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Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
To meet the development needs of aeroengines for high thrust-to-weight ratios and fuel-air ratios, a high temperature rise triple-swirler main combustor was designed with a total fuel-air ratio of 0.037, utilising advanced technologies including staged combustion, multi-point injection and multi-inclined hole cooling. Fluent software was used to conduct numerical simulations under both takeoff and idle conditions, thereby obtaining the distribution characteristics of the velocity and temperature fields within the combustor, as well as the generation of pollutants. The simulation results indicate that under takeoff conditions, the high temperature rise triple-swirler combustor achieves a total pressure loss coefficient of less than 6% and a combustion efficiency exceeding 99%. Under takeoff conditions, the OTDF and RTDF values are 0.144 and 0.0738, respectively. The mole fraction of NOx emissions is 3,700ppm, while the mole fraction of soot emissions is 2.55×10−5ppm. Under idle conditions, the triple-swirler combustor maintains a total pressure loss coefficient of less than 6% and a combustion efficiency greater than 99.9%. The OTDF and RTDF values are 0.131 and 0.0624, respectively. The mole fractions of CO and UHC emissions are both 0×10−32ppm at the calculation limit of Fluent software.
Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.
Methods
We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.
Results
The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).
Conclusion
The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.
With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.
Cruznema velatum isolated from soil in a chestnut orchard located at Guangdong province, China, is redescribed with morphology, molecular barcoding sequences, and transcriptome data. The morphological comparison for C. velatum and six other valid species is provided. Phylogeny analysis suggests genus Cruznema is monophyletic. The species is amphimix, can be cultured with Escherichia coli in 7–9 days from egg to egg-laying adult, and has a lifespan of 11 to 14 days at 20°C. The transcription data generated 45,366 unigenes; 29.9%, 31.3%, 24.8%, and 18.6% of unigenes were annotated in KOG, SwissProt, GO, and KEGG, respectively. Further gene function analysis demonstrated that C. velatum share the same riboflavin, lipoic acid, and vitamin B6 metabolic pathways with Caenorhabditis elegans and Pristionchus pacificus.
Vortex shedding in the wake of a cylinder in uniform flow can be suppressed via the application of a porous coating; however, the suppression mechanism is not fully understood. The internal flow field of a porous coated cylinder (PCC) can provide a deeper understanding of how the flow within the porous medium affects the wake development. A structured PCC (SPCC) was three-dimensionally printed using a transparent material and tested in water tunnel facilities using flow visualisation and tomographic particle image velocimetry at outer-diameter Reynolds numbers of $Re = 7 \times 10^{3}$ and $7.3 \times 10^{4}$, respectively. The internal and near-wall flow fields are analysed at the windward and mid-circumference regions. Flow stagnation is observed in the porous layer on the windward side and its boundary is shown to fluctuate with time in the outermost porous layer. This stagnation region generates a quasi-aerodynamic body that influences boundary layer development on the SPCC inner diameter, that separates into a shear layer within the porous medium. For the first time via experiment, spectral content within the separated shear layer reveals vortex shedding processes emanating through single pores at the outer diameter, providing strong evidence that SPCC vortex shedding originates from the inner diameter. Velocity fluctuations linked to this vortex shedding propagate through the porous layers into the external flow field at a velocity less than that of the free stream. The Strouhal number linked to this velocity accurately predicts the SPCC vortex shedding frequency.
Parenting can protect against the development of, or increase risk for, child psychopathology; however, it is unclear if parenting is related to psychopathology symptoms in a specific domain, or to broad liability for psychopathology. Parenting differs between and within families, and both overall family-level parenting and the child-specific parenting a child receives may be important in estimating transdiagnostic associations with psychopathology. Data come from a cross-sectional epidemiological sample (N = 10,605 children ages 4–17, 6434 households). Parents rated child internalizing and externalizing symptoms and their parenting toward each child. General and specific (internalizing, externalizing) psychopathology factors, derived with bifactor modeling, were regressed on parenting using multilevel modeling. Less warmth and more aversive/inconsistent parenting in the family, and toward an individual child relative to family average, were associated with higher general psychopathology and specific externalizing problems. Unexpectedly, more warmth in the family, and toward an individual child relative to family average, was associated with higher specific internalizing problems in 4–11 (not 12–17) year-olds. Less warmth and more aversive/inconsistent parenting are broad correlates of child psychopathology. Aversive/inconsistent parenting, is also related to specific externalizing problems. Parents may behave more warmly when their younger children have specific internalizing problems, net of overall psychopathology.
Mood episodes in bipolar disorder (BD) are still identified with subjective retrospective reports and scales. Digital biomarkers, such as actigraphy, heart rate variability, or ElectroDermal activity (EDA) have demonstrated their potential to objectively capture illness activity.
Objectives
To identify physiological digital signatures of illness activity during acute episodes of BD compared to euthymia and healthy controls (HC) using a novel wearable device (Empatica´s E4).
Methods
A pragmatic exploratory study. The sample will include 3 independent groups totalizing 60 individuals: 36 BD inpatients admitted due to severe acute episodes of mania (N=12), depression (N=12), and mixed features (N=12), will wear the E4-device at four timepoints: the acute phase (T0), treatment response (T1), symptoms remission (T2) and during euthymia (T3; outpatient follow-up). 12 BD euthymic outpatients and 12 HC will be asked to wear the E4-device once. Data pre-processing included average downsampling, channel time-alignment in 2D segments, 3D-array stacking of segments, and random shuffling for training/validation sets. Finally, machine learning algorithms will be applied.
Results
A total of 10 patients and 5 HC have been recruited so far. The preliminary results follow the first differences between the physiological digital biomarkers between manic and depressive episodes. 3 fully connected layers with 32 hidden units, ectified linear activation function (ReLU) activation, 25% dropout rate, significantly differentiated a manic from a depressive episode at different timepoints (T0, T1, T2).
Conclusions
New wearables technologies might provide objective decision-support parameters based on digital signatures of symptoms that would allow tailored treatments and early identification of symptoms.
Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.
Objectives
To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.
Methods
RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.
Results
A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.
Conclusions
RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.
Steinernema populi n. sp. was recovered by baiting from beneath poplar trees in China. Morphological and molecular features provided evidence for placing the new species into the Kushidai clade. The new species is characterized by the following morphological features: third-stage infective juveniles (IJ) with a body length of 1095 (973–1172) μm, a distance from the anterior end to excretory pore of 77 (70–86) μm and a tail length of 64 (55–72) μm. The Body length/Tail length (c) ratio and Anterior end to Excretory pore/ Tail length × 100 (E%) of S. populi n. sp. are substantially greater than those of all other ‘Feltiae–Kushidai–Monticolum’ group members. The first-generation males can be recognized by a spicule length of 66 (57–77) μm and a gubernaculum length of 46 (38–60) μm. The new species is further characterized by sequences of the internal transcribed spacer and partial 28S regions of the ribosomal DNA. Phylogenetic analyses show that Steinernema akhursti and Steinernema kushidai are the closest relatives to S. populi n. sp.
The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.
Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.
Background: Standardized magnetic resonance imaging (MRI) guidelines published in 2015 by the Europoean MAGNIMS group and in 2016 by the CMSC are important for the diagnosis and monitoring of patients with multiple sclerosis (MS) and for the appropriate use of MRI in routine clinical practice. Methods: Two panels of experts convened to update existing guidelines for a standardized MRI protocol. The MAGNIMS panel convened in Graz, Austria in April 2019. The CMSC NAIMS panel met separately and independently in Newark, USA in October 2019. Subsequently, the MAGNIMS, NAIMS, and CMSC working groups combined their efforts to reach an international consensus Results: The revised guidelines on MRI in MS merges recommendations from MAGNIMS, CMSC, and NAIMS to improve the use of MRI for diagnosis, prognosis and monitoring of individuals with MS. 3D acquisitions are emphasized for optimal comparison over time. Core brain sequences include a 3D-T2wFLAIR for lesion identification and monitoring treatment effectiveness. Gadolinium-based contrast is recommended for diagnostic studies and judicious use for routine monitoring of MS patients. DWI sequences are recommended for PML safety monitoring. Conclusions: The international consensus guidelines strive for global acceptance of a useful and usable standard of care for patients with MS.
The present paper uses the detailed flow data produced by direct numerical simulation (DNS) of a three-dimensional, spatially developing plane free shear layer to assess several commonly used turbulence models in compressible flows. The free shear layer is generated by two parallel streams separated by a splitter plate, with a naturally developing inflow condition. The DNS is conducted using a high-order discontinuous spectral element method (DSEM) for various convective Mach numbers. The DNS results are employed to provide insights into turbulence modelling. The analyses show that with the knowledge of the Reynolds velocity fluctuations and averages, the considered strong Reynolds analogy models can accurately predict temperature fluctuations and Favre velocity averages, while the extended strong Reynolds analogy models can correctly estimate the Favre velocity fluctuations and the Favre shear stress. The pressure–dilatation correlation and dilatational dissipation models overestimate the corresponding DNS results, especially with high compressibility. The pressure–strain correlation models perform excellently for most pressure–strain correlation components, while the compressibility modification model gives poor predictions. The results of an a priori test for subgrid-scale (SGS) models are also reported. The scale similarity and gradient models, which are non-eddy viscosity models, can accurately reproduce SGS stresses in terms of structure and magnitude. The dynamic Smagorinsky model, an eddy viscosity model but based on the scale similarity concept, shows acceptable correlation coefficients between the DNS and modelled SGS stresses. Finally, the Smagorinsky model, a purely dissipative model, yields low correlation coefficients and unacceptable accumulated errors.
The compressibility effects on energy exchange mechanisms in a three-dimensional, spatially developing plane free shear layer are investigated via data produced by direct numerical simulation. The compressible shear layer is simulated using a high-order discontinuous spectral element method for convective Mach numbers $M_c = 0.3$, 0.5 and 0.7. The energy exchange mechanisms in the flow are examined by analysing the budget terms of mean kinetic, internal and turbulent kinetic energy transport equations, in both transition and turbulent regions. The results show that turbulent production, turbulent viscous dissipation, mean viscous dissipation, pressure dilatation and enthalpic production are the main mechanisms responsible for energy exchange among different forms of energy. The effects of compressibility on energy transfer mechanisms are studied based on the analyses of those five budget terms. The primary budget terms evolve differently in the transition and turbulent regions and change significantly for varying compressibility. In the transition region, a double-peak variation becomes a single peak in the streamwise profile of the turbulent production as $M_c$ increases from 0.3 to 0.7, due to significant changes in the vortex pairing structures. The shear layer centre slightly shifts to the high-speed side due to the appearance of the velocity deficit. The velocity deficit presence distance (VDPD) becomes longer as compressibility increases. However, in the turbulent region, the cross-stream profiles of the main budget terms significantly shift to the low-speed side because of the asymmetric mass entrainment and shift even further as $M_c$ increases.
It is important to understand the temporal trend of the paediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load to estimate the transmission potential of children in schools and communities. We determined the differences in SARS-CoV-2 viral load dynamics between nasopharyngeal samples of infected asymptomatic and symptomatic children. Serial cycle threshold values of SARS-CoV-2 from the nasopharynx of a cohort of infected children were collected for analysis. Among 17 infected children, 10 (58.8%) were symptomatic. Symptomatic children, when compared to asymptomatic children, had higher viral loads (mean cycle threshold on day 7 of illness 28.6 vs. 36.7, P = 0.02). Peak SARS-CoV-2 viral loads occurred around day 2 of illness in infected children. Although we were unable to directly demonstrate infectivity, the detection of significant amount of virus in the upper airway of asymptomatic children suggest that they have the potential to shed and transmit SARS-CoV-2. Our study highlights the importance of contact tracing and screening for SARS-CoV-2 in children with epidemiological risk factors regardless of their symptom status, in order to improve containment of the virus in the community, including educational settings.
Coronavirus disease 2019 (COVID-19) pandemic is a major public health concern all over the world. Little is known about the impact of COVID-19 pandemic on mental health in the general population. This study aimed to assess the mental health problems and associated factors among a large sample of college students during the COVID-19 outbreak in China.
Methods
This cross-sectional and nation-wide survey of college students was conducted in China from 3 to 10 February 2020. A self-administered questionnaire was used to assess psychosocial factors, COVID-19 epidemic related factors and mental health problems. Acute stress, depressive and anxiety symptoms were measured by the Chinese versions of the impact of event scale-6, Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively. Univariate and hierarchical logistic regression analyses were performed to examine factors associated with mental health problems.
Results
Among 821 218 students who participated in the survey, 746 217 (90.9%) were included for the analysis. In total, 414 604 (55.6%) of the students were female. About 45% of the participants had mental health problems. The prevalence rates of probable acute stress, depressive and anxiety symptoms were 34.9%, 21.1% and 11.0%, respectively. COVID-19 epidemic factors that were associated with increased risk of mental health problems were having relatives or friends being infected (adjusted odds ratio = 1.72–2.33). Students with exposure to media coverage of the COVID-19 ≥3 h/day were 2.13 times more likely than students with media exposure <1 h/day to have acute stress symptoms. Individuals with low perceived social support were 4.84–5.98 times more likely than individuals with high perceived social support to have anxiety and depressive symptoms. In addition, senior year and prior mental health problems were also significantly associated with anxiety or/and depressive symptoms.
Conclusions
In this large-scale survey of college students in China, acute stress, anxiety and depressive symptoms are prevalent during the COVID-19 pandemic. Multiple epidemic and psychosocial factors, such as family members being infected, massive media exposure, low social support, senior year and prior mental health problems were associated with increased risk of mental health problems. Psychosocial support and mental health services should be provided to those students at risk.
Critical shortages of personal protective equipment, especially N95 respirators, during the coronavirus disease 2019 (COVID-19) pandemic continues to be a source of concern. Novel methods of N95 filtering face-piece respirator decontamination that can be scaled-up for in-hospital use can help address this concern and keep healthcare workers (HCWs) safe.
Methods:
A multidisciplinary pragmatic study was conducted to evaluate the use of an ultrasonic room high-level disinfection system (HLDS) that generates aerosolized peracetic acid (PAA) and hydrogen peroxide for decontamination of large numbers of N95 respirators. A cycle duration that consistently achieved disinfection of N95 respirators (defined as ≥6 log10 reductions in bacteriophage MS2 and Geobacillus stearothermophilus spores inoculated onto respirators) was identified. The treated masks were assessed for changes to their hydrophobicity, material structure, strap elasticity, and filtration efficiency. PAA and hydrogen peroxide off-gassing from treated masks were also assessed.
Results:
The PAA room HLDS was effective for disinfection of bacteriophage MS2 and G. stearothermophilus spores on respirators in a 2,447 cubic-foot (69.6 cubic-meter) room with an aerosol deployment time of 16 minutes and a dwell time of 32 minutes. The total cycle time was 1 hour and 16 minutes. After 5 treatment cycles, no adverse effects were detected on filtration efficiency, structural integrity, or strap elasticity. There was no detectable off-gassing of PAA and hydrogen peroxide from the treated masks at 20 and 60 minutes after the disinfection cycle, respectively.
Conclusion:
The PAA room disinfection system provides a rapidly scalable solution for in-hospital decontamination of large numbers of N95 respirators during the COVID-19 pandemic.