Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Increased Pneumonia Risk Associated with Concomitant Use of Inhaled Corticosteroids and Benzodiazepines: A Pharmacovigilance Analysis

BACKGROUND

Inhaled corticosteroids (ICS) are effective in managing asthma and chronic obstructive pulmonary disease (COPD) but increase the risk of pneumonia. Benzodiazepines (BZD), commonly prescribed for comorbid psychiatric disorders in asthma or COPD patients, are also associated with pneumonia. This study investigates the risk of pneumonia associated with the concomitant use of ICS and BZD.

METHODS

Data from the FDA Adverse Event Reporting System from Q4 2013 to Q3 2023 were extracted. Reports involving asthma or COPD patients were included. Disproportionality analysis and logistic regression analysis were performed to assess the risk of pneumonia associated with the combined use of ICS and BZD. Additive and multiplicative models were used to further confirm the results. Additionally, subgroup analyses were conducted based on gender, age, and disease type.

RESULTS

A total of 238,411 reports were included. The combined use of ICS and BZD was associated with a higher reporting of pneumonia (ROR: 2.41, 95% CI 2.25-2.58). Using additive and multiplicative methods, the results remained significant. The strongest risk signals were observed in specific drug combinations, such as mometasone with clonazepam, budesonide with temazepam, and mometasone with zopiclone. Subgroup analyses showed higher pneumonia risks in females, patients over 60 years old, and those with asthma.

CONCLUSION

Our findings identified a significantly elevated pneumonia risk with the combined use of ICS and BZD. These results highlighted the necessity for cautious co-prescription of ICS and BZD and suggested the need for more comprehensive clinical studies to assess this interaction.

Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases

Many dominant genetic disorders result from protein-altering mutations, acting primarily through dominant-negative (DN), gain-of-function (GOF), and loss-of-function (LOF) mechanisms. Deciphering the mechanisms by which dominant diseases exert their effects is often experimentally challenging and resource intensive, but is essential for developing appropriate therapeutic approaches. Diseases that arise via a LOF mechanism are more amenable to be treated by conventional gene therapy, whereas DN and GOF mechanisms may require gene editing or targeting by small molecules. Moreover, pathogenic missense mutations that act via DN and GOF mechanisms are more difficult to identify than those that act via LOF using nearly all currently available variant effect predictors. Here, we introduce a tripartite statistical model made up of support vector machine binary classifiers trained to predict whether human protein coding genes are likely to be associated with DN, GOF, or LOF molecular disease mechanisms. We test the utility of the predictions by examining biologically and clinically meaningful properties known to be associated with the mechanisms. Our results strongly support that the models are able to generalise on unseen data and offer insight into the functional attributes of proteins associated with different mechanisms. We hope that our predictions will serve as a springboard for researchers studying novel variants and those of uncertain clinical significance, guiding variant interpretation strategies and experimental characterisation. Predictions for the human UniProt reference proteome are available at https://osf.io/z4dcp/.

Antenatal cardiotocography in primary midwife-led care: a budget impact analysis

OBJECTIVES

In many countries, the healthcare sector is dealing with important challenges such as increased demand for healthcare services, capacity problems in hospitals and rising healthcare costs. Therefore, one of the aims of the Dutch government is to move care from in-hospital to out-of-hospital care settings. An example of an innovation where care is moved from a more specialised setting to a less specialised setting is the performance of an antenatal cardiotocography (aCTG) in primary midwife-led care. The aim of this study was to assess the budget impact of implementing aCTG for healthy pregnant women in midwife-led care compared with usual obstetrician-led care in the Netherlands.

METHODS

A budget impact analysis was conducted to estimate the actual costs and reimbursement of aCTG performed in midwife-led care and obstetrician-led care (ie, base-case analysis) from the Dutch healthcare perspective. Epidemiological and healthcare utilisation data describing both care pathways were obtained from a prospective cohort, survey and national databases. Different implementation rates of aCTG in midwife-led care were explored. A probabilistic sensitivity analysis was conducted to estimate the uncertainty surrounding the budget impact estimates.

RESULTS

Shifting aCTG from obstetrician-led care to midwife-led-care would increase actual costs with €311 763 (97.5% CI €188 574 to €426 072) and €1 247 052 (97.5% CI €754 296 to €1 704 290) for implementation rates of 25% and 100%, respectively, while it would decrease reimbursement with -€7 538 335 (97.5% CI -€10 302 306 to -€4 559 661) and -€30 153 342 (97.5% CI -€41 209 225 to -€18 238 645) for implementation rates of 25% and 100%, respectively. The sensitivity analysis results were consistent with those of the main analysis.

CONCLUSIONS

From the Dutch healthcare perspective, we estimated that implementing aCTG in midwife-led care may increase the associated actual costs. At the same time, it might lower the healthcare reimbursement.

Language and reading in attention-deficit/hyperactivity disorder and comorbid attention-deficit/hyperactivity disorder + developmental language disorder

Background

The current study sought to examine whether psycholinguistic assessments could discriminate children and adolescents with developmental language disorder (DLD) from those with attention-deficit/hyperactivity disorder (ADHD; combined or inattentive subtype) and comorbid DLD + ADHD.

Methods

The Clinical Evaluation of Language Fundamentals-Screening Test (CELFST; Wiig et al., 2013), the Comprehensive Test of Phonological Processing (nonword repetition subtest; Wagner et al., 2013), and the Test of Word Reading Efficiency (sight word and phonemic decoding subtests; Torgesen et al., 2012) were examined in 441 children and adolescents between 6 and 16 years of age.

Results

The presence of a language disorder (with or without ADHD) predicted poor performance across tasks. Children and adolescents with ADHD (combined vs. inattentive) only significantly differed in sight word reading, in favor of those with combined type. Measures of reading efficiency could distinguish between the two types of ADHD, but not between other groups. Interestingly, scores on the standard language screener were no worse for children with ADHD + DLD than children with DLD only.

Conclusions

The combination of comorbid ADHD + DLD did not appear to be associated with lower language abilities, sight word reading, or phonemic decoding relative to DLD alone. Reading efficiency was effective in discriminating between ADHD subtypes. These findings offer valuable insights into differential diagnosis and the identification of comorbidity.

Meta-analysis review for pilot and large-scale constructed wetlands: Design parameters, treatment performance, and influencing factors

Despite their longstanding use in environmental remediation, constructed wetlands (CWs) are still topical due to their sustainable and nature-based approach. While research and review publications have grown annually by 7.5 % and 37.6 %, respectively, from 2018 to 2022, a quantitative meta-analysis employing advanced statistics and machine learning to assess CWs has not yet been conducted. Further, traditional statistics of mean ± standard deviation could not convey the extent of confidence or uncertainty in results from CW studies. This study employed a 95 % bootstrap-based confidence interval and out-of-bag Random Forest-based driver analysis on data from 55 studies, totaling 163 cases of pilot and full-scale CWs. The study recommends, with 95 % confidence, median surface hydraulic loading rates (HLR) of 0.14 [0.11, 0.17] m/d for vertical flow-CWs (VF) and 0.13 [0.07, 0.22] m/d for horizontal flow-CWs (HF), and hydraulic retention time (HRT) of 125.14 [48.0, 189.6] h for VF, 72.00 [42.00, 86.28] h for HF, as practical for new CW design. Permutation importance results indicate influent COD impacted primarily on COD removal rate at 21.58 %, followed by HLR (16.03 %), HRT (12.12 %), and substrate height (H) (10.90 %). For TN treatment, influent TN and COD were the most significant contributors at 12.89 % and 10.01 %, respectively, while H (9.76 %), HRT (9.72 %), and HLR (5.87 %) had lower impacts. Surprisingly, while HRT and H had a limited effect on COD removal, they substantially influenced TN. This study sheds light on CWs' performance, design, and control factors, guiding their operation and optimization.

First-Attempt Success Between Anatomically and Physiologically Difficult Airways in the National Emergency Airway Registry

BACKGROUND

In the emergency department (ED), certain anatomical and physiological airway characteristics may predispose patients to tracheal intubation complications and poor outcomes. We hypothesized that both anatomically difficult airways (ADAs) and physiologically difficult airways (PDAs) would have lower first-attempt success than airways with neither in a cohort of ED intubations.

METHODS

We performed a retrospective, observational study using the National Emergency Airway Registry (NEAR) to examine the association between anticipated difficult airways (ADA, PDA, and combined ADA and PDA) vs those without difficult airway findings (neither ADA nor PDA) with first-attempt success. We included adult (age ≥14 years) ED intubations performed with sedation and paralysis from January 1, 2016 to December 31, 2018 using either direct or video laryngoscopy. We excluded patients in cardiac arrest. The primary outcome was first-attempt success, while secondary outcomes included first-attempt success without adverse events, peri-intubation cardiac arrest, and the total number of airway attempts. Mixed-effects models were used to obtain adjusted estimates and confidence intervals (CIs) for each outcome. Fixed effects included the presence of a difficult airway type (independent variable) and covariates including laryngoscopy device type, intubator postgraduate year, trauma indication, and patient age as well as the site as a random effect. Multiplicative interaction between ADAs and PDAs was assessed using the likelihood ratio (LR) test.

RESULTS

Of the 19,071 subjects intubated during the study period, 13,938 were included in the study. Compared to those without difficult airway findings (neither ADA nor PDA), the adjusted odds ratios (aORs) for first-attempt success were 0.53 (95% CI, 0.40-0.68) for ADAs alone, 0.96 (0.68-1.36) for PDAs alone, and 0.44 (0.34-0.56) for both. The aORs for first-attempt success without adverse events were 0.72 (95% CI, 0.59-0.89) for ADAs alone, 0.79 (0.62-1.01) for PDAs alone, and 0.44 (0.37-0.54) for both. There was no evidence that the interaction between ADAs and PDAs for first-attempt success with or without adverse events was different from additive (ie, not synergistic/multiplicative or antagonistic).

CONCLUSIONS

Compared to no difficult airway characteristics, ADAs were inversely associated with first-attempt success, while PDAs were not. Both ADAs and PDAs, as well as their interaction, were inversely associated with first-attempt success without adverse events.

Nitrogen dioxide exposure, health outcomes, and associated demographic disparities due to gas and propane combustion by U.S. stoves

Gas and propane stoves emit nitrogen dioxide (NO2) pollution indoors, but the exposures of different U.S. demographic groups are unknown. We estimate NO2 exposure and health consequences using emissions and concentration measurements from >100 homes, a room-specific indoor air quality model, epidemiological risk parameters, and statistical sampling of housing characteristics and occupant behavior. Gas and propane stoves increase long-term NO2 exposure 4.0 parts per billion volume on average across the United States, 75% of the World Health Organization's exposure guideline. This increased exposure likely causes ~50,000 cases of current pediatric asthma from long-term NO2 exposure alone. Short-term NO2 exposure from typical gas stove use frequently exceeds both World Health Organization and U.S. Environmental Protection Agency benchmarks. People living in residences <800 ft2 in size incur four times more long-term NO2 exposure than people in residences >3000 ft2 in size; American Indian/Alaska Native and Black and Hispanic/Latino households incur 60 and 20% more NO2 exposure, respectively, than the national average.

Does the introduction of an infliximab biosimilar always result in savings for hospitals? A descriptive study using real-world data

BACKGROUND

Biosimilars are biologic drugs that have the potential to increase the efficiency of healthcare spending and curb drug-related cost increases. However, their introduction into hospital formularies through initiatives such as non-medical switching must be carefully orchestrated so as not to cause treatment discontinuation or result in increased health resource utilization, such as additional visits or laboratory tests, among others. This retrospective cohort study aims to assess the impact of the introduction of CT-P13 on the healthcare expenditures of patients who were treated with originator infliximab or CT-P13.

METHODS

Gastroenterology, immunoallergology and rheumatology patients treated between September 2017 and December 2020 at a university hospital in Western Switzerland were included and divided into seven cohorts, based on their treatment pathway (i.e., use and discontinuation of CT-P13 and/or originator infliximab). Costs in Swiss francs were obtained from the hospital's cost accounting department and length of stay was extracted from inpatient records. Comparisons of costs and length of stay between cohorts were calculated by bootstrapping.

RESULTS

Sixty immunoallergology, 84 rheumatology and 114 gastroenterology patients were included. Inpatient and outpatient costs averaged (sd) CHF 1,611 (1,020) per hospital day and CHF 4,991 (6,931) per infusion, respectively. The mean (sd) length of stay was 20 (28) days. Although immunoallergology and rheumatology patients had higher average costs than gastroenterology patients, differences in costs and length of stay were not formally explained by treatment pathway. Differences in health resource utilization were marginal.

CONCLUSIONS

The introduction of CT-P13 and the disruption of patient treatment management were not associated with differences in average outpatient and inpatient costs and length of stay, in contrast to the results reported in the rest of the literature. Future research should focus on the cost-effectiveness of non-medical switching policies and the potential benefits for patients.

Study protocol of a randomized controlled trial to assess the efficacy of the "PrEPare for Work" intervention to enhance PrEP uptake and optimize adherence for HIV prevention among male sex workers in the U.S

BACKGROUND

Male sex workers (MSWs), specifically cisgender men who exchange sex for money, goods, drugs, or other items of value with other cisgender men, are at high risk for HIV infection. Compared to men not engaged in sex work, MSWs are more likely to engage in frequent condomless sex with paying and non-paying sexual partners. While MSWs are often included as a subgroup of gay and bisexual men, data show that a large proportion identify as heterosexual; additionally, most MSWs do not identify as "sex workers." This places MSWs in a unique position where they may not engage with traditional HIV prevention programs, and when they do, they may not feel comfortable, leading to poor retention. Thus, HIV prevention interventions that address MSWs' unique life circumstances and provide support in exploring their sexual health options are needed.

METHODS

In this protocol paper, we describe the design and procedures for a National Institute of Health-funded, randomized controlled trial testing the efficacy of "PrEPare for Work,"- a theory-based, manualized PrEP uptake and adherence intervention for MSW - using a 2-stage randomization design. Stage 1: MSWs are equally randomized to receive either the "PrEPare for Work Stage 1 intervention" (strength-based case management and facilitated PrEP linkage) or Standard of Care (SOC) to evaluate successful PrEP uptake (prescription filled) within two months post-randomization. Stage 2: Those who initiate PrEP are then equally re-randomized to receive either the "PrEPare for Work Stage 2 intervention" (1-on-1 skills training, problem-solving, and motivational interviewing adherence counseling and personalized, daily text message reminders) or SOC to assess adherence (Tenofovir concentrations in hair) over 12 months of follow up. Planned analyses will examine intervention efficacy, specific conceptual mediators, and hypothesized moderators.

DISCUSSION

Based on our extensive preliminary research, multi-component, theory-informed interventions targeting this subpopulation of MSWs' unique life circumstances are urgently needed. In this study, we are evaluating whether "PrEPare for Work" can improve PrEP uptake and adherence among MSWs. If this intervention is efficacious, it would be readily disseminated to diverse community organizations that serve MSWs and possibly other community or clinic-based settings.

TRIAL REGISTRATION

ClinicalTrials.gov number NCT05736614, registered February 8, 2023.

Association of common variants of TCF7L2 and PCSK2 with gestational diabetes mellitus in West Bengal, India

The genetic etiology of gestational diabetes mellitus (GDM) was suggested to overlap with type-2 diabetes(T2D). Transcription factor 7-like 2 (TCF7L2) and Proprotein Convertase Subtilisin/Kexin type 2 (PCSK2) are T2D susceptibility genes of the insulin synthesis/processing pathway. We analyzed associations of TCF7L2 and PCSK2 variants with GDM risk and evaluated their potential impact on impaired insulin processing in an eastern Indian population. The study included 114 GDM (case) and 228 non-GDM pregnant women (control). rs7903146, rs4132670, rs12255372 of TCF7L2, and rs2269023 of PCSK2 were genotyped by PCR-RFLP, and genotype distributions were compared between case and control. Fasting serum proinsulin and C-peptide levels were measured by ELISA and the Proinsulin/C-peptide ratio was considered an indicator of proinsulin conversion. Significantly higher frequency of risk allele (T) of rs12255372 (p = 0.02, OR = 2.0, 95%CI = 1.11-3.64) and rs4132670 (p = 0.002, OR = 2.26, 95%CI = 1.32-3.87) of TCF7L2 was found in GDM cases than non-GDM controls; TT genotype was associated with significantly increased disease risk. In rs7903146 (TCF7L2) and rs2269023 (PCSK2), although the frequency of risk allele (T) was not significantly higher in cases than controls, an association of TT for both variants remained significant with higher GDM risk in the recessive model. Increased serum pro-insulin and proinsulin:c-peptide ratio was found in GDM than non-GDM women and the phenomenon showed significant association with careers of risk alleles for TCF7L2 variants. In conclusion, TCF7L2 and PCSK2 variants are related to GDM risk in the studied population and hence may serve as potential biomarkers for assessing the disease risk. TCF7L2 variants contribute to impaired insulin processing.

Bootstrap Statistics and Its Application in Disintegration and Dissolution Data Analysis

Disintegration time (DT) and rate of drug dissolution in different media are among the most widely studied crucial parameters for various types of drug products. In the ever-evolving landscape of generic formulation development, dissolution comparison of reference and test products is the major reliable in vitro method of establishing product similarity. This is one of the most widely accepted methods of proving pharma equivalency between two drug products. A well-studied match between the disintegration and dissolution profile of the test and reference formulations can ensure in vitro product similarity. Various statistical approaches have been employed to establish product performance similarity; among them, the similarity factor (f2) calculation based approach is the most widely accepted and explored method to date. However, the f2 statistics fail to predict the similarity of batches with unit-to-unit variability. Bootstrap statistical analysis of dissolution data between the test and reference products was introduced to overcome the problems associated with batches with unit variability. Bootstrap can also be applied to extract statistically significant results by treating a series of data from different batches, which can further help to understand the trend. The current review depicts different case study based approaches to show the applications of bootstrap statistics in disintegration and dissolution similarity evaluation for both conventional and additively manufactured solid dosage forms. It is concluded that bootstrap statistics can be a very promising and reliable data analytical tool for establishing in vitro product similarity for both conventional and additively manufactured formulations with a high level of intraunit variability.

Machine learning to predict myocardial injury and death after non-cardiac surgery

Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. Validation was performed on a randomly selected subset of the study population. Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69-0.72) vs. 0.71 (0.70-0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72-0.75) vs. 0.75 (0.74-0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75-0.77) vs. 0.77 (0.76-0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66-0.76) vs. 0.74 (0.71-0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73-0.82) vs. 0.83 (0.79-0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83-0.89) vs. 0.87 (0.85-0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.

Gas and Propane Combustion from Stoves Emits Benzene and Increases Indoor Air Pollution

Exposure pathways to the carcinogen benzene are well-established from tobacco smoke, oil and gas development, refining, gasoline pumping, and gasoline and diesel combustion. Combustion has also been linked to the formation of nitrogen dioxide, carbon monoxide, and formaldehyde indoors from gas stoves. To our knowledge, however, no research has quantified the formation of benzene indoors from gas combustion by stoves. Across 87 homes in California and Colorado, natural gas and propane combustion emitted detectable and repeatable levels of benzene that in some homes raised indoor benzene concentrations above well-established health benchmarks. Mean benzene emissions from gas and propane burners on high and ovens set to 350 °F ranged from 2.8 to 6.5 μg min^-1, 10 to 25 times higher than emissions from electric coil and radiant alternatives; neither induction stoves nor the food being cooked emitted detectable benzene. Benzene produced by gas and propane stoves also migrated throughout homes, in some cases elevating bedroom benzene concentrations above chronic health benchmarks for hours after the stove was turned off. Combustion of gas and propane from stoves may be a substantial benzene exposure pathway and can reduce indoor air quality.

Future trends in incidence and long-term survival of metastatic cancer in the United States

BACKGROUND

Previous studies have demonstrated epidemiological trends in individual metastatic cancer subtypes; however, research forecasting long-term incidence trends and projected survivorship of metastatic cancers is lacking. We assess the burden of metastatic cancer to 2040 by (1) characterizing past, current, and forecasted incidence trends, and (2) estimating odds of long-term (5-year) survivorship.

METHODS

This retrospective, serial cross-sectional, population-based study used registry data from the Surveillance, Epidemiology, and End Results (SEER 9) database. Average annual percentage change (AAPC) was calculated to describe cancer incidence trends from 1988 to 2018. Autoregressive integrating moving average (ARIMA) models were used to forecast the distribution of primary metastatic cancer and metastatic cancer to specific sites from 2019 to 2040 and JoinPoint models were fitted to estimate mean projected annual percentage change (APC).

RESULTS

The average annual percent change (AAPC) in incidence of metastatic cancer decreased by 0.80 per 100,000 individuals (1988-2018) and we forecast an APC decrease by 0.70 per 100,000 individuals (2018-2040). Analyses predict a decrease in metastases to liver (APC = -3.40, 95% CI [-3.50, -3.30]), lung (APC (2019-2030) = -1.90, 95% CI [-2.90, -1.00]); (2030-2040) = -3.70, 95% CI [-4.60, -2.80]), bone (APC = -4.00, 95% CI [-4.30, -3.70]), and brain (APC = -2.30, 95% CI [-2.60, -2.00]). By 2040, patients with metastatic cancer are predicted to have 46.7% greater odds of long-term survivorship, driven by increasing plurality of patients with more indolent forms of metastatic disease.

CONCLUSIONS

By 2040, the distribution of metastatic cancer patients is predicted to shift in predominance from invariably fatal to indolent cancers subtypes. Continued research on metastatic cancers is important to guide health policy and clinical intervention efforts, and direct allocations of healthcare resources.

Detection of potential drug-drug interactions for risk of acute kidney injury: a population-based case-control study using interpretable machine-learning models

Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting. Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively. Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H₂ blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H₂ blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226-5.591) also in the LLR model. Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H₂ blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H₂ blocker is associated with increased risk of AKI.

Factors Affecting Remote Workers' Job Satisfaction in Utah: An Exploratory Study

With structural changes in work arrangements, employee retention becomes more important for organizational success. Guided by the Ability, Motivation, Opportunity (AMO) framework, this study investigated the factors affecting remote workers' job satisfaction and personal wellbeing in Utah. From a sample of n = 143 remote workers, the study used a correlational design to identify the significant predictors of job satisfaction and personal wellbeing. It mapped the relationships between significant predictors of job satisfaction and personal wellbeing and explored the role of human resources (HR) policies and organizational culture in a remote work environment. Results showed intrinsic motivation, affective commitment, opportunity, and amotivation affected employee job satisfaction, while self-efficacy, amotivation, and job satisfaction affected personal wellbeing. A structural equation model (SEM) showed that remote workers with higher levels of self-efficacy, lower amotivation, and higher job satisfaction were likely to have greater personal wellbeing compared to others. When exploring the role of HR, findings showed that HR bundles and organizational culture indirectly affected job satisfaction but had a direct effect on the most important predictors of job satisfaction and personal wellbeing. Overall, results demonstrated the interconnectivity of HR practices, AMO factors, job satisfaction, and personal wellbeing.

Ecological Study of Urbanicity and Self-reported Poor Mental Health Days Across US Counties

Geography may influence mental health by inducing changes to social and physical environmental and health-related factors. This understanding is largely based on older studies from Western Europe. We sought to quantify contemporary relationships between urbanicity and self-reported poor mental health days in US counties. We performed regression on US counties (n = 3142) using data from the County Health Rankings and Roadmaps. Controlling for state, age, income, education, and race/ethnicity, large central metro counties reported 0.24 fewer average poor mental health days than small metro counties (t = - 5.78, df = 423, p < .001). Noncore counties had 0.07 more average poor mental health days than small metro counties (t = 3.06, df = 1690, p = 0.002). Better mental health in large central metro counties was partly mediated by differences in the built environment, such as better food environments. Poorer mental health in noncore counties was not mediated by considered mediators.

Measuring Effects of Spatial Visualization and Domain on Visualization Task Performance: A Comparative Study

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Different user needs and abilities suggest that an individual's background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that motivational differences between domains could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.

Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching

PURPOSE

Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM.

METHODS

Comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and β-blocker monotherapy identified from a large U.S. administrative claims database. One-to-one matching was conducted through PSM using nearest-neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post-match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group.

RESULTS

The eligible study population included 182 235 (ACEI: 129363; β-blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD <0.05 was associated with improved residual bias as compared to PSM.

CONCLUSION

We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.

Effects of perceived change of urban destination on destination attachment

The impact of urbanization on tourism is a widespread macroeconomic concern. However, few studies have explored the impact of destination urbanization on such individual tourist behavior as destination attachment. By developing an urbanization perception scale and analyzing tourists’ destination attachment, this study provides empirical evidence for the micro-impact of urbanization. A sample of 825 repeat visitors of Chengdu, China, was included in the partial least squares-based structural modeling. The results of several tests show that the environmental changes caused by green urbanization positively impact tourists’ destination attachment. This study also examines the explanatory role of tourist experience satisfaction and behavior control in this relationship. The findings suggest that tourists’ experience of urban change is critical for the sticky marketing of tourist destinations.

Toward a knowledge economy: Factors affecting the sustainable consumption behavior in the Chinese online education industry

Introduction

Buoyed by recent calls to research and advance the knowledge economy and sustainable development. This study explains how the role of the knowledge economy in influencing the COVID-19 pandemic has emerged with numerous opportunities for the global E-learning or online education industry. And, knowledge sharing behavior has been hugely driven by various sharing platforms concerning a new paradigm for diversifying education and learning. However, our study is to further extend the understanding and examine the related empirically correlations to deepen online education for sustainable development (OESD). Both advancing theoretical underpinnings and enhancing the online education industry are highly integrated and introduced toward a sustainable pathway. This study brings the perspectives from consumer value, social identity social exchange, and value-attitude-behavior to explain sustainable consumption behavior in the Chinese online education industry (SCBOEI). Thus, the relationship among factors in this study is statistically examined and the SCBOEI model as the new theoretical insight is introduced in a way of sustainable consumption behaviors in the Chinese online education industry. Finally, this study addresses managerial implications to practitioners, the government, universities, and markets.

Materials and methods

Employing a quantitative approach, about 559 valid questionnaire surveys are collected from Chinese higher education institutions. This study includes participants from four controlling variables (age, education level, family income, and gender) and six latent variables. The bootstrapping method was applied to validate mediating factors and their interacting relationships.

Results

The finding reveals that a set of classic psychological theories related fits the SCBOEI in higher education from the consumer value, contextual factors, social identity, sustainable consumption attitudes, and consumer engagement to explain SCBOEI. The mediating role of identity, sustainable consumption attitudes, and consumer engagement is highly concerned. The value and contextual factors directly make impact on SCBOEI through identity, sustainable consumption attitudes, and consumer engagement.

Implications

The study significantly contributes to enriching the theoretical bases for advancing the literature on sustainable consumption behavior and online education. Our research provides managerial insights into government policy about the online education industry and marketers to set more advertisements to wake awareness of SCBOEI. Furthermore, higher education institutions should encourage their employees and students to participate in SCBOEI actively. All the stakeholders are essential to lead the consumer to SCBOEI by shaping their internal psychology while paying more attention to social equality (education, gender), responsible consumption, and decent economic development. Overall, addressing these issues will help to provide scholars with novel theoretical insights and practitioners with managerial advice.

Assessing equivalent and inverse change in genes between diverse experiments

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Epileptiform activity predicts epileptogenesis in cerebral hemorrhage

This retrospective case-controlled study was performed to evaluate whether Epileptiform Activity, suspected clinical seizures, and/or 2HELPS2B/S after nontraumatic Intraparenchymal Hemorrhage or Subarachnoid Hemorrhage can predict Epilepsy. Hundred and thirty-two patients were included-29 (Epilepsy), 103 (Control Group). After matching, the average effect for all three risk factors was significant as follows: (1) Epileptiform Activity (p = 0.012, odds ratio 3.14), (2) suspected seizures (p = 0.021, odds ratio 3.78), and (3) 2HELPS2B/S score (p < 0.001, odds ratio 4.94). This study shows that Epileptiform Activity, suspected seizures, and particularly, the 2HELPS2B/S score in the acute phase are risk factors for the development of epilepsy after nontraumatic intraparenchymal and subarachnoid hemorrhage.

Exploring the relationship between social exclusion and smartphone addiction: The mediating roles of loneliness and self-control

Previous studies have identified many antecedents of smartphone addiction. However, social exclusion as a risk factor for smartphone addiction has not been widely studied, and little is known concerning the psychological mechanism underlying this association. The present study tested the influence of social exclusion on smartphone addiction as well as the mediating roles of loneliness and self-control in this relationship. An online survey was conducted, and the sample consisted of 573 university students (323 females). The results revealed that (1) social exclusion was a positive predictor of smartphone addiction; (2) loneliness and self-control separately mediated the association between social exclusion and smartphone addiction; and (3) loneliness and self-control sequentially mediated the relation between social exclusion and smartphone addiction. Possible explanations were discussed. The findings of the current study would contribute to understanding the relationships between these study variables as well as the psychological mechanisms underlying these associations.

Overall negative trends for snow cover extent and duration in global mountain regions over 1982-2020

Notwithstanding the large availability of data and models, a consistent picture of the snow cover extent and duration changes in global mountain areas is lacking for long-term trends. Here, model data and satellite images are combined by using Artificial Neural Networks to generate a consistent time series from 1982 to 2020 over global mountain areas. The analysis of the harmonized time series over 38 years indicates an overall negative trend of − 3.6% ± 2.7% for yearly snow cover extent and of − 15.1 days ± 11.6 days for snow cover duration. The most affected season by negative trends is winter with an average reduction in snow cover extent of − 11.5% ± 6.9%, and the most affected season by positive changes is spring with an average increase of 10% ± 5.9%, the latter mainly located in High Mountain Asia. The results indicated a shift in the snow regime located between the 80 s and 90 s of the previous century, where the period from 1982 to 1999 is characterized by a higher number of areas with significant changes and a higher rate of changes with respect to the period 2000–2020. This quantification can lead to a more accurate evaluation of the impact on water resources for mountainous communities.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.

Findings

1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.

Interpretation

CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.

Funding

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Detection of Synergistic Interaction on an Additive Scale Between Two Drugs on Abnormal Elevation of Serum Alanine Aminotransferase Using Machine-Learning Algorithms

Drug-induced liver injury (DILI) is a common adverse drug reaction, with abnormal elevation of serum alanine aminotransferase (ALT). Several clinical studies have investigated whether a combination of two drugs alters the reporting frequency of DILI using traditional statistical methods such as multiple logistic regression (MLR), but this model may over-fit the data. This study aimed to detect a synergistic interaction between two drugs on the risk of abnormal elevation of serum ALT in Japanese adult patients using three machine-learning algorithms: MLR, logistic least absolute shrinkage and selection operator (LASSO) regression, and extreme gradient boosting (XGBoost) algorithms. A total of 58,413 patients were extracted from Nihon University School of Medicine's Clinical Data Warehouse and assigned to case (N = 4,152) and control (N = 54,261) groups. The MLR model over-fitted a training set. In the logistic LASSO regression model, three combinations showed relative excess risk due to interaction (RERI) for abnormal elevation of serum ALT: diclofenac and famotidine (RERI 2.427, 95% bootstrap confidence interval 1.226-11.003), acetaminophen and ambroxol (0.540, 0.087-4.625), and aspirin and cilostazol (0.188, 0.135-3.010). Moreover, diclofenac (adjusted odds ratio 1.319, 95% bootstrap confidence interval 1.189-2.821) and famotidine (1.643, 1.332-2.071) individually affected the risk of abnormal elevation of serum ALT. In the XGBoost model, not only the individual effects of diclofenac (feature importance 0.004) and famotidine (0.016), but also the interaction term (0.004) was included in important predictors. Although further study is needed, the combination of diclofenac and famotidine appears to increase the risk of abnormal elevation of serum ALT in the real world.

Global Impact of Different Blood Pressure Thresholds in 4 021 690 Participants of the May Measurement Month Initiative

BACKGROUND

Influential guidelines currently define hypertension at different thresholds of blood pressure (BP). The global May Measurement Month initiative provides a unique opportunity to estimate the potential consequences of universal lowering of BP thresholds on the prevalence of hypertension based on a large, real-world, patient-level data set.

METHODS

The average of the second and third of 3 attended BP readings after 5 minutes of rest from 4 021 690 standardized, opportunistic BP screenings in various settings of the 2017 to 2019 May Measurement Month initiatives from 104 countries were analyzed to assess the demographic and phenotypic impact of 3 defined BP thresholds.

RESULTS

The age- and sex-standardized median of the relative increase in rate of hypertension with a change of thresholds from ≥140/≥90 to ≥130/≥80 mm Hg was 72.3% (interquartile range, 59.3%-91.3%) among the participating countries. With a change from ≥140/≥90 to ≥120/≥70 mm Hg, the median range was 162.6% (interquartile range, 132.8%-202.6%). The increase in rate of hypertension was most pronounced in low-income (95.3% and 203.9%) and least pronounced in high-income countries (71.6% and 167.1%).

CONCLUSIONS

The impact of a universal application of lower BP thresholds for the definition of hypertension would have an enormous impact on the prevalence of hypertension globally with large disparities between countries and substantial heterogeneity of demographic and phenotypic patterns, which should be confirmed in dedicated population studies. While focusing on lower BP targets may constitute an opportunity for early intervention, the potential socioeconomic consequences may pose unsurmountable obstacles for most health care systems worldwide.

Variability of α/β ratios for prostate cancer with the fractionation schedule: caution against using the linear-quadratic model for hypofractionated radiotherapy

BACKGROUND

Prostate cancer (PCa) is known to be suitable for hypofractionated radiotherapy due to the very low α/β ratio (about 1.5-3 Gy). However, several randomized controlled trials have not shown the superiority of hypofractionated radiotherapy over conventionally fractionated radiotherapy. Besides, in vivo and in vitro experimental results show that the linear-quadratic (LQ) model may not be appropriate for hypofractionated radiotherapy, and we guess it may be due to the influence of fractionation schedules on the α/β ratio. Therefore, this study attempted to estimate the α/β ratio in different fractionation schedules and evaluate the applicability of the LQ model in hypofractionated radiotherapy.

METHODS

The maximum likelihood principle in mathematical statistics was used to fit the parameters: α and β values in the tumor control probability (TCP) formula derived from the LQ model. In addition, the fitting results were substituted into the original TCP formula to calculate 5-year biochemical relapse-free survival for further verification.

RESULTS

Information necessary for fitting could be extracted from a total of 23,281 PCa patients. A total of 16,442 PCa patients were grouped according to fractionation schedules. We found that, for patients who received conventionally fractionated radiotherapy, moderately hypofractionated radiotherapy, and stereotactic body radiotherapy, the average α/β ratios were 1.78 Gy (95% CI 1.59-1.98), 3.46 Gy (95% CI 3.27-3.65), and 4.24 Gy (95% CI 4.10-4.39), respectively. Hence, the calculated α/β ratios for PCa tended to become higher when the dose per fraction increased. Among all PCa patients, 14,641 could be grouped according to the risks of PCa in patients receiving radiotherapy with different fractionation schedules. The results showed that as the risk increased, the k (natural logarithm of an effective target cell number) and α values decreased, indicating that the number of effective target cells decreased and the radioresistance increased.

CONCLUSIONS

The LQ model appeared to be inappropriate for high doses per fraction owing to α/β ratios tending to become higher when the dose per fraction increased. Therefore, to convert the conventionally fractionated radiation doses to equivalent high doses per fraction using the standard LQ model, a higher α/β ratio should be used for calculation.

Examining the Performance of Implantable-Grade Lithium-Ion Cells after Overdischarge and Thermally Accelerated Aging

For implanted medical devices containing rechargeable batteries, maximizing battery lifetime is paramount as surgery is required for battery replacement. In non-life-sustaining applications (e.g., spinal cord stimulators or sacral nerve modulation), these implants may be left unused and unmaintained for extended periods, according to patient preference or in the case of unexpected life events. In this study, we examine the performance of two commercial lithium-ion cells intended for implantable neurostimulators (using lithium titanium oxide (LTO) and graphite as the negative electrode) when subjected to repeated deep overdischarge and to aging at a high state of charge (SOC). The graphite-based cells exhibited significant performance decline and swelling after overdischarge and became unable to store a charge after 42 days at 0 V. In contrast, the LTO-based cells exhibited minimal changes in performance even after 84 days (the length of the study) at 0 V. When subjected to an accelerated aging protocol at 100% SOC, the graphite-based cells were found to age more rapidly than the LTO cells, which exhibited minimal aging over the course of the study period. These results show that practical LTO-based lithium-ion cells are much more tolerant of abuse as a result of neglect and misuse and are worth considering for use in high-value applications where battery replacement is difficult or impossible.

Methane and NOx Emissions from Natural Gas Stoves, Cooktops, and Ovens in Residential Homes

Natural gas stoves in >40 million U.S. residences release methane (CH₄)─a potent greenhouse gas─through post-meter leaks and incomplete combustion. We quantified methane released in 53 homes during all phases of stove use: steady-state-off (appliance not in use), steady-state-on (during combustion), and transitory periods of ignition and extinguishment. We estimated that natural gas stoves emit 0.8-1.3% of the gas they use as unburned methane and that total U.S. stove emissions are 28.1 [95% confidence interval: 18.5, 41.2] Gg CH₄ year^-1. More than three-quarters of methane emissions we measured originated during steady-state-off. Using a 20-year timeframe for methane, annual methane emissions from all gas stoves in U.S. homes have a climate impact comparable to the annual carbon dioxide emissions of 500 000 cars. In addition to methane emissions, co-emitted health-damaging air pollutants such as nitrogen oxides (NO_x) are released into home air and can trigger respiratory diseases. In 32 homes, we measured NO_x (NO and NO₂) emissions and found them to be linearly related to the amount of natural gas burned (r² = 0.76; p ≪ 0.01). Emissions averaged 21.7 [20.5, 22.9] ng NO_x J^-1, comprised of 7.8 [7.1, 8.4] ng NO₂ J^-1 and 14.0 [12.8, 15.1] ng NO J^-1. Our data suggest that families who don't use their range hoods or who have poor ventilation can surpass the 1-h national standard of NO₂ (100 ppb) within a few minutes of stove usage, particularly in smaller kitchens.

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators.

Exploring Structural Relationships in Attracting and Retaining International Students in STEM for Sustainable Development of Higher Education

Attracting and retaining international students has been widely discussed in higher education settings. Increasing the number of international students has become an indispensable strategy for national and global competition. This study focuses on effective strategies and international students’ issues regarding satisfaction in the most popular STEM (science, technology, engineering, and mathematics) programs. We designed a structural equation modeling (SEM) method to determine the effect of institutional mediation between push factors and satisfaction factors for the development of better strategies by which to attract and retain international students. This study employed a self-designed questionnaire to collect data: 485 degree-seeking international students in STEM programs were invited and successfully participated in this study during spring 2021 in Taiwan. IBM SPSS 26 and AMOS 26 (Analysis of Moment Structure) were used to carry out the data analysis. We employed reliability, factor, and SEM analyses. This study assumed that the impact of push factors could be modified by institutional situations and result in international students’ satisfaction with their learning and environment and regarding migration policy. The results revealed that the predictors, mediation, and criteria were significant at the 0.05 or 0.01 levels. The findings suggest that push factors impact international students’ satisfaction when using institutional leadership and strategy. The results of the bootstrap with a generalized least-squares method showed that the SEM model fit in 2000 bootstrap samples. The effect of institutional mediation can provide useful information for STEM programs to boost their future recruitment and retention strategies. This study provides an innovative approach to the detection of issues among international students in specific programs. The design of the study can be extended to similar higher education settings. These findings can enrich our knowledge regarding attracting and retaining global students in higher education.

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report

Purpose

This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS.

Methods

The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (AL_cs) and tolerance limit (TL_cs) were calculated for our clinic and monitored during the analysed period.

Results

The mean values ​​(± 1 standard deviation) of GP% (3%/2 mm) (both global and local normalization) are: 97.6% and 90.9%, respectively. The proposed AL_cs and TL_cs, after a period of two years’ process monitoring are 89.4% and 91.1% respectively.

Conclusions

The phantom measurements closely match the planned dose distributions, demonstrating that the calculation accuracy of the new Precision TPS and the delivery accuracy of the Radixact unit are adequate, with respect to international guidelines and reports. Furthermore, a first correlation with the planning parameters was made. Action and tolerance limits have been set for the new Radixact Linac.

Information and Communication Technology and Organizational Performance During Covid-19 Pandemic: The Role of Organizational Commitment, Growth Mindset, and Entrepreneurial Orientation

The purpose of this study is to assess how information and communication technology (ICT) adoption influences organizational performance (OP) during the Covid-19 pandemic by highlighting psychometric variables such as employees’ organizational commitment (OC), growth mindset (GM), and entrepreneurial orientation (EO). Based on the complementarity theory, we built a theoretical framework where OC, GM, and EO mediate the influence of ICT on OP and tested hypotheses proposed. Responses of 297 employees from agriculture cooperatives in Côte d’Ivoire were obtained on the basis of questionnaires which composed the data for this study. The empirical analysis affirmed the significant and positive effect of ICT adoption on OP, and the significant mediating effect of OC and GM in the relationship between ICT adoption and OP. However, the role of EO in mediating the influence of ICT adoption on OP is insignificant. This research increases understanding of the underlying process of the relationship between ICT adoption and organizational performance during the Covid-19 pandemic.

Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection

The information captured by the gist signal, which refers to radiologists’ first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: −0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62–0.86), 0.79 (CI: 0.63–0.89), and 0.88 (CI: 0.79–0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.

Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial

Exploratory Graph Analysis (EGA) has emerged as a popular approach for estimating the dimensionality of multivariate data using psychometric networks. Sampling variability, however, has made reproducibility and generalizability a key issue in network psychometrics. To address this issue, we have developed a novel bootstrap approach called Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA generates a sampling distribution of EGA results where several statistics can be computed. Descriptive statistics (median, standard error, and dimension frequency) provide researchers with a general sense of the stability of their empirical EGA dimensions. Structural consistency estimates how often dimensions are replicated exactly across the bootstrap replicates. Item stability statistics provide information about whether dimensions are unstable due to misallocation (e.g., item placed in the wrong dimension), multidimensionality (e.g., item belonging to more than one dimension), and item redundancy (e.g., similar semantic content). Using a Monte Carlo simulation, we determine guidelines for acceptable item stability. After, we provide an empirical example that demonstrates how bootEGA can be used to identify structural consistency issues (including a fully reproducible R tutorial). In sum, we demonstrate that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in multivariate data.

Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

BlocklyXR: An Interactive Extended Reality Toolkit for Digital Storytelling

Traditional in-app virtual reality (VR)/augmented reality (AR) applications pose a challenge of reaching users due to their dependency on operating systems (Android, iOS). Besides, it is difficult for general users to create their own VR/AR applications and foster their creative ideas without advanced programming skills. This paper addresses these issues by proposing an interactive extended reality toolkit, named BlocklyXR. The objective of this research is to provide general users with a visual programming environment to build an extended reality application for digital storytelling. The contextual design was generated from real-world map data retrieved from Mapbox GL. ThreeJS was used for setting up, rendering 3D environments, and controlling animations. A block-based programming approach was adapted to let users design their own story. The capability of BlocklyXR was illustrated with a use case where users were able to replicate the existing PalmitoAR utilizing the block-based authoring toolkit with fewer efforts in programming. The technology acceptance model was used to evaluate the adoption and use of the interactive extended reality toolkit. The findings showed that visual design and task technology fit had significantly positive effects on user motivation factors (perceived ease of use and perceived usefulness). In turn, perceived usefulness had statistically significant and positive effects on intention to use, while there was no significant impact of perceived ease of use on intention to use. Study implications and future research directions are discussed.

An Exploratory Study of Consumer Food Waste Attitudes, Social Norms, Behavioral Intentions, and Restaurant Plate Waste Behaviors in Taiwan

The rising trend of people dining out has made food waste in restaurants become a significant issue because of sustainability. Consumers’ attitudes toward food waste in restaurants are still undergoing scrutiny. The main purpose of this study was to test the relationships among consumers’ attitudes, social norms, behavioral intentions, and plate waste behaviors in restaurants in Taiwan. This paper contributes to the understanding of consumers’ food waste behaviors by examining a hypothesized research model. Based on a questionnaire with 464 restaurant customers, the hypothesized research model was examined by using structural equation modeling. Sequential mediation for examining the relationships of variables was conducted, and it was found that there was a significant serial mediation effect on the path of consumers’ attitudes, behavioral intentions, food waste behavior, and consumer plate waste. The results of this study can contribute to better engaging consumers in mitigating food waste in restaurants. Implications and suggestions for further research and recommendations for restaurant managers are provided based on sustainable management.

Early pubertal maturation and externalizing behaviors: Examination of peer delinquency as mediator and cognitive flexibility as a moderator

INTRODUCTION

While peer delinquency is a known mediator between early pubertal timing and externalizing behaviors, little is known about factors that could protect against the adverse influence of peer delinquency. This study assesses the possible moderating role of cognitive flexibility, which is one index of executive functioning that facilitates flexible and adaptive responses to challenging situations. We assessed the interactive influence of peer delinquency and cognitive flexibility in the association between pubertal maturation and externalizing behaviors in boys and girls.

METHOD

Participants were 220 adolescents (111 boys) from the United States, between the ages of 11 and 16 years (M = 13.2, SD = 1.53) who participated in the Adolescent Emotion Study (AES).

RESULTS

Findings from the cross-sectional path modeling analyses provided evidence for the mediating role of peer delinquency for boys and girls, indicating that early maturing adolescents tend to affiliate with delinquent peers, which in turn exacerbates externalizing problems. Additionally, the moderating role of cognitive flexibility was also demonstrated for both boys and girls. Region of significance tests revealed that relatively well-developed cognitive flexibility skills could protect against the adverse influences of peer delinquency, whereas lower levels could exacerbate those negative influences.

CONCLUSION

These findings suggest that involvement with deviant peers increases vulnerability for both early maturing boys and girls. Additionally, cognitive flexibility was an important moderating factor for adolescents, such that youths with less developed skills would be at a higher risk for psychopathology, whereas those with better development could be protected.

Cytokine changes associated with the maternal immune activation (MIA) model of autism: A penalized regression approach

Maternal immune activation (MIA) during pregnancy induces a cytokine storm that alters neurodevelopment and behavior in the progeny. In humans, MIA increases the odds of developing neuropsychiatric disorders such as autism spectrum disorder (ASD). In mice, MIA can be induced by injecting the viral mimic polyinosinic:polycytidylic acid (poly(I:C)) to pregnant dams. Although the murine model of MIA has been extensively studied, it is not clear whether MIA results in cytokine changes in the progeny at early postnatal stages. Further, the murine model of MIA suffers from a lack of reproducibility and high inter-individual variability. Multivariable (MV) statistical analysis is widely used in human studies to control for confounders and covariates such as sex, age and exposure to environmental factors. We therefore reasoned that animal studies in general and studies on the MIA model in particular could benefit from MV analyses to account for complex phenotype interactions and high inter-individual variability. Here, we used MV statistical analysis to identify cytokines associated with MIA after adjustment for covariates. Besides confirming the association between previously described variables and MIA, we identified new cytokines that could play a role in behavioural alterations in the progeny during the early postnatal period.

BlocklyAR: A Visual Programming Interface for Creating Augmented Reality Experiences

State-of-the-art tools for creating augmented reality (AR) applications often depend on a specific programming language and the deployed target devices. The typing syntax of a program is error-prone, and device dependency makes it difficult to share newly created AR applications. This paper presents BlocklyAR, a novel web-based visual programming interface for creating and generating an AR application. This tool is intended for non-programmers (young learners and enthusiasts) who are interested in making an AR application. The goals of this tool are: (1) to help young learners and enthusiasts express their programming ideas without memorizing syntax, (2) to enable users to perceive their expressions, (3) to enable learners to generate an AR application with minimal effort, and (4) to support users by allowing them to share newly created AR applications with others. BlocklyAR uses Blockly for creating a palette of commands and AR.js for transcribing commands into AR experience. The applicability of BlocklyAR was demonstrated through a use case where an existing AR application was recreated by using our tool. The result showed that our tool could yield an equivalent product. We evaluated the visual tool with the help of 66 users to gather perspectives on the specific benefits of employing BlocklyAR in producing an AR application. The technology acceptance model was adapted to assess an individual’s acceptance of information technology.

Meet the Virtual Jeju Dol Harubang—The Mixed VR/AR Application for Cultural Immersion in Korea’s Main Heritage

Jeju Island comes second to only Seoul as Korea’s most visited destination, yet most visitors do not have the chance to go beyond brief visits and immerse themselves in the island’s history and cultural heritage. This project introduces the cultural heritage of Jeju Island to visitors through virtual reality/augmented reality (VR/AR) model visualization technology, namely JejuView, which provides an intuitive way to experience cultural heritage sites on the island. The proposed VR/AR application is designed to introduce a series of heritage spots on Jeju Island through (i) a printed Jeju map with embedded QR code markers that enable viewers to experience the locations without being present at the site, (ii) a mobile device with WebGL supported browser which allows 3D content to be rendered, and (iii) an AR library (A-Frame.io) that enables enthusiasts to recreate similar work. To test the effectiveness of the proposed VR/AR application, the authors conducted an experiment with 251 participants to test the research model based on the technology acceptance model (TAM) and employed generalized structured component analysis (GSCA) for the analysis. Results show that when using sensory new media such as VR/AR, consumers are more focused on the hedonic value than on the utilitarian value of the information. In conclusion, the proposed VR/AR application is complementary to existing studies and provides significant support to researchers, engineers, and designers developing VR/AR technologies for use in cultural education and tourism marketing.