Balanced Sampling Research Articles

Exploring contextual effects of post-migration housing environment on mental health of asylum seekers and refugees: A cross-sectional, population-based, multi-level analysis in a German federal state.

Asylum seekers and refugees (ASR) in Germany are dispersed quasi-randomly to state-provided, collective accommodation centres. We aimed to analyse contextual effects of post-migration housing environment on their mental health. We drew a balanced random sample of 54 from 1 938 accommodation centres with 70 634 ASR in Germany's 3rd largest federal state. Individual-level data on depression and anxiety as well as sociodemographic- and asylum-related covariates, were collected and linked to contextual geo-referenced data on housing environment ('Small-area Housing Environment Deterioration' index, number of residents, remoteness, urbanity, and German Index of Multiple Deprivation). We fitted two-level random-intercept models to exploratively estimate adjusted contextual effects. Of 411 surveyed participants, 45.53% and 44.83%, respectively, reported symptoms of depression or anxiety. 52.8% lived in centres with highest deterioration, 46.2% in centres with > = 50 residents, 76.9% in urban, and 56% in deprived districts. 7.4% of centres were remote. We found statistically significant clustering in reporting anxiety on the level of accommodation centres. The model resulted in an intraclass correlation of 0.16 which translated into a median odds ratio of 2.10 for the accommodation-level effects. No significant clustering was found for symptoms of depression. The highest degree of deterioration, large accommodation size, remoteness, and district urbanity showed higher, but statistically not significant, odds for reporting anxiety or depression. District deprivation demonstrated higher odds for anxiety and lower odds for depression yet remained statistically insignificant for both. Evidence for contextual effects of housing environment on mental health of ASR could not be established but residual confounding by length of stay in the accommodation centre cannot be ruled out. Confirmatory analyses with prior power calculations are needed to complement these exploratory estimates.

THE ROLE OF MEDICAID HOME- AND COMMUNITY-BASED SERVICES IN USE OF MEDICARE POST–ACUTE CARE

Abstract Medicaid-funded long-term services and supports are increasingly provided through home and community-based services (HCBS) to promote continued community living. While an emerging body of evidence examines the direct benefits and costs of HCBS, there may also be unexplored synergies with Medicare-funded post-acute care (PAC). This study aims to provide empirical evidence on how the use of Medicaid HCBS influences Medicare PAC utilization among the dually enrolled. Using national Medicare claims, Medicaid claims (TAF), MDS, and OASIS data from 2014 to 2017, we estimated the relationship between prior Medicaid HCBS use and PAC utilization in a national sample of duals with qualifying index hospitalizations. We used inverse probability weights to create balanced samples on observed characteristics and estimated multivariable regression with hospital fixed effects and extensive controls. We also conducted stratified analyses for key subgroups and tested for interactions between HCBS use and three measures of states’ HCBS generosity. We found HCBS use was associated with a 5.2 percentage-point increase in use of any Medicare-funded PAC, a 7.2 percentage-point increase in home health use, a 2.1 percentage-point decrease in institutional PAC, and a 1.95-day reduction of skilled nursing facility length of stay. In other words, use of Medicaid-funded HCBS was associated with a shift in Medicare-funded PAC use toward home-based settings. These findings were stronger in states with more generous HCBS funding. As we found that use of Medicaid-funded HCBS may alter choices about Medicare-funded PAC use, this synergy with HCBS should be considered by policymakers when contemplating further HCBS expansions.

Predicting Football Match Outcomes with Machine Learning Approaches

The increasing use of data-driven approaches has led to the development of models to predict football match outcomes. However, predicting match outcomes accurately remains a challenge due to the sport's inherent unpredictability. In this study, we have investigated the usage of different machine learning models in predicting the outcome of English Premier League matches. We assessed the performance of random forest, logistic regression, linear support vector classifier and extreme gradient boosting models for binary and multiclass classification. These models are trained with datasets obtained using different sampling techniques. The result showed that the models performed better when trained with dataset obtained using a balanced sampling technique for binary classification. Additionally, the models' predictions were evaluated by conducting simulation on football betting profits based on the 2022-2023 EPL season. The model achieved the highest accuracy is the binary class random forest, but the model provided the highest football betting profit is the binary class logistic regression.

A High-Sensitivity Low-Nanoflow LC-MS Configuration for High-Throughput Sample-Limited Proteomics.

This work demonstrates the utility of high-throughput nanoLC-MS and label-free quantification (LFQ) for sample-limited bottom-up proteomics analysis, including single-cell proteomics (SCP). Conditions were optimized on a 50 μm internal diameter (I.D.) column operated at 100 nL/min in the direct injection workflow to balance method sensitivity and sample throughput from 24 to 72 samples/day. Multiple data acquisition strategies were also evaluated for proteome coverage, including data-dependent acquisition (DDA), wide-window acquisition (WWA), and wide-window data-independent acquisition (WW-DIA). Analyzing 250 pg HeLa digest with a 10-min LC gradient (72 samples/day) provided >900, >1,800, and >3,000 protein group identifications for DDA, WWA, and WW-DIA, respectively. Total method cycle time was further reduced from 20 to 14.4 min (100 samples/day) by employing a trap-and-elute workflow, enabling 70% mass spectrometer utilization. The method was applied to library-free DIA analysis of single-cell samples, yielding >1,700 protein groups identified. In conclusion, this study provides a high-sensitivity, high-throughput nanoLC-MS configuration for sample-limited proteomics.

Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.

Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions

Vehicle matching in variable illumination environments can be challenging due to the heavy dependence of vehicle appearance on lighting conditions. To address this issue, we propose a two-stage progressive learning (TSPL) framework. In the first stage, illumination-aware metric learning is enforced using a two-branch network via two illumination-specific feature spaces, used to explicitly model differences in lighting. In the second stage, discriminative feature learning is introduced to extract distinguishing features from a given vehicle. This process consists of a local feature extraction attention module, a local constraint, and a balanced sampling strategy. During the metric learning phase, the model expresses the union of local features, extracted from the attention module, with illumination-specific global features to form joint vehicle features. As part of the study, we construct a large-scale dataset, termed VERI-DAN (vehicle re-identification across day and night), to address the current lack of vehicle datasets exhibiting variable lighting conditions. This set is composed of 200,004 images from 16,654 vehicles, collected in various natural illumination environments. Validation experiments conducted with the VERI-DAN and Vehicle-1M datasets demonstrated that our proposed methodology effectively improved vehicle re-identification Rank-1 accuracy.

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p<0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p<0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women.Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings.Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

Age‐ and Sex‐Related Morphological Changes in Cerebral Blood Vessels: a 7T TOF MRA Study

AbstractBackgroundIn this study we evaluated age‐ and sex‐associated morphological changes of cerebral blood vessels using 7T Time‐of‐flight (TOF) magnetic resonance angiography (MRA).MethodOur study consisted of 25 cognitively normal older adults (20 female, mean age 68.2 years). Whole‐Brain TOF MRA images were collected on a 7T Siemens scanner at the University of Pittsburgh (slice number = 354, voxel size = 0.38*0.38*0.38 mm, 12 mins). An iterative vessel segmentation algorithm, VesselMapper, was used to segment blood vessels on TOF MRA images (Li et al., 2023). For each subject, vessels are further divided into large and small vessels by median split based on vessel diameters. Mean tortuosity of large vessels, small vessels and overall vessels was computed. Linear regression analyses were performed to evaluate the main effects of age and sex, and the age*sex interaction on vessel tortuosity and diameter.ResultAs shown in Figure 1, we found age‐related increase in mean vessel tortuosity (greater age is associated with greater tortuosity, p<0.02). This age‐related difference is driven by small vessels (p = 0.002) rather than large vessels (p = 0.7). We also found that age is positively associated with mean voxel tortuosity (p<0.001, black line in Figure 2). Furthermore, women showed greater age‐related increase in mean arterial tortuosity, compared to men (age*sex interaction p = 0.04, Figure 2).ConclusionOur findings validated that morphological markers of cerebral blood vessels are sensitive to aging. Small vessels in the brain are particularly vulnerable. Further, compared to men, older women are at a higher risk for cerebral small vessel disease, which may contribute to elevated SVD‐related AD risk in women. Limitation: The study has an unbalanced sample (more women than men) and a relatively modest sample size (N = 25). A cross‐sectional instead of longitudinal design was used in this analysis. Future study with a large balanced sample and a longitudinal design are required to confirm our findings. Reference: Li, Jiatai, et al. “VesselMapper ‐ A Robust Vessel Segmentation Algorithm for 3D Images.” Annual Meeting of the Organization for Human Brain Mapping. 2023

OP144 Impact Of A Training Program For The General Population On Knowledge Of Aortic Valve Stenosis

IntroductionLimited knowledge of the symptomatology of aortic stenosis (AS) among the general population may delay diagnosis and have a major impact on morbidity and resource use. Training programs have often been advocated by the scientific community. The present study reported the results of an assessment of a training program for the general population.MethodsPatients who attended healthcare centers were asked to answer a questionnaire on their level of knowledge around AS. A cohort of patients without training (n=681) answered the questionnaire and a second cohort answered the questionnaire via phone 24 hours after training (n=197). Propensity score matching by sex and age was used to obtain a balanced sample between the two cohorts, giving a total study sample of 394 individuals (197 without training and 197 with training). A descriptive analysis was performed to compare differences in the level of knowledge between the two cohorts. Predictors of AS symptomatology were identified using multivariate logistic regression.ResultsThe trained cohort was more aware of AS disease than the untrained cohort (79% versus 31%, 95% confidence interval [CI]: 0.39, 0.56; p<0.001). They were also better at distinguishing the symptoms associated with AS (80% versus 43%, 95% CI: 0.28, 0.48, p<0.001) and were more aware of its severity (36% versus 12%; 95% CI: 0.16, 0.32, p<0.001). Moreover, the trained cohort were better at identifying symptoms that should make them consider visiting a doctor (76% versus 65%; 95% CI 0.02, 0.20, p<0.02). No differences were observed in level of concern regarding AS (8% versus 4%; 95% CI: -0.0046, 0.09, p=0.08).The trained people who were aware of AS (p=0.04) correctly classified AS as a valvular disease (p=0.025), would seek medical consultation when AS symptoms occurred (p=0.04), and were more likely to correctly detect AS symptoms.ConclusionsThe training program significantly improved the knowledge and awareness of AS in the general population. This can improve the timeliness of AS diagnosis, reducing the health and economic burden of AS for the healthcare system.

Personality Pathology and Suicide Risk: Examining the Relationship Between DSM-5 Alternative Model Traits and Suicidal Ideation and Behavior in College-Aged Individuals.

This study examines the link between personality pathology and suicide risk regarding the DSM-5 alternative model of personality disorders. The study investigates the facets, domains, internalizing, and externalizing of personality pathology and their correlation and predictive significance for suicidal ideation and behavior. This study examined a diverse and balanced sample of 1,398 college students aged between 18- and 29-year-olds from nine colleges in Kafrelshiekh University, with nearly equal representation of both genders (687 males, 711 females), a mix of rural and urban residents (807 rural, 591 urban), and a wide range of socioeconomic backgrounds (15 very low SES, 84 low SES, 878 moderate SES, 364 high SES, and 57 very high SES). The Personality Inventory for the DSM-5 (PID-5) was utilized to assess personality pathology. Columbia-Suicide Severity Rating Scale (C-SSRS) was used to evaluate suicidal ideation and behavior. Logistic regression reveals significant associations between personality traits and suicidal ideation (e.g., Anhedonia, Suspiciousness) and behavior (e.g., Risk Taking, Depressivity). Negative Affect and Detachment are significantly linked to suicidal ideation, while Detachment, Disinhibition, and Psychoticism are linked to suicidal behavior. Internalizing personality pathology predicts both ideation and behavior, indicating a contribution to suicidal thoughts and self-destructive acts. Externalizing is a significant predictor of suicidal behavior.

Basil plant leaf disease detection using amalgam based deep learning models

<p class="Normal1">Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting <em>Basil</em> plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect <em>basil</em> plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for <em>basil</em> leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of <em>basil</em> plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.</p>

Significance of N bonding configurations in N-doped graphene for enhanced supercapacitive performance: A comparative study in aqueous electrolytes

Herein, Nitrogen doped graphene (NG) samples for supercapacitive applications were synthesized using green approach of microwave assisted hydrothermal synthesis in 300 s. The as prepared NG samples with appreciable N content (2.93–7.48 %) showed all major N bonding configurations fruitful for enhancing the supercapacitance performance. The surface, chemical and morphological features of the synthesized samples were examined, especially the different N configurations available on N doped graphene framework. Detailed analysis revealed that equal distribution of all the N bonding configurations along with high crystalline nature and high surface area was exhibited by the NG3 sample. The supercapacitive performance of NG3 sample was highly appreciable for all electrolytes even when compared with synthesized samples having higher N doping (NG6 and NG8). The present study brings to fore the significance and necessity of balance in N-doped graphene synthesized for supercapacitance electrode applications. It emerged that for good supercapacitive characteristics the synthesized sample should be balanced both in terms of amount of N content vs crystalline integrity of the graphene as well as nearly equal contributions of all major N bonding configurations (pyridinic N, pyrrolic N and quaternary N) rather than dominance of any one configuration. The most balanced sample had enhanced capacitance performance for all aqueous electrolytes even in lowest molarity of 0.5 M. The ion conduction pathways in each individual electrolyte have been discussed on the basis of hydrodynamic radius and ionic mobility of respective anions and cations. The enhanced wettability, high surface area, optimum N content, crystalline integrity and synergistic effect of equal distribution of the relevant N bonding configurations (pyridinic N, pyrrolic N and quaternary N) contributed to high specific capacitance of 113.53 Fg−1 for NG3-Ni sample. The sample showed high capacitance retention with high coulombic efficiency and low internal resistance, thus, cementing its candidature as an electrode material for supercapacitor industry.

Mental health and resilience after the covid-19 pandemic: a multi-ethnic longitudinal survey

BackgroundLongitudinal work on the impact of COVID-19 on population mental health and resilience beyond the first year of the pandemic is lacking. We aimed to understand how mental health and resilience evolved during the pandemic (2020) and two years later (2022) in a multi-ethnic Singaporean population. In addition, we assessed what characteristics were associated with mental health and resilience scores.MethodsWe surveyed and analysed two balanced panel samples up to four times between 30th April 2020 and 11th July 2022. One panel assessed psychological distress (Kessler-10) and well-being (short Warwick Edinburgh Mental Well-being scale) n = 313, and one panel assessed resilience (10-item Connor-Davidson Resilience Scale©) n = 583. A linear panel regression model with random effects assessed the temporal patterns for psychological distress, well-being, and resilience.ResultsMean psychological distress scores (Kessler-10) were relatively stable over time and were not statistically significantly worse than baseline at any follow-up. Well-being scores improved over time and were significantly better than baseline by the third survey (22nd Jul-18th Aug 2020) (0.54 p = 0.007, Cohen’s d 0.12). Scores had worsened by the last survey (27th June-11th July 2022) but were not significantly different from baseline 0.20 p = 0.30. Resilience scores declined over time. Scores at both follow-ups (14th Aug- 4th Sep 2020 and 27th June-11th July 2022) were statistically significantly lower than baseline: -1.69 p < 0.001 (Cohen’s d 0.25) and -0.96 p = 0.006 (Cohen’s d 0.14), respectively.ConclusionsOur study joins a body of work measuring the longitudinal effects of COVID-19 on population mental health and resilience. While, the magnitude of the effect related to resilience decline is small, our findings indicate that particular attention should be given to ongoing population surveillance, with the aim of maintaining good health and well-being.

Examining The Relationship Between Children's Dietary Patterns and The Development of Dental Problems

Background: This cross-sectional study explores the relationship between children's dietary patterns and the development of dental problems in a sample of 200 participants aged 6 to 12 years. Recognizing the critical role of nutrition in pediatric oral health, the study aims to identify associations between dietary habits, nutrient intake, and prevalent dental issues. Methods: Dietary patterns were assessed using a Food Frequency Questionnaire (FFQ), and nutrient intake was analyzed. Clinical examinations by trained dental professionals evaluated dental problems, including caries, enamel erosion, and malocclusions. Statistical analyses, including logistic regression, were employed to examine associations between dietary patterns and dental outcomes. Results: Demographic characteristics indicated a balanced sample with equal gender distribution and representation across age groups. Analysis of dietary patterns revealed mean values within recommended ranges for caloric intake, protein, carbohydrates, fat, and calcium. Significant associations were found between high sugar consumption and dental caries, low calcium intake and enamel erosion, and irregular snacking habits and malocclusions Conclusion: This study underscores the importance of understanding the intricate relationship between children's dietary patterns and dental health. The identified associations provide insights for targeted interventions aimed at reducing sugar consumption, promoting adequate calcium intake, and addressing irregular snacking habits to enhance pediatric oral health.

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models.

The emotion regulation strategies of flourishing adults

Flourishing is an optimal state of wellbeing, but the mechanisms that enable flourishing are unclear. This study examined the role of emotion regulation (ER) strategies that may enable flourishing. The first aim examined differences between flourishers and non-flourishers in the use of functional/adaptive and dysfunctional/maladaptive ER strategies. A second aim specifically compared differences between flourishers and those non-flourishers who were free of psychopathology. We hypothesised that flourishers utilise greater use of functional/adaptive and lower use of dysfunctional/maladaptive strategies in comparison with non-flourishers, and those without pathology. Australian adults (N = 292) completed measures of flourishing, depression, and anxiety, and two measures of emotion regulation. Quota sampling obtained a balanced sample by age-group and gender. Regression analyses regressed ER strategies on flourishing and depression/anxiety status, adjusting for socio-demographic covariates. ER measures included the Emotion Regulation Questionnaire (ERQ) to assess reappraisal and suppression strategies, and the Emotion Regulation Profile – Revised (ERP-R) used vignettes to assess intentional response to situations. For the first aim, there was limited evidence that flourishers utilise higher levels of functional/adaptive, but substantive evidence that they use lower levels of dysfunctional/maladaptive ER strategies. For the second aim, flourishing was associated with these ER strategies over-and-above being free of pathology. The findings highlight a nuanced understanding of the ER of flourishers; flourishers appear to limit their use of dysfunctional/maladaptive strategies and do not necessarily report increased use of functional/adaptive strategies. Implications for wellbeing research and clinical practice are discussed.

One point per cluster spatially balanced sampling

A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with relatively high precision. Spatially balanced designs have good spatial spread and give precise results for commonly used estimators when surveying natural resources. A new design is proposed which draws spatially balanced samples from stratified and unstratified populations. The method is two-fold. First, the population is partitioned into n compact geographic clusters. Then, a one point per cluster sample is drawn using a linear assignment strategy that optimises the spatial spread of the sample. Numerical results on several simulated populations show that the method generates well-spread samples and compares favourably with existing designs. An example application is also provided, where soil organic matter concentrations are estimated over a study area in Voorst, Netherlands.

Evaluating the impact of a street outreach intervention on participant involvement in gun violence.

The past several years have witnessed increased calls for community violence interventions (CVIs) that address firearm violence while centering local expertise and avoiding the criminal legal system. Currently, little evidence exists on CVI effectiveness at the individual level. This study presents an evaluation of the impact of a street outreach-based CVI [Chicago CRED (Create Real Economic Destiny)] on participant involvement in violence. We used a quasiexperimental design with a treatment sample of 324 men recruited by outreach staff from 2016 to 2021 and a balanced comparison sample of 2,500 men from a network of individuals arrested in CRED's service areas. We conducted a Bayesian survival analysis to evaluate CRED's effect on individual violence-related outcomes on three levels of treatment: All enrolled participants, a subsample that made it through the initial phase, and those who completed programming. The intervention had a strong favorable effect on the probability of arrest for a violent crime for those completing the program: After 24 mo, CRED alumni experienced an 11.3 percentage point increase in survival rates of arrest for a violent crime relative to their comparisons (or, stated differently, a 73.4% reduction in violent crime arrests). The other two treatment levels experienced nontrivial declines in arrests but did not reach statistical significance. No statistically significant reduction in victimization risk was detected for any of the treatment levels. Results demonstrate that completion of violence intervention programming reduces the likelihood of criminal legal involvement for participants, despite the numerous systemic and environmental factors that impede personal success.

Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma

PurposeTo evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma. MethodsRetrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0−2) and poor prognosis (mRS 3–6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70−30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort. Results105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778−1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge. ConclusionThe use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

Specialties Providing Ambulatory Care and Associated HealthCare Utilization and Quality for Children With Medical Complexity.

Although children with medical complexity (CMC) have substantial health care needs, the extent to which they receive ambulatory care from primary care versus specialist clinicians is unknown. We aimed to determine the predominant specialty providing ambulatory care to CMC (primary care or specialty discipline), the extent to which specialists deliver well-child care, and associations between having a specialty predominant provider and health care utilization and quality. In a retrospective cohort analysis of 2012-17 all-payer claims data from Colorado, New Hampshire, and Massachusetts, we identified the predominant specialty providing ambulatory care for CMC <18 years. Propensity score weighting was used to create a balanced sample of CMC and assess differences in outcomes, including adequate well-child care, continuity of care, emergency visits, and hospitalizations, between CMC with a primary care versus specialty predominant provider. Among 67,218 CMC, 75.3% (n=50,584) received the plurality of care from a primary care discipline. Body system involvement, age >2 years, urban residence, and cooccurring disabilities were associated with predominantly receiving care from specialists. After propensity score weighting, there were no significant differences between CMC with a primary care or specialist "predominant specialty seen" (PSS) in ambulatory visit counts, adequate well-child care, hospitalizations, or overall continuity of care. Specialists were the sole providers of well-child care and vaccines for 49.9% and 53.1% of CMC with a specialist PSS. Most CMCreceived the plurality of care from primary care disciplines, and there were no substantial differences in overall utilization or quality based on the PSS.