no stone unturned to continue to preserve these archives for the future generations of surgeons, physicians, all providers of paediatric and congenital cardiac care, and, most importantly, our patients.
In the December 2021 issue of Cardiology in the Young, Hubrechts and colleagues, from Brussels and Leuven in Belgium, describe their experience in which the pulmonary veins were normally connected to the morphologically left atrium. By virtue of the presence of a shelf dividing the morphologically left atrium, however, the venous return was to the morphologically right atrium, with no evidence of formation of the superior interatrial fold, meaning that there was no obstruction of flow into the systemic venous circulation. The question posed by the Belgian authors is whether the shelf dividing the morphologically left atrium is a deviated primary atrial septum, as the arrangement has previously been interpreted. As they discuss, it is currently impossible to arbitrate this conundrum. In our commentary, we discuss the background to the dilemma. We point out that, as yet, it is not possible to code accurately this congenital cardiac malformation within The International Paediatric and Congenital Cardiac Code (IPCCC), nor within the newly produced 11th Revision of the International Classification of Diseases (ICD-11).
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
Aims
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
Method
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
Results
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Conclusions
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.