Bayesian Regression Research Articles

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

<p>Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still little research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares several machine learning methods to predict hotspots in Kalimantan. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Some of the machine learning methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, several measures of sensitivity and feature importance used are variance-based, density-based, and distribution-based sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the ML model concluded that the Bayesian linear regression model outperformed other ML models, based on RMSE and explained variance score. Meanwhile, tree-based models, such as random forest and gradient boosting, are indicative of overfit. Based on the results of sensitivity analysis and feature importance, the number of dry days is the most important feature for the Bayesian linear regression model in predicting the number of hotspots in Kalimantan.</p>

Flexible instrumental variable models with Bayesian additive regression trees

Flexible instrumental variable models with Bayesian additive regression trees

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data

Functional generalized linear models (FGLM) have been widely used to study the relations between non-Gaussian response and functional covariates. However, most existing works for FGLM assume independence among observations and therefore they are of limited applicability for correlated data. A particularly important example is spatial functional data, where we observe functions over spatial domains, such as the age population curve or temperature curve at each areal unit. In this paper, we extend FGLM by incorporating spatial random effects. Especially, we study the relationship between the non-Gaussian response variable and functional covariates that are spatially observed. However, such model has computational and inferential challenges. The high-dimensional spatial random effects cause the slow mixing of Markov chain Monte Carlo (MCMC) algorithms. Furthermore, spatial confounding can lead to bias in parameter estimates and inflate their variances. To address these issues, we propose an efficient Bayesian method using a sparse reparameterization of high-dimensional random effects. We also study the average coverage probabilities of the credible intervals of functional parameters. We apply our methods to simulated and real data examples, including malaria incidence data and US COVID-19 data. The proposed method is fast while providing accurate functional estimates.

Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.

Economic diplomacy: Analyzing theories and models for informed policy

This paper examines the intricate relationship between economic theories, trade policies, and diplomatic engagements in the context of international relations. Utilizing theoretical frameworks such as the theory of comparative advantage and the Heckscher-Ohlin model, we analyze the foundational principles guiding global trade and policy-making. We explore the role of probabilistic models, including Bayesian statistics and regression analysis, in forecasting economic trends and their implications for international relations. Furthermore, we investigate the impact of trade policies, including tariffs, non-tariff barriers, and global trade agreements, on economic dynamics and diplomatic interactions. Through case studies of economic sanctions, we assess the effectiveness of sanctions as a diplomatic tool and propose policy recommendations for their optimal implementation. Our analysis highlights the complexity of economic diplomacy and underscores the importance of quantitative analysis in informing policy decisions and fostering international cooperation.

A Prospective Cohort Study of Persistent Endocrine Disrupting Chemicals and Perceived Stress.

Persistent endocrine disrupting chemicals (EDCs) can dysregulate the stress response. We evaluated associations between persistent EDCs and perceived stress among participants from the Study of Environment, Lifestyle and Fibroids (n=1,394), a prospective cohort study of Black women. Participants completed the Perceived Stress Scale (PSS-4) at baseline, and every 20 months through 60 months (range of scores: 0-16); higher scores indicated higher stress. EDCs, including per- and polyfluoroalkyl substances (PFAS), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides, were quantified in plasma samples at baseline. We fit Bayesian Kernel Machine Regression (BKMR) and linear mixed effects models to estimate associations of EDCs (as a mixture and individually) with PSS-4 scores at baseline and at each follow-up visit, respectively. Increasing percentiles of the mixture were not strongly associated with PSS-4 scores at baseline, and no interactions were observed among EDCs. Several individual EDCs (e.g., PFDA, PCB 118, PBDE 99) were associated with higher PSS-4 scores at baseline or follow-up, while other EDCs (e.g., PCB 138/158) were associated with lower PSS-4 scores at baseline or follow-up. The directionality of associations for individual EDCs was inconsistent across follow-up visits. In conclusion, specific EDCs may be associated with perceived stress in Black women.

Banking with purpose: the impact of CSR-S on customer behavior during the COVID-19

PurposeThis article examines the impact of banks’ corporate social responsibility communication through social media (CSR-S), electronic word of mouth (eWOM), and brand reputation on consumer behavior during the COVID-19 crisis, with a focus on purchase intention.Design/methodology/approachThe study employed a quantitative approach to analyze data from a survey of 621 Egyptian bank customers who followed the banks’ social media pages and interacted with CSR-S initiatives. A genetic algorithm selected the most relevant variables affecting purchase intention. A Bayesian regression model was used to analyze the impact of CSR-S communication, eWOM, and brand reputation on purchase intention.FindingsCSR-S initiatives, eWOM, and brand reputation were found to influence customer purchase intention. CSR-S initiatives can boost purchase intention by encouraging brand reputation and initiative sharing with friends and other customers. However, CSR-S negatively moderates the positive impact of eWOM and brand reputation on the predisposition to contract products and services with the bank.Originality/valueThis study addresses critical research gaps in CSR literature. Firstly, it examines the impact of CSR-S actions on customer behavior, a perspective less explored in previous research. Secondly, it investigates the intricate relationships between CSR-S, eWOM, brand reputation, and purchase intention, shedding light on their interplay, particularly during the COVID-19 pandemic. Additionally, this research extends CSR-S investigations to the competitive banking industry and focuses on a developing country context, enhancing the applicability of findings for Egyptian banks. Lastly, the study employs advanced methodologies to improve the accuracy of results.

Bayesian dynamic modelling for probabilistic prediction of pavement condition

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0.86, 1.52, and 2.27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability.

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco.

Chagas disease, caused by Trypanosoma cruzi, is still a public health problem in Latin America and in the Southern Cone countries, where Triatoma infestans is the main vector. We evaluated the relationships among the density of green vegetation around rural houses, sociodemographic characteristics, and domestic (re)infestation with T. infestans while accounting for their spatial dependence in the municipality of Pampa del Indio between 2007 and 2016. The study comprised sociodemographic and ecological variables from 734 rural houses with no missing data. Green vegetation density surrounding houses was estimated by the normalized difference vegetation index (NDVI). We used a hierarchical Bayesian logistic regression composed of fixed effects and spatial random effects to estimate domestic infestation risk and quantile regressions to evaluate the association between surrounding NDVI and selected sociodemographic variables. Qom ethnicity and the number of poultry were negatively associated with surrounding NDVI, whereas overcrowding was positively associated with surrounding NDVI. Hierarchical Bayesian models identified that domestic infestation was positively associated with surrounding NDVI, suitable walls for triatomines, and overcrowding over both intervention periods. Preintervention domestic infestation also was positively associated with Qom ethnicity. Models with spatial random effects performed better than models without spatial effects. The former identified geographic areas with a domestic infestation risk not accounted for by fixed-effect variables. Domestic infestation with T. infestans was associated with the density of green vegetation surrounding rural houses and social vulnerability over a decade of sustained vector control interventions. High density of green vegetation surrounding rural houses was associated with households with more vulnerable social conditions. Evaluation of domestic infestation risk should simultaneously consider social, landscape and spatial effects to control for their mutual dependency. Hierarchical Bayesian models provided a proficient methodology to identify areas for targeted triatomine and disease surveillance and control.

Type I Tobit Bayesian Additive Regression Trees for censored outcome regression

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore, the error is flexibly modeled as a Dirichlet process mixture of normal distributions. An R package is available at https://github.com/EoghanONeill/TobitBART.

P.081 Chordoma management with artificial intelligence: a scoping review of current applications and future prospects

Background: Chordomas are rare, malignant bone tumors that present significant challenges in management and treatment due to their complex anatomical locations and propensity for recurrence. Advancements in artificial intelligence (AI) and machine learning (ML) show promise in improving chordoma management. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including MEDLINE, Cochrane, Embase, Scopus, and Web of Science. The search targeted articles related to AI and ML applications in clinical tasks associated with chordoma management. The selection process involved systematic screening, data extraction, and assessment of inter-rater variability. Results: The search yielded 1,006 records, with 18 included for analysis. Convolutional neural networks (CNNs) excelled in tumor volume estimation, with the state-of-the-art model achieving a Dice similarity score of 74.2%, sensitivity of 79.4%, and positive predictive value of 74.3%. Clustering algorithms were effective in prognostic evaluations. Bayesian models and logistic regression demonstrated robustness in diagnostics. Support vector machines (SVMs) were noted for their diagnostic precision. Conclusions: AI and ML algorithms, particularly CNNs, clustering algorithms, Bayesian models, logistic regression, and SVMs, show promise in improving chordoma management through enhanced imaging, diagnostics, and prognostics. Future research should focus on larger, externally validated datasets and explore underutilized techniques like multi-modal data integration.

Association Between Exposure to Multiple Toxic Metals in Follicular Fluid and the Risk of PCOS Among Infertile Women: The Mediating Effect of Metabolic Markers.

Polycystic ovary syndrome (PCOS) severely affects women's fertility and accompanies serious metabolic disturbances, affecting 5%-20% of women of reproductive age globally. We previously found that exposure to toxic metals in the blood raised the risk of PCOS, but the association between exposure to toxic metals and the risk of PCOS in the follicular fluid, the microenvironment for oocyte growth and development in females, and its effect on metabolism has not been reported. This study aimed to evaluate the associations between the concentrations of cadmium (Cd), mercury (Hg), barium (Ba) and arsenic (As) in FF and the risk of PCOS, and to explore the mediating effect of metabolic markers in FF on the above relationship. We conducteda case-control study, including 557 women with PCOS and 651 controls. Ba, Cd, Hg and As levels in FF were measured by ICP-MS, metabolites levels in FF was measured by LC-MS/MS among 168 participants randomly selected from all the participants. Logistic regression models were used to assess the association of a single metal level with the PCOS risk, and linear regression models were used to assess the relationships of a single metal level with clinical phenotype parameters and metabolites levels. Combined effect of metals mixture levels on the risk of PCOS were assessed via weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR). Medication analysis was performed to explore the role of metabolic markers on the relationship of toxic metals levels with the risk of PCOS. The exposure levels of Cd, Hg, Ba and As in FF were all positively and significantly associated with the PCOS risk (with respect to the highest vs. lowest tertile group: OR = 1.57, 95% CI = 1.17 ~ 2.12 for Cd, OR = 1.69, 95% CI = 1.22 ~ 2.34 for Hg, OR = 1.76, 95% CI = 1.32 ~ 2.34 for Ba, OR = 1.42, 95% CI = 1.05 ~ 1.91 for As). In addition, levels of metal mixture also significantly correlated with the risk of PCOS, Cd level contributed most to it. Moreover, we observed significant positive relationships between Cd level and LH (β = 0.048, 95% CI = 0.002 ~ 0.094), T (β = 0.077, 95% CI = 0.029 ~ 0.125) and HOMA-IR value (β = 0.060, 95% CI = 0.012 ~ 0.107), as well as Hg level with LH, FSH/LH ratio and TC. Furthermore, we revealed that estrone sulfate, LysoPE 22:6 and N-Undecanoylglycine were significantly and positively mediating the association between Cd level and the risk of PCOS (with mediated proportion of 0.39, 0.24 and 0.35, respectively), and between Hg level and the risk of PCOS (with mediated proportion of 0.29, 0.20 and 0.46, respectively). These highly expressed metabolites significantly enriched in the fatty acid oxidation, steroid hormone biosynthesis and glycerophospholipids metabolism, which may explain the reason why the levels of Cd and Hg in FF associated with the phenotype of PCOS. Ba and As in FF was not found the above phenomenon. Our results suggested that exposure to multiple toxic metals (Cd, Hg, Ba and As) in FF associated with the increased risk of PCOS, Cd was a major contributor. Levels of Cd and Hg in FF significantly associated with the phenotype of PCOS. The above association may result from that Cd and Hg in FF related with the disturbance of fatty acid oxidation, steroid hormone biosynthesis and the glycerophospholipids metabolism.

Bayesian causal forests for multivariate outcomes: application to Irish data from an international large scale education assessment

Abstract Bayesian Causal Forests (BCF) is a causal inference machine learning model based on the flexible non-parametric regression and classification tool, Bayesian Additive Regression Trees (BART). Motivated by data from the Trends in International Mathematics and Science Study (TIMSS), which includes data on student achievement in both mathematics and science, we present a multivariate extension of the BCF algorithm. With the help of simulation studies, we show that our approach can accurately estimate causal effects for multiple outcomes subject to the same treatment. We apply our model to Irish data from TIMSS 2019. Our findings reveal the positive effects of having access to a study desk at home (Mathematics ATE 95% CI: [−0.50, 10.14]) while also highlighting the negative consequences of students often feeling hungry at school (Mathematics ATE 95% CI: [−8.86, −1.56] , Science ATE 95% CI: [−10.35, −0.94]) or often being absent (Mathematics ATE 95% CI: [−11.88, −2.27]). Code for replicating the results can be found at https://github.com/Nathan-McJames/MVBCF-Paper.

Predictors of quality of life improvement in patients undergoing catheter ablation for atrial fibrillation: a cluster-based analysis

Abstract Introduction Traditional classification of Atrial Fibrillation (AF) based on temporal patterns doesn't fully encompass the condition's complexity. Unsupervised cluster analyses have been used for classifying patients into groups with similar comorbid profiles. However, inconsistent comorbidity patterns have been found across different cohorts. Bayesian profile regression, a semi-supervised machine learning technique, may improve the precision of clustering by integrating outcome data, diminishing the variability observed with previous methods. Since quality of life (QoL) primarily drives the indication for AF ablation, we utilized the AF Effect on QualiTy-of-Life (AFEQT) questionnaire to cluster patients. Purpose Our aim is to use patient-reported outcome measures to identify clinically relevant phenotypes of AF that benefit the most from catheter ablation. Methods We implemented a single center follow-up (FUP) program for patients with AF referred for ablation since august 2020, including scheduled visits and remote monitoring through a digital health platform. AFEQT score reported by patients was analyzed using a non-linear mixed model and change in AFEQT by month 12 was used as outcome. Comorbidities, echocardiographic data, and biomarkers were collected. Profile regression mixture modelling guided by QoL improvement was performed to create clinically relevant patient groupings. Penalized multinomial logistic regression model was used for validation. Results From the 466 ablations performed, 354 patients were enrolled until July 2023. 211 patients had FUP > 12 months (age 59±10 years, 31% female, 80% paroxysmal). The overall AFEQT questionnaire completeness rate was 72%. Patients were classified into 3 clusters: 1) patients with long duration AF, left heart cavities enlargement and diastolic dysfunction (n=57); 2) patients with self-reported anxiety/depression (n=50); and 3) patients with low rates of comorbidities (n=104). For clinical application, 8 variables (dyslipidemia, AF type and duration since diagnosis, mitral E/A ratio, systolic pulmonary arterial pressure, left ventricle ejection fraction, and PROMIS T-score for anxiety and depression) were sufficient for classifying patients (85% accuracy in internal cross-validation). Patients with higher depressive and anxiety burden (cluster 2) had worse baseline AFEQT score (median: 46, IQR: 39-60) and derived less benefit (median improvement: 21, IQR: 12-32), while patients with low comorbid burden (cluster 3) displayed higher baseline (median: 58, IQR: 48-62) and final QoL (median improvement: 28, IQR: 21-36, p<0.01). Conclusion Our analysis identified 3 reproducible clinically relevant phenotypes of AF using widely available clinical data. These clusters have distinct associations with QoL, underscoring the heterogeneity of AF and importance of comorbidities and substrates. This approach may allow for the prediction of which patients are most likely to benefit from treatment.

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

Factors associated with renal oxygen extraction in mechanically ventilated children after the Norwood operation: insights from high fidelity haemodynamic data.

Maintaining the adequacy of systemic oxygen delivery is of utmost importance, particularly in critically ill children. Renal oxygen extraction can be utilised as metric of the balance between systemic oxygen delivery and oxygen consumption. The primary aim of this study was to determine what clinical factors are associated with renal oxygen extraction in children after Norwood procedure. Mechanically ventilated children who underwent Norwood procedure from 1 September, 2022 to 1 March, 2023 were identified as these patients had data collected and stored with high fidelity by the T3 software. Data regarding haemodynamic values, fluid balance, and airway pressure were collected and analysed using Bayesian regression to determine the association of the individual metrics with renal oxygen extraction. A total of 27,270 datapoints were included in the final analyses. The resulting top two models explained had nearly 80% probability of being true and explained over 90% of the variance in renal oxygen extraction. The coefficients for each variable retained in the best were -1.70 for milrinone, -19.05 for epinephrine, 0.129 for mean airway pressure, -0.063 for mean arterial pressure, 0.111 for central venous pressure, 0.093 for arterial saturation, 0.006 for heart rate, -0.025 for respiratory rate, 0.366 for systemic vascular resistance, and -0.032 for systemic blood flow. Increased milrinone, epinephrine, mean arterial pressure, and systemic blood flow were associated with decreased (improved) renal oxygen extraction, while increased mean airway pressure, central venous pressure, arterial saturation, and systemic vascular resistance were associated with increased (worsened) renal oxygen extraction.

Bayesian effect selection in structured additive quantile regression

Bayesian structured additive quantile regression is an established tool for regressing outcomes with unknown distributions on a set of explanatory variables and/or when interest lies with effects on the more extreme values of the outcome. Even though variable selection for quantile regression exists, its scope is limited. We propose the use of the Normal Beta Prime Spike and Slab (NBPSS) prior in Bayesian quantile regression to aid the researcher in not only variable but also effect selection. We compare the Bayesian NBPSS approach to statistical boosting for quantile regression, a current standard in automated variable selection in quantile regression, in a simulation study with varying degrees of model complexity and illustrate both methods on an example of childhood malnutrition in Nigeria. The NBPSS prior shows good performance in variable and effect selection as well as prediction compared to boosting and can thus be recommended as an additional tool for quantile regression model building.

Exploring the Multidimensional Characteristics of Selected and Non-Selected White British and British South Asian Youth Cricketers: An Exploratory Machine Learning Approach

Selection into a County Age Group (CAG; under 10–18) programme is the first step for young aspiring cricketers on their journey to achieving professional status. Recognising that the British South Asian (BSA) community represents 30% of those who play recreational cricket compared to less than 5% of those who are selected to play at the professional level in England and Wales, it is important to better understand the characteristics of selected and non-selected players based on ethnicity to identify potential sociocultural differences during selection. Thus, the purpose of this study was to investigate the multidimensional factors that differentiated between selected and non-selected adolescent male cricketers as well as between White British (WB) and BSA selected players into a CAG programme. A total of 82 male participants aged between 14 and 17 years were included (mean = 15.3 ± 1.1 years: selected n = 33 and non-selected n = 49: WB n = 34, BSA n = 44, Other n = 4). In total, 104 characteristics were measured over nine tests, which were subsequently placed into five overarching factors: (a) physiological and anthropometrical, (b) perceptual–cognitive expertise, (c) psychological, (d) participation history, and (e) socio-cultural influences. A Bayesian binomial regression was performed in rSTAN using a weak normal prior to highlight differentiators in selection as well as differences between WB and BSA selected players. The results highlighted that athleticism, wellbeing and cohesion, the number of older brothers, and being born in birth quarters two and three were positively correlated with player selection into a CAG. Conversely, increases in psychological scores, a greater number of younger brothers and older sisters, as well as antisocial behaviour resulted in a reduced chance of player selection. Finally, several developmental factors (i.e., athleticism, wellbeing and cohesion, psychological distress, and levels of anti-social behaviour) differed based on ethnicity. These exploratory findings serve as a useful opening to highlight there are important differences to consider based on selection and ethnicity in CAG cricket.

Subkiloparsec Scaling Relations between Hot Gas, Dense Gas, and Star Formation Rate in Five Nearby Star-forming Galaxies

Based on the newly acquired dense gas observations from the JCMT MALATANG survey and X-ray data from Chandra, we explore the correlation between hot gas and HCN J = 4 → 3 and HCO+ J = 4 → 3 emission for the first time at subkiloparsec scale of five nearby star-forming galaxies, namely M82, M83, IC 342, NGC 253, and NGC 6946. We find that both HCN J = 4 → 3 and HCO+ J = 4 → 3 line luminosity show a statistically significant correlation with the 0.5−2 keV X-ray emission of the diffuse hot gas ( L0.5−2keVgas ). The Bayesian regression analysis gives the best fit of log(L0.5−2keVgas / ergs−1)=2.39log(LHCN(4−3)′ /K km s−1 pc2) + 24.83 and log(L0.5−2keVgas / ergs−1)=2.48log(LHCO+(4−3)′ /K km s−1 pc2) + 23.84, with dispersion of ∼0.69 dex and 0.54 dex, respectively. At the subkiloparsec scale, we find that the power-law index of the L0.5−2keVgas −star formation rate (SFR) relation is log(L0.5−2keVgas/ergs−1)=1.80log(SFR/M⊙yr−1)+39.16 , deviated from previous linear relations at the global scale. This implies that the global property of hot gas significantly differs from individual resolved regions, which is influenced by the local physical conditions close to the sites of star formation.

Absence of Evidence for Sustained Effects of Daily Cannabidiol Administration on Anandamide Plasma Concentration in Individuals with Cocaine Use Disorder: Exploratory Findings from a Randomized Controlled Trial.

Background: Cannabidiol (CBD) has been proposed to have a therapeutic potential over a wide range of neuropsychiatric disorders, including substance use disorders. Pre-clinical evidence suggests that CBD can increase anandamide (AEA) plasma concentration, possibly mediating some of its therapeutic properties. Whether CBD exerts such an effect on AEA in individuals with cocaine use disorder (CUD) remains unknown. Aims: To explore the sustained effects of daily CBD administration on AEA plasma concentrations compared with placebo in CUD. Methods: We used data from a randomized, double-blind, placebo-controlled trial evaluating CBD's efficacy in CUD. Seventy-eight individuals were randomized to receive a daily oral dose of 800 mg CBD (n = 40) or a placebo (n = 38). Participants stayed in an inpatient detoxification setting for 10 days, after which they were followed in an outpatient setting for 12 weeks. AEA plasma concentration was measured at baseline and at 23-h post CBD ingestion on day 8 and week 4. A generalized estimating equation model was used to assess CBD's effects on AEA, and sensitivity analyses were computed using Bayesian linear regressions. Results: Sixty-four participants were included in the analysis. Similar mean AEA plasma concentrations in both treatment groups (p = 0.357) were observed. At day 8, mean AEA plasma concentrations (± standard deviation) were 0.26 (± 0.07) ng/mL in the CBD group and 0.29 (± 0.08) ng/mL in the placebo group (p = 0.832; Bayes factor [BF] = 0.190). At week 4, they were 0.27 (± 0.09) ng/mL in the CBD group and 0.30 (± 0.09) ng/mL in the placebo group (p = 0.181; BF = 0.194). Conclusion: While not excluding any potential acute and short-term effect, daily CBD administration did not exert a sustained impact on AEA plasma concentrations in individuals with CUD compared with placebo. Registration: clinicaltrials.gov (NCT02559167).