Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal decomposition maps: An exploratory tool for designing area‐level interventions aimed at reducing health disparities

Methods for decomposition analyses have been developed to partition between‐group differences into explained and unexplained portions. In this paper, we introduce the concept of causal decomposition maps, which allow researchers to test the effect of area‐level interventions on disease maps before implementation. These maps quantify the impact of interventions that aim to reduce differences in health outcomes between groups and illustrate how the disease map might change under different interventions. We adapt a new causal decomposition analysis method for the disease mapping context. Through the specification of a Bayesian hierarchical outcome model, we obtain counterfactual small area estimates of age‐adjusted rates and reliable estimates of decomposition quantities. We present two formulations of the outcome model, with the second allowing for spatial interference of the intervention. Our method is utilized to determine whether the addition of gyms in different sets of rural ZIP codes could reduce any of the rural–urban difference in age‐adjusted colorectal cancer incidence rates in Iowa ZIP codes.
Cold season dose rate contributions from gamma, radon, thoron or progeny in legacy mines with high natural background radiation

Abstract In areas with high natural background radiation, underground cavities tend to have high levels of airborne radionuclides. Within mines, occupancy may involve significant exposure to airborne radionuclides like radon (222Rn), thoron (220Rn) and progeny. The Fen carbonatite complex in Norway has legacy mines going through bedrock with significantly elevated levels of uranium (238U) and especially thorium (232Th), and significant levels of their progeny 222Rn and 220Rn. There are also significantly elevated levels of gamma radiation in these mines. These mines are naturally chimney ventilated and release large volumes of air to the outdoors giving a large local outdoor impact. We placed alpha track detectors at several localities within these mines to measure airborne radionuclides and measured gamma radiation of bedrock at each locality. The bedrock within the mines shows levels up to 1900 Bq kg−1 for 238U, 12 000 Bq kg−1 for 232Th and gamma dose rates up to 11 μSv h−1. Maximum levels of airborne radionuclides were 45 000 Bq m−3 for 220Rn and 6900 Bq m−3 for 222Rn. In addition, we measured levels of thoron progeny (TnP). In order to estimate radiation dose contribution, TnP should be assessed rather than 220Rn, but deposition-based detectors may be biased by the airflow of mine-draft. We present dose rate contributions using UNSCEAR dose conversion factors, and correcting for airflow bias, finding a combined cold season dose rate within these mines of 17–24 μSv h−1. Interestingly, fractional dose rate contributions vary from 0.02 to 0.6 for gamma, 0.33 to 0.95 for radon and 0.1 to 0.25 for TnP.
Semi-Supervised Learning with Conditional Harmonic Mixing

Recently graph-based algorithms, in which nodes represent data points and links encode similarities, have become popular for semi-supervised learning. In this chapter we introduce a general probabilistic formulation called `Conditional Harmonic Mixing’, in which the links are directed, a conditional probability matrix is associated with each link, and where the numbers of classes can vary from node to node. The posterior class probability at each node is updated by minimizing the KL divergence between its distribution and that predicted by its neighbours. We show that for arbitrary graphs, as long as each unlabeled point is reachable from at least one training point, a solution always exists, is unique, and can be found by solving a sparse linear system iteratively. This result holds even if the graph contains loops, or if the conditional probability matrices are not consistent. We show how, given a classifier for a task, CHM can learn its transition probabilities. Using the Reuters database, we show that CHM improves the accuracy of the best available classifier, for small training set sizes.
Deep learning‐based body weight from scout images can be an alternative to actual body weight in CT radiation dose management

Abstract Purpose Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning‐based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management. Methods Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients. A deep learning model was developed to predict body weight from scout images. The correlation between actual and predicted body weight was analyzed. To validate the use of predicted body weight in radiation dose management, the volume CT dose index (CTDIvol) and the dose–length product (DLP) were compared between the body weight subgroups based on actual and predicted body weight. Surrogate size‐specific dose estimates (SSDEs) acquired from actual and predicted body weight were compared to the reference standard. Results The median actual and predicted body weight were 64.1 (interquartile range: 56.5–72.4) and 64.0 (56.3–72.2) kg, respectively. There was a strong correlation between actual and predicted body weight (ρ = 0.892, p < 0.001). The CTDIvol and DLP of the body weight subgroups were similar based on actual and predicted body weight (p < 0.001). Both surrogate SSDEs based on actual and predicted body weight were not significantly different from the reference standard (p = 0.447 and 0.410, respectively). Conclusion Predicted body weight can be an alternative to actual body weight in managing dose metrics and simplifying SSDE calculation. Our proposed method can be useful for CT radiation dose management in adult patients with unknown body weight.
Mass spectrometry-based untargeted metabolomics study of non-obese individuals with non-alcoholic fatty liver disease

Abstract Objectives Non-alcoholic fatty liver disease (NAFLD) is a disease characterized by the accumulation of excessive fat in the liver, which can lead to fibrosis and has an increasing prevalence. NAFLD requires non-invasive diagnostic biomarkers. While typically observed in overweight individuals, it can also occur in non-obese/non-overweight individuals. Comparative studies on non-obese NAFLD patients are scarce. This study aimed to conduct a using liquid chromatography-high resolution mass spectrometry (LC-MS/MS)-based metabolic profiling of non-obese NAFLD patients and healthy controls. Materials and methods The patient group consisted of 27 individuals with NAFLD, while the healthy control group included 39 individuals. Both groups were between 18 and 40 years old, had a BMI of less than 25 and had alcohol consumption less than 20 g/week for men and 10 g/week for women. Serum samples were collected and analyzed using LC-MS/MS. The data were analyzed using the TidyMass and MetaboAnalyst. Results The LC-MS/MS analyses detected significant changes in D-amino acid metabolism, vitamin B6 metabolism, apoptosis, mTOR signaling pathway, lysine degradation, and phenylalanine metabolism pathways in non-obese NAFLD patients. Significant changes were also observed in the metabolites D-pantothenic acid, hypoxanthine, citric acid, citramalic acid, L-phenylalanine, glutamine, and histamine-trifluoromethyl-toluidide, β-hydroxymyristic acid, DL-Lactic acid, and 3-methyl-2-oxopentanoic. Overall, the study provides valuable insights into the metabolic changes associated with non-obese NAFLD patients and can contribute to the development of non-invasive diagnostic biomarkers for NAFLD. Conclusions This study sheds light on the metabolic changes in non-obese NAFLD patients. Further research is needed to better understand the metabolic changes associated with NAFLD and to develop effective treatment options.
Comment on: Risk of myocardial infarction in Parkinson's disease: A systematic review and meta‐analysis

Dear Editor, We recently read an article published in the European Journal of Neurology by Nabizadeh et al. entitled, "Risk of Myocardial Infarction in Parkinson's Disease: A Systematic Review and MetaAnalysis. " The authors examined the risk of myocardial infarction in Parkinson's disease (PD) patients in the form of a systematic review and metaanalysis. In particular, the authors analyzed the possible association between PD and reduced risk of myocardial infarction from different perspectives and types through subgroup analyses [1]. However, we would like to review this article from the following three perspectives to further refine this important study. First, the authors did not mention trial preregistration information in the article. Preregistration of trials can be effective in improving transparency and avoiding potential biases, such as selective outcome reporting bias. Therefore, for an updated and unbiased review of the risk and rate of myocardial infarction in PD patients, the authors need to add registration information to this study. It is well known that the quality assessment of the included studies is an important component of a comprehensive metaanalysis. Poorquality studies can lead to bias in combined effect estimates. The authors state that the quality of all studies was assessed using the Newcastle– Ottawa Scale. To our knowledge, the currently used Newcastle– Ottawa Scale has not been published in a peerreviewed journal, and the only reference that can be found is a webbased link. However, the validity of the scale scores is unknown at best, and even invalid for some highquality items. Therefore, the use of the scores in systematic reviews and metaanalyses may lead to highly arbitrary results [2]. In this context, we suggest using the modified Down and Black tool to assess the methodological quality of nonrandomized studies. This tool provides a comprehensive assessment of all aspects of the study from a more detailed perspective [3]. The authors analyzed many results in their paper, but there was high heterogeneity in the total complications. The high heterogeneity of the outcome may make it difficult for the reader to trust the conclusions presented by the authors. Although the authors performed subgroup analyses based on possible specific confounders, we suggest using an alternative model— inverse variance heterogeneity (IVHet)— for highly heterogeneous outcome. This model can avoid, to some extent, the statistical errors of underestimation and false overestimation of random effects models [4]. Thus, we subsequently used the IVHet model to reanalyze the outcome in this metaanalysis; no statistically significant difference was detected for myocardial infarction in European patients with PD versus controls (odds ratio = 0.73, 95% confidence interval = 0.53– 1.00; Figure 1). In other words, the protective effect of PD on myocardial infarction seems to be overestimated. Therefore, highquality studies with larger sample sizes are still needed in the future to confirm these conclusions.
Pre-Injury Frailty and Clinical Care Trajectory of Geriatric Trauma Patients: A Retrospective Cohort Analysis of A Large Level I US Trauma Center

Background: Pre-injury frailty among older trauma patients is a predictor of increased morbidity and mortality. We sought to determine the relationship between frailty status and the care trajectories of older adult patients who underwent frailty screening in the emergency department (ED). Methods: Using a retrospective cohort design of a single institutional trauma database, we pooled data on trauma patients, 65 years and older, who had frailty screening at ED presentation (N=987). The predictor variable was frailty status, measured as either robust, pre-frail, or frail. The outcome variables were measures of clinical care trajectory: inpatient admission, length of hospital stay, home discharge, and discharge to rehabilitation. We controlled for age, sex, race/ethnicity, body mass index, Charlson Comorbidity Index, injury type and severity, and Glasgow Coma Scale score. We performed multivariable logistic and quantile regressions to measure the influence of frailty on post-trauma care trajectories. Results: The mean (SD) age of the study population was 81 (9.0) years and the population was predominantly female (66%) and non-Hispanic White (64%). Compared to older adult trauma patients classified as robust, those categorized as frail had 2.8 (95% CI: 1.75-4.40), 0.4 (95% CI: 0.27-0.63), and 2.1 (95% CI: 1.38-3.27) times the adjusted odds of hospital admission, home discharge, and discharge to rehabilitation, respectively. Those classified as pre-frail (Adjusted MD: 1.0; 95% CI: 0.46-1.54) and frail (Adjusted MD: 2.0; 95% CI: 1.35-2.65) had longer lengths of hospital stay compared to those classified as robust. Conclusion: Pre-injury frailty is a predictor of care trajectories for older-adult trauma patients
Procedures and Designs Useful for Screening Material in Selection and Allocation, with a Bibliography

This paper represents a brief review of results on screening. The references listed in the paper were selected to illustrate diversity of topics related to screening procedures with particular attention being paid to the results for plant and animal breeding screening programs and from truncation, fragmentation, and censoring of samples. Also, the large number of journals involved should be noted. In this form it is hoped that researchers involved in screening various drugs, pesticides, fumigants, herbicides, etc. (and also with students, enlistees, missiles, etc.) will become acquainted with the topics and some of the literature on screening in breeding fields, and vice versa. With this in mind, the similarities in biochemical and breeding programs were stressed. In particular, the developmental phase, the evaluation phase, and the production or maintenance phase are common to both fields. Many of the statistical results obtained in one field are applicable to the other. No discussion is given concerning unsolved statistical problems related to screening. This subject is discussed in papers listed at the end of the paper. Some comments are given on experimental designs useful for multistage screening experiments. Particular reference is made to the class of designs known as augmented designs.
Dense, 11 V-Tolerant, Balanced Stimulator IC with Digital Time-Domain Calibration for $< $100 nA Error

This article presents a multichannel neurostimulator implementing a novel charge balancing technique to achieve maximal integration. Safe neurostimulation demands accurate charge balancing of the stimulation waveforms to prevent charge build-up on the electrode-tissue interface. We propose digital time-domain calibration (DTDC), which adjusts the second phase of the biphasic stimulation pulses digitally, based on a one-time characterization of all stimulator channels with an on-chip ADC. Accurate control of the stimulation current amplitude is loosened in exchange for time-domain corrections, relieving circuit matching constraints and consequentially saving channel area. A theoretical analysis of DTDC is presented, establishing expressions for the required time resolution and the new, relaxed circuit matching constraints. To validate the DTDC principle, a 16-channel stimulator was implemented in 65 nm CMOS, requiring only 0.0141 mm$^{2}$ area/channel. Despite being implemented in a standard CMOS technology, 10.4 V compliance is achieved for compatibility with high-impedance microelectrode arrays typical for high-resolution neural prostheses. To the authors' knowledge, this is the first stimulator in a 65 nm low-voltage process achieving over 10 V output swing. Measurements after calibration show the DC error is successfully reduced below 96 nA on all channels. Static power consumption is 20.3 µW/channel.
Fear of cancer recurrence eight years after early-stage breast cancer – results from a national survey

Abstract Background Fear of cancer recurrence (FCR) in breast cancer survivors (BCSs) is common, associated with reduced quality of life and effective interventions exist. There are knowledge gaps concerning FCR among long-term, early-stage BCSs and its associations with other late effects. Within a national cohort, we explored these knowledge gaps, with the ultimate aim of improved care for BCSs experiencing long-term FCR. Methods In this cross-sectional study, all BCSs aged 20–65 years with early-stage breast cancer in 2011–2012 (n = 2803), were identified by the Cancer Registry of Norway in 2019 and mailed a survey including the Assessment of Survivor Concerns used to measure FCR. Factors associated with moderate/high FCR (defined as a sum score of ≥ 6 of a possible range 3–12, or a single score on one of the items of ≥ 3) were explored using a three-block regression analyses including relevant sociodemographic-, health- and cancer-related variables. Results In total, 1311 BCSs were included (47%). Median age at survey was 60 years. Fifty-six % reported moderate-to-high FCR, associated with younger age (OR 0.96, 95% CI 0.95–0.97) and receiving chemo- and endocrine therapy (OR 1.59, 95% CI 1.15–2.20). After adding late effects into the model, FCR remained significantly associated with these variables, in addition to sleep disturbances (OR 1.58, 95% CI 1.18–2.10). In the final block, adding mental distress, FCR remained significantly associated with younger age (OR 0.97, 95% CI 0.96–0.99), receiving chemo- and endocrine therapy (OR 1.14, 95% CI 1.00–1.97), sleep disturbances (OR 1.44, 95% CI 1.08–1.94) and anxiety (OR 2.67, 95% CI 1.38–5.19). Conclusions FCR was prevalent eight years after early-stage breast cancer. Being younger, receiving intensive treatment, experiencing sleep disturbances and/or anxiety were associated with moderate/high FCR. Addressing FCR should be part of standard follow-up care of long-term BCSs.
The Brief Case: a White-Colony-Producing Clostridioides difficile Ribotype 020 Strain

aSchool of Biomedical Sciences, The University of Western Australia, Perth, Western Australia, Australia bDepartment of Microbiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Salaya, Thailand cDepartment of Microbiology, PathWest Laboratory Medicine, Queen Elizabeth II Medical Centre, Nedlands, Western Australia, Australia dSchool of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia eCentre for Biosecurity and One Health, Harry Butler Institute, Murdoch University, Perth, Western Australia, Australia
Who makes in-play bets? Investigating the demographics, psychological characteristics, and gambling-related harms of in-play sports bettors

Abstract Background and aims Sports betting has increased markedly in recent years, in part due to legislative changes and the introduction of novel forms of sports betting (e.g., in-play betting). Some evidence suggests that in-play betting is more harmful than other types of sports betting (i.e., traditional and single-event). However, existing research on in-play sports betting has been limited in scope. To address this gap, the present study examined the extent to which demographic, psychological, and gambling-related constructs (e.g., harms) are endorsed by in-play sports bettors relative to single-event and traditional sports bettors. Methods Sports bettors (N = 920) aged 18+ from Ontario, Canada completed an online survey containing self-report measures of demographic, psychological, and gambling-related variables. Participants were classified as either in-play (n = 223), single-event (n = 533), or traditional bettors (n = 164) based on their sports betting engagement. Results In-play sports bettors reported higher problem gambling severity, endorsed greater gambling-related harms across several domains, and reported greater mental health and substance use difficulties compared to single-event and traditional sports bettors. There were generally no differences between single-event and traditional sports bettors. Discussion Results provide empirical support for the potential harms associated with in-play sports betting and inform our understanding of who may be at risk for increased harms associated with in-play betting. Conclusions Findings may be important for the development of public health and responsible gambling initiatives to reduce the potential harms of in-play betting, particularly as many jurisdictions globally move towards legalization of sports betting.
Clustering Effects on Performance and Heating of a Linear Aerospike Nozzle

Numerical and experimental investigations are conducted to identify the key aspects of the flowfield that effects the performance and heating of a linear aeropsike nozzle with clustered modules. The flowfield near the linear aerospike nozzle surface is characterized by the viscid-inviscid interaction between the jet and boundary layer over the nozzle. Whereas, the off-surface flowfield exhibits the inviscid features of the jet from the rectangular-exit module and the interaction between the jets from neighboring modules. The correlation between the flow mechanism and clustering effects on the performance and heating was analyzed, based on the knowledge of the flowfield. It is revealed that the major part of the performance loss results in the shock loss due to the inviscid jet interaction in the offsurface flowfield. On the other hand, the heat-flux distribution over the linear aerospike nozzle stems from the viscid-inviscid interaction near the nozzle surface.
Low-energy structures in strong-field ionization

I. A. Ivanov,1,2,* Chang Hee Nam,1,3 and Kyung Taec Kim1,3,† 1Center for Relativistic Laser Science, Institute for Basic Science, Gwangju 500-712, Republic of Korea 2Research School of Physics and Engineering, The Australian National University, Canberra ACT 0200, Australia 3Advanced Photonics Research Institute, GIST, Gwangju 500-712, Republic of Korea (Received 28 January 2016; revised manuscript received 14 March 2016; published 4 April 2016)
On the Moments of a Mixture of Uniform and Shifted Binomial random variables

Summary: In this note, we study the first four moments of the MUB random variable, that is a mixture of two discrete random variables, recently introduced for the fitting of ranks data models. After a brief review of the location and variability indexes, the paper derives and discusses the asymmetry and the kurtosis measures, investigating the shape properties of this distribution on the admissible par ametric space. Finally, the usefulness of the parameters moment estimators is shown in order to get starting values for the maximum likelihood estimation procedure.