Cross-sectional Dataset Research Articles

Meta-Analysis of Brain Volumetric Abnormalities in Patients with Remitted Major Depressive Disorder

Although patients with major depressive disorder (MDD) achieve remission after antidepressant treatment, >90% of those in remission have at least one residual depressive symptom, which may be due to neural damage linked with MDD. To better understand the structural impairments in patients with remitted MDD, we conducted a meta-analysis comparing grey matter volume (GMV) abnormalities between patients with remitted MDD and healthy controls (HCs). There were 11 cross-sectional datasets that investigated 275 patients with remitted MDD versus 437 HCs, and 7 longitudinal datasets that investigated 167 patients with remitted MDD. We found that GMV in the left insula, inferior parietal gyri, amygdala, and right superior parietal gyrus was decreased in patients with remitted MDD than in HCs. Additionally, patients with remitted MDD had lower GMV in the bilateral gyrus rectus than those in the nonremission state. Moreover, increased GMV in the bilateral anterior cingulate cortex, right striatum, middle temporal gyrus, and superior frontal gyrus was observed in patients with remitted MDD than in HCs. Furthermore, patients with remitted MDD had a larger GMV in the bilateral median cingulate/paracingulate gyri, left striatum, putamen, amygdala, hippocampus, and parahippocampal gyrus at follow-up than at baseline. Based on the brain morphological abnormalities in patients with remitted MDD after electroconvulsive therapy and pharmacological treatment, we proposed a schematic diagram of targeted intervention approaches for residual symptoms. In summary, our findings provide neurobiology-based evidence for multitarget treatment of depression to reduce residual symptoms and improve social function in patients with MDD.

Deworming and micronutrient status by community open defecation prevalence: An observational study using nationally representative data from India, 2016-2018.

Micronutrient deficiencies are widespread in India. Soil-transmitted helminth (STH) infections are acquired by interaction with soil and water contaminated by human feces and lead to blood loss and poor micronutrient absorption. The current recommendation for control of STH-related morbidity is targeted deworming, yet little is known about the effectiveness of deworming on micronutrient status in varying sanitation contexts. Ranging between 1% and 40% prevalence across Indian states, open defecation (OD) remains high despite India's investments at elimination by promoting community-wide sanitation. This variation provides an opportunity to study the relationship between deworming, micronutrient status, and OD at-scale. Cross-sectional datasets that were representative for India were obtained the Comprehensive National Nutrition Survey in 2016 to 2018 (n = 105,060 individuals aged 1 to 19 years). Consumption of deworming medication was described by age and community OD level. Logistic regression models were used to examine the relationship between deworming, cluster OD, and their interactions, with anemia and micronutrient deficiencies (iron, zinc, vitamin A, folate, and vitamin B12), controlling for age, sex, wealth, diet, and seasonality. These regression models further allowed us to identify a minimum OD rate after which deworming becomes ineffective. In sensitivity analyses, the association between deworming and deficiencies were tested in subsamples of communities classified into 3 OD levels based on statistical tertiles: OD free (0% of households in the community practicing OD), moderate OD (>0% and <30%), or high OD (at least 30%). Average deworming coverage and OD prevalence in the sample were 43.4% [IQR 26.0, 59.0] and 19.1% [IQR 0, 28.5], respectively. Controlling for other determinants of nutritional status, adolescents living in communities with higher OD levels had lower coverage of deworming and higher prevalence of anemia, zinc, vitamin A, and B12 deficiencies. Compared to those who were not dewormed, dewormed children and adolescents had lower odds of anemia (adjusted odds ratio 0.72, (95% CI [0.67, 0.78], p < 0.001) and deficiencies of iron 0.78, (95% CI [0.74, 0.82], p < 0.001) and folate 0.69, (95% CI [0.64,0.74], p<0.001)) in OD free communities. These protective effects remained significant for anemia but diminished for other micronutrient deficiencies in communities with moderate or high OD. Analysis of community OD indicated a threshold range of 30% to 60%, above which targeted deworming was no longer significantly associated with lower anemia, iron, and folate deficiency. The primary limitations of the study included potential for omitted variables bias and inability to capture longitudinal effects. Moderate to high rates of OD significantly modify the association between deworming and micronutrient status in India. Public health policy could involve sequencing interventions, with focus on improving deworming coverage in communities that have achieved minimum thresholds of OD and re- triggering sanitation interventions in high OD communities prior to deworming days, ensuring high coverage for both. The efficacy of micronutrient supplementation as a complementary strategy to improve nutritional outcomes alongside deworming and OD elimination in this age group needs further study.

Why do some populist presidents erode liberal democratic institutions more than others in Latin America?

ABSTRACT This paper contributes to the current literature on the relationship between populist rule and liberal democracy by conducting quantitative research with a time-series cross-sectional dataset covering 18 Latin American countries from 1991 to 2017. The findings reveal an overall negative correlation between populist rule and the quality of liberal democratic institutions, highlighting their inherent incompatibility. Nevertheless, this paper argues that several factors influence the governing populists' ability to erode liberal democracy. Specifically, it is observed that the negative impact of populist rule on the quality of liberal democratic institutions is amplified in the presence of higher levels of natural resource rents and lower levels of industrial employment and financial development. Moreover, it is found that the impact of these moderating variables on the relationship between populist rule and liberal democracy is contingent upon the ideological orientation of governing populists.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Fusing Repeated Cross-Sectional Revealed Preference Datasets based on Rational Inattention Theory: Accounting for Changing Modal Preferences

To address the methodological limitation of cross-sectional studies and the data constraints of longitudinal/panel studies, this paper presents a model-based method to fuse repeated cross-sectional travel survey data based on the theory of rational inattention (RI) in discrete choice modeling. In the proposed framework, older cross-sectional data are used to model the prior probability of choice alternatives, and more recent cross-sectional data are used to capture conditional heterogeneous choices. The fusion method is theoretically more robust and computationally less burdensome than existing data pooling techniques. The method is empirically tested using data from two cycles of a large-sample post-secondary student travel survey in the Greater Toronto and Hamilton Area to investigate the commuting mode choices of post-secondary students. Parameter estimates of the RI-based multinomial logit (MNL) model indicate that the proposed method can generate behaviorally consistent results. Validation of the estimated model using a holdout sample indicates its improved forecasting performance compared with the classical random utility maximizing MNL model. The fusion method can be extended to more than two cycles of repeated cross-sectional data by updating the prior probabilities whenever new cross-sectional data become available. Thus, the study presents a continuous framework for fusing information from multiple time points using repeated cross-sectional datasets to capture preference evolution better and enhance the forecasting robustness of discrete choice models.

Reduced lateralization of multiple functional brain networks in autistic males

BackgroundAutism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.Methods In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.ResultsWe found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.ConclusionsThese findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

BEB-based models for ionisation cross sections of electron and positron impact with diatomic molecules

The ionising interactions of high-energy particles with molecules have applications in many areas. Despite this, some areas, including positron scattering, lack experimental data due to difficulties in performing experiments. Here, quick and simple methods for computing direct electron and positron impact ionisation cross sections are presented. These calculations, performed using the open-source software RAPID-CS, can provide a complete data set as a first approximation for when experimental, or more detailed computational work is not available. The cross-sectional data set includes the total, partial/fragment-specific, single-differential, average secondary electron energy and stopping power cross sections. The molecules N2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document}, O2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} and CO were chosen to study due to the availability of positron scattering data. An overall good agreement with experimental and other computational results is presented.Graphical abstractDirect electron (left) and positron right) impact partial ionisation cross sections for N2. Total cross section: red, N2+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{+}_{2}$$\\end{document} partial cross section: blue, N+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{+}$$\\end{document} partial cross section: green.electron: solid lines: BEB, experimental measurements by Lindsay and Mangan [36]:Ürosses, Straub et al. [37]: stars, Opel et al. [39]: squares. Positron: solid lines: BEB0, Dashed lines: BEBA, Marler and Surko [23]: red crosses, Bluhme et al. [24]: stars

Positive relationship between bird diversity and human mental health: an analysis of repeated cross-sectional data

An increasing body of research has examined the link between biodiversity of birds and human mental health, but most studies only use cross-sectional data. Few studies have used longitudinal or repeated cross-sectional data to investigate the mental health benefits of bird diversity. The aim of this study is to analyse the relationship between bird diversity and mental health at the national level using a unique repeated cross-sectional dataset. I used repeated cross-sectional health data from the German National Cohort health study, collected between March, 2014, and September, 2019, and annual bird citizen science data to investigate the effects of bird-diversity exposure on mental health. Mental health was measured using the summary score of the Patient Health Questionnaire depression module 9 (SumPHQ) and the Short Form Health Survey-12 Mental Health Component Scale. As a proxy for bird diversity, I created a unique indicator called reporting-rate richness and combined it with the health data. Reporting-rate richness measures the number of bird species within postcode areas across Germany in probabilities while accounting for variation in survey efforts. Alternative indicators of bird diversity, such as bird-species richness or abundance, were also calculated. Associations between bird diversity and mental health were estimated using linear regression with region and time fixed effects, adjusted for a range of sociodemographic and environmental confounders and spatial autocorrelation. Interaction terms between income levels and reporting-rate richness were also analysed to examine the moderating effect of socioeconomic status. I did the analyses for an unbalanced (n=176 362) and balanced (n=125 423) dataset, with the balanced dataset comprising only regions (postcode areas) in which health data were available for each year. The linear fixed-effects regression analysis indicated a significant negative association between reporting-rate richness and SumPHQ, as observed in both the unbalanced dataset (β -0·02, p=0·017) and the balanced dataset (β -0·03, p=0·0037). Similarly, regression results with both datasets showed a positive relationship between reporting-rate richness and Mental Health Component Scale (MCS; unbalanced β 0·02, p=0·0086; balanced β 0·03, p=0·0018). The moderator analyses revealed a significant influence of socioeconomic status on the relationship between reporting-rate richness and mental health. The robustness of these findings was confirmed through sensitivity analyses. The results suggest that a greater likelihood of having many different bird species in a person's area of residence might positively contribute to mental health, especially for people with lower socioeconomic status. These findings could have implications for biodiversity conservation and health policy decisions, as governments are facing challenges such as global biodiversity loss and growing public mental health problems. None.

Empowering Education: Addressing Vaccination Hesitancy in The Covid-19 Era

The COVID-19 pandemic started in 2019 with the emergence of a new coronavirus and has greatly affected societies worldwide. It has led to an increase in mortality rates, disturbed pre-existing health conditions, disrupted educational systems, and reduced global workforce productivity and production. The scientific community has expedited vaccine development to combat the pandemic. However, the emergence of various vaccines has paradoxically resulted in a decline in public confidence, contributing to anti-vaccine sentiments. This study explores the pivotal role of education in fortifying vaccination rates and seeks to identify effective strategies to address hesitancy and strengthen public health measures. This research uses the two-stage least squares (2SLS) methodology to analyze the “Learning Loss Covid-19” cross-sectional dataset, which the World Bank published on January 4, 2023. The investigation focuses on the relationship between “Vaccination rate (per person)” and “Year of Education” across 41 countries. The empirical findings show that education has a significant impact on vaccination rates. An increase of one year in educational attainment leads to an approximate 14% increase in doses administered per person and a substantial 23% surge in the average dosage across nations. Therefore, education plays a crucial role in fortifying and elevating vaccination rates, demonstrating its significance as a potent tool in global public health endeavours.

A STRUCTURAL EQUATION MODELING WITH WELFARE INDICATORS: EXAMINING THE RELATIONSHIP BETWEEN SOCIOECONOMIC STATUS, ACCESS TO SERVICES AND MATERIAL DEPRIVATION

This study aims at presenting comprehensive model using welfare indicators such as socioeconomic status, access to services and material deprivation, and examining the relationships between these variables. In the analysis, we use the pooled cross-sectional data set of the Household Budget Survey of the Turkish Statistical Institute (TURKSTAT) covering the period 2015-2019. Since the simultaneous relationship between welfare indicators such as socioeconomic status, material deprivation and access to services is examined using latent variables, Structural Equation Modeling is preferred. The study finds that socioeconomic status, composed of education and income, increases households' access to services and decreases their material deprivation. In addition, as households' access to services increases, their material deprivation decreases. Our study also finds that women's socioeconomic status is lower than men's in Turkiye during the period analysed. Moreover men in households have more limited access to services and face more material deprivation than women. The increase in household size makes access to services more difficult, worsens the socioeconomic situation and increases deprivation. Considering the health status variable, it is concluded that healthy individuals have higher socioeconomic status and suffer from less deprivation.

Institutional reforms and entrepreneurial growth ambitions

Institutional reforms have resulted in deep transformations of the global economy. Yet, the theoretical development and accumulating insights about the effects of institutional reforms on entrepreneurial outcomes have been inconclusive. Our study applies categorisation theory to argue that flexibility- and stability-enhancing reforms may affect entrepreneurial growth ambitions in distinct ways, depending on whether more innovative versus less innovative entrepreneurs perceive specific reforms as an opportunity or a threat. Our study employs a multi-source, repeated cross-sectional dataset of approximately 150,000 entrepreneurs from 65 countries, covering the period from 2002 to 2016. Our findings indicate that flexibility-enhancing reforms lead to higher growth ambitions. They are particularly favoured by less innovative entrepreneurs. On the contrary, stability-enhancing reforms do not affect growth ambitions of entrepreneurs in general but rather increase growth ambitions of more innovative entrepreneurs. Our study provides important theoretical and practical implications about the consequences of institutional reforms on growth ambitions of entrepreneurs with different levels of innovation.

Change in work arrangement during the COVID-19 pandemic: A large shift to remote and hybrid work

With two repeated cross-sectional datasets collected in Southern California, we investigate changes in work arrangement and commute trip generation across four timepoints before and during the COVID-19 pandemic: fall 2019 (recalled in fall 2020 retrospectively); fall 2020 and summer 2021 (reported at the moment); and summer 2022 (an expectation as of summer 2021 for a near future prospectively). Our results highlight a large shift from physical commutes to exclusively/predominantly remote work for many workers in fall 2020, followed by a transition towards hybrid work (combining in-person work and remote work) in summer 2021. The adoption of remote/hybrid work varied considerably across different types of workers and sociodemographic groups, prominently driven by high-income earners, highly educated individuals, urban residents, full-time workers, and those with white-collar, STEM (science, technology, engineering, and mathematics), and government jobs. As the pandemic subsides, many workers have returned to physical commutes for some of their workdays, but the prevalence of remote/hybrid work is expected to endure into the future. These findings confirm oft-discussed trends and anecdotal evidence in the study region. However, the study provides rigorous quantitative evidence into reasons behind these observations and heterogeneity across groups. We also suggest forward-looking policies to promote transportation equity, reimagine transportation options, and support public transit to adapt to the evolving travel patterns in the post-pandemic society.

Repetitive transcranial magnetic stimulation can improve negative symptoms and/or neurocognitive impairments in the first psychosis episode: A randomized controlled trial

ObjectiveNegative symptoms and neurocognitive impairments in psychosis correlate with their severity. Currently, there is no satisfactory treatment. We aimed to evaluate and compare the effects of repetitive transcranial magnetic stimulation(rTMS) on negative symptoms and neurocognitive impairments in patients in first-episode of psychosis(FEP) in a randomized controlled trial(RCT). MethodThis is a single-site RCT of 85 patients with FEP. Patients were randomized to receive a 4-week course of active(n = 45) or sham rTMS(n = 40). Factor analysis was applied to a cross-sectional dataset of 744 FEP patients who completed negative symptom evaluation and neurocognitive battery tests. Two independent dimensions were generated and used for the K-means cluster analysis to produce sub-clusters. rTMS of 1-Hz was delivered to the right orbitofrontal(OFC) cortex. ResultsTwo distinct dimensional factors of neurocognitive functions(factor-1) and negative symptoms(factor-2), and three clusters with distinctive features were generated. Significant improvements in factor-1 and factor-2 were observed after 4-weeks of rTMS treatment in both the active and sham rTMS groups. The repeated-measures analysis of variance revealed a significant effect of time×group(F = 5.594, p = 0.021, η2 = 0.073) on factor-2, but no effect of time×group on factor-1. Only improvements in negative symptoms were significantly different between the active and sham rTMS groups(p = 0.028). Patients in cluster-3 characterized by extensive negative symptoms, showed greater improvement in the active rTMS group than in the sham rTMS group. ConclusionsThe 1-Hz right OFC cortex rTMS is more effective in reducing negative symptoms than neurocognitive impairments. It is especially effective in patients with dominantly negative symptoms in FEP.

Overindebtedness, unemployment, and poor mental health - and the role of sense of control: a population-based Swiss study.

Both overindebtedness and unemployment are critical life events that can result in or lead to poor mental health. What is less known is that the two partly interrelated events frequently go along with a feeling of loss or lack of control in life, which could be the main reason why they are associated with poor mental health. This has not been examined in previous research, particularly not in this combination. This study used and merged two cross-sectional data sets. Data collected in 2019 on 219 overindebted clients of the four official debt advisory centers in the Canton of Zurich were linked with a comparable subsample of 1,997 respondents from the Swiss Health Survey of 2017. The entire study population covered 2,216 adult individuals living in the Canton of Zurich. The prevalence of no or low sense of control, medium to high psychological distress, and moderate to major depression was much higher among the 44 solely unemployed (36/30/12%), the 189 solely overindebted (73/83/53%), and particularly among the 30 unemployed and overindebted (93/97/60%) than among all 1,953 other survey participants (21/13/7%). Unemployment, overindebtedness, and a (resulting) lack or loss of control were all found to be strong risk factors for the two mental health outcomes under study. Associations, or rather negative health effects, were partly but not fully mediated by the sense of control. Overindebtedness much more strongly predicted psychological distress (ß = -0.37) and depression (ß = 0.17) than unemployment (ß = -0.05/0.01). The sense of control turned out to be an independent explanatory factor for poor mental health and even the strongest of all (ß = 0.49/-0.59). Improving a person's control beliefs could be a promising measure for preventing mental health disorders in general and in people who are unemployed and/or overindebted in particular.

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

This paper presents a new method for automatic identification of the inspiratory and expiratory breathing phases in lung sound recordings. Adventitious lung sounds (wheezes and crackles), superimposed on the breath sounds, are generally an early indication of the disease, and their timing in the breathing cycle (early/mid/late inspiratory or expiratory) has clinical significance for monitoring or diagnosing disease. Therefore, the identification of the phases of the breathing cycle is an essential step for clinical interpretation of pulmonary auscultation. The proposed algorithm is designed to be robust in the presence of adventitious lung sounds or where the breath sounds may be noisy compared to healthy lung sounds. The algorithm uses the Savitzky & Golay (SG) filter to estimate the second derivative of the lung sound signal then calculates its normalized absolute value. A threshold value is used to clip any large amplitude peaks and, following low-pas filtering, the breathing phases are visible in the plotted signal. A rule-based approach to locating peaks and troughs is then used to identify inspirations and expirations. The performance of the method is evaluated using four different datasets: (a) a longitudinal dataset recorded from 19 subjects with a diagnosis of idiopathic pulmonary fibrosis (IPF), (b) a cross-sectional dataset recorded from 55 subjects who were referred for high-resolution computed tomography (HRCT) scan of the chest for various clinical indications, (c) a longitudinal dataset recorded from 10 healthy subjects, and (d) an open access lung sounds dataset containing recordings from 41 subjects with wheeze (9 with chronic obstructive pulmonary fibrosis and 32 with asthma). On average for inspiratory phase identification the algorithm had a sensitivity of (mean (standard deviation)) 92.84 (12.88)%, positive predictive value of 90.64(15.96)%, and F1-score of 90.67 (12.53)%. For identification of the expiratory phase, the algorithm had an average sensitivity of 92.19 (13.44)%, an average positive predictive value of 91.55 (15.23)%, and an average F1-score of 90.89 (12.45)%. The method shows good potential for automatic identification of breathing phases in recorded lung sounds.

Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome.

Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.

An improved OMI ozone profile research product version 2.0 with collection 4 L1b data and algorithm updates

Abstract. We describe the new and improved version 2 of the ozone profile research product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. One of the major changes is to switch the OMI L1b data from collection 3 to the recent collection 4 as well as the accompanying auxiliary datasets. The algorithm details are updated on radiative transfer model calculation and measurement calibrations, along with the input changes in meteorological data, and with the use of a tropopause-based ozone profile climatology, an improved high-resolution solar reference spectrum, and a recent ozone absorption cross-section dataset. A super Gaussian is applied to better represent OMI slit functions instead of a normal Gaussian. The effect of slit function errors on the spectral residuals is further accounted for as pseudo-absorbers in the iterative fit process. The OMI irradiances are averaged into monthly composites to reduce noise uncertainties in OMI daily measurements and to cancel out the temporal variations of instrument characteristics that are common in both radiance and irradiance measurements, which was previously neglected due to use of climatological composites. The empirical soft calibration spectra are re-derived to be consistent with the updated implementations and derived annually to remove the time-varying systematic biases between measured and simulated radiances. The “common mode” correction spectra are derived from remaining residual spectra after soft calibration as a function of solar zenith angle. The common mode is included as a pseudo-absorber in the iterative fit process, which helps to reduce the discrepancies of ozone retrieval accuracy between lower and higher solar zenith angles and between nadir and off-nadir pixels. Validation with ozonesonde measurements demonstrates the improvements of ozone profile retrievals in the troposphere, especially around the tropopause. The retrieval quality of tropospheric column ozone is improved with respect to the seasonal consistency between winter and summer as well as the long-term consistency before and after the row-anomaly occurrence.

Metabolite T1 relaxation times decrease across the adult lifespan.

Relaxation correction is an integral step in quantifying brain metabolite concentrations measured by in vivo magnetic resonance spectroscopy (MRS). While most quantification routines assume constant T1 relaxation across age, it is possible that aging alters T1 relaxation rates, as is seen for T2 relaxation. Here, we investigate the age dependence of metabolite T1 relaxation times at 3 T in both gray- and white-matter-rich voxels using publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3 T using Point RESolved Spectroscopy (PRESS) localization. Data were acquired from voxels in the posterior cingulate cortex (PCC) and centrum semiovale (CSO) in 102 healthy volunteers across 5 decades of life (aged 20-69 years). All spectra were analyzed in Osprey v.2.4.0. To estimate T1 relaxation times for total N-acetyl aspartate at 2.0 ppm (tNAA2.0) and total creatine at 3.0 ppm (tCr3.0), the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single-inversion-recovery signal equation. Correlations between T1 and subject age were evaluated. Spearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.27; p < 0.006) and tCr3.0 (rs = -0.40; p < 0.001) decreased significantly with age in white-matter-rich CSO, and less steeply for tNAA2.0 (rs = -0.228; p = 0.005) and (not significantly for) tCr3.0 (rs = -0.13; p = 0.196) in graymatter-rich PCC. The analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

Adverse Childhood Experiences May Dampen the Protective Role of Sleep Duration on Adolescent Obesity Risk.

The purpose of this study was to explore Adverse Childhood Experiences (ACEs) as a moderator between sleep duration/irregularity and overweight/obesity in U.S. adolescents. Using the National Survey of Children's Health 2017-2018 cross-sectional dataset, we included adolescents with available sleep and Body Mass Index (BMI) data. In a sample of 24,100 adolescents (mean age = 13.56years, 49.35% female; 51% White), parents reported adolescent's sleep duration/irregularity, and number of ACEs. Logistic regression estimated the interaction between sleep duration/irregularity and the number of ACEs on overweight/obesity risk (BMI ≥ 85th percentile-for-age) using a stepwise approach and accounting for complex survey design. In the 24,100 adolescents, 33% were overweight/obese, 50% had ≥ 1 ACE, 37% slept < 8-10h/night, and 14% had irregular sleep. Accounting for covariates and ACEs, every hour increase in sleep duration was associated with 6% decrease in overweight/obesity odds. There was a significant interaction between sleep duration and ACEs; the association between increasing sleep duration and decreasing odds of overweight/obesity was significant only in adolescents without ACEs (OR = 0.87, 95% CI [0.80, 0.95], p < 0.001). Increasing sleep duration is a recognized intervention target to decrease obesity risk, yet in adolescents experiencing ≥ 1 ACE, this protective role may be dampened. Future work may explore mechanisms for overweight/obesity development to inform interventions for adolescents facing adversity.

The determinants of legislation for radiofrequency electromagnetic fields (RF-EMFs) with the onset of 5G: An empirical analysis with a worldwide cross-sectional dataset.

The unprecedented exposure of radiofrequency electromagnetic field (RF-EMF) to humans from mobile communications raises serious public concern about the possibility of unexpected adverse health effects and has stimulated authorities to adopt precautionary exposure limits. These limits are distinctly different across countries, and the causes of these differences are unclear from the literature. This article is the first empirical analysis on the determinants of RF-EMF exposure legislation, using a novel cross-sectional database of 164 countries worldwide. The analysis shows that decentralization and mobile competition in countries with low mobile network deployment tend to promote more stringent RF-EMF exposure limits across the dataset with 164 countries. In more decentralized countries, the regions had a greater influence on national legislation and could accommodate local demands with the advent of mobile technology in the 2000s. In contrast, decentralization and mobile competition in countries with high levels of mobile network deployment tend to relax RF-EMF exposure limits in the sample of 61 countries with fifth-generation (5G) technology. Indeed, restrictive RF-EMF exposure limits are constraining 5G deployment in a context of the widespread adoption of mobile-broadband technologies. These results should be useful for policymakers and mobile operators alike to anticipate the outcome of legislation in countries that have yet to introduce 5G technology. The results should also be useful when reviewing policies and strategies for the implementation of the upcoming 6G technology in frequency bands that will be increasingly higher (above 6GHz up to THz for very local usage), and hence where the health effects on humans are less well studied.