Results Of Sensitivity Analysis Research Articles

Diabetes mellitus and idiopathic pulmonary fibrosis: a Mendelian randomization study

BackgroundThe question as to whether or not diabetes mellitus increases the risk of idiopathic pulmonary fibrosis (IPF) remains controversial. This study aimed to investigate the causal association between type 1 diabetes (T1D), type 2 diabetes (T2D), and IPF using Mendelian randomization (MR) analysis.MethodsWe used two-sample univariate and multivariate MR (MVMR) analyses to investigate the causal relationship between T1D or T2D and IPF. We obtained genome-wide association study (GWAS) data for T1D and T2D from the European Bioinformatics Institute, comprising 29,652 T1D samples and 101,101 T1D single nucleotide polymorphisms (SNPs) and 655,666 T2D samples and 5,030,727 T2D SNPs. We also used IPF GWAS data from the FinnGen Biobank comprising 198,014 IPF samples and 16,380,413 IPF SNPs. All cases and controls in these datasets were derived exclusively from European populations. In the univariate MR analysis, we employed inverse variance-weighted (IVW), weighted median (WM), and MR-Egger regression methods. For the MVMR analysis, we used the multivariate IVW method primarily, and supplemented it with multivariate MR-Egger and multivariate MR- least absolute shrinkage and selection operator methods. Heterogeneity tests were conducted using the MR-IVW and MR-Egger regression methods, whereas pleiotropic effects were assessed using the MR-Egger intercept. The results of MR and sensitivity analyses were visualized using forest, scatter, leave-one-out, and funnel plots.ResultsUnivariate MR revealed a significant causal relationship between T1D and IPF (OR = 1.118, 95% CI = 1.021–1.225, P = 0.016); however, no significant causal relationship was found between T2D and IPF (OR = 0.911, 95% CI = 0.796–1.043, P = 0.178). MVMR analysis further confirmed a causal association between T1D and IPF (OR = 1.133, 95% CI = 1.011–1.270, P = 0.032), but no causal relationship between T2D and IPF (OR = 1.009, 95% CI = 0.790–1.288, P = 0.950). Sensitivity analysis results validated the stability and reliability of our findings.ConclusionUnivariate and multivariate analyses demonstrated a causal relationship between T1D and IPF, whereas no evidence was found to support a causal relationship between T2D and IPF. Therefore, in clinical practice, patients with T1D should undergo lung imaging for early detection of IPF.

Identifying the opportunities for sustainable bitumen production in India.

The present study assessed the environmental impacts due to bitumen production in India using life cycle assessment approach. The impacts were calculated for production of 1 t of bitumen and system boundary covered extraction of resources, processing at refinery, transportation of bitumen and storage at the production site. In this study, five scenarios were considered to estimate the impacts reduction assuming different future electricity mix and thermal energy source. Crude oil extraction phase had contributed highest (91%) followed by refinery phase (4%), then transportation (3%) and at last storage of bitumen (2%). The normalization results found that the bitumen production had highest impacts on abiotic depletion fossil and lowest impact on eutrophication. Scenario S4 had the least environmental impacts and provided the overall reductions of 33% compared to the baseline scenario. Scenario S4 reduced the impacts significantly on acidification (51%), eutrophication (30%), and human toxicity (71%), but the reductions were not significant on global warming (11%) and increased the impacts on abiotic depletion fossil (1%). The results of sensitivity analysis found that thermal energy obtained from hard coal consumed during bitumen production is the most sensitive parameter for all the impact categories. The uncertainty analysis showed that the results of this study are reliable and had standard deviation less than 5% for all the impact categories. The findings of the present study will help the decision makers and concerned authorities to reduce the environmental impacts from bitumen production in India.

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.

(076) Hyaluronic Acid Injection of Penis for Management of Premature Ejaculation: A Systematic Review with Single-Arm Meta-Analysis

Abstract Introduction Premature ejaculation (PE) is the most prevalent male sexual dysfunction globally. Pharmacological treatments as the primary mainstay for PE therapy have drawbacks in systemic effect and possible recurrence after drug cessation. In the last few years, there has been a progressive use of hyaluronic acid (HA) injection in Andrology, including its use to prolong ejaculation latency time by increasing penile glans sensitivity. However, no meta-analysis on its efficacy and safety exists. Objective The study aimed to conduct a comprehensive systematic review and single-arm meta-analysis to identify the efficacy and safety of HA injection in the penis for treating PE. Methods A systematic search on PubMed, Scopus, and Embase databases were searched from inception to May 31, 2023, according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement. Risk of bias of included study were carried out based on Cochrane ROB2 and ROBINS-I tools. Intravaginal ejaculatory latency time (IELT) was considered the primary outcome, whereas satisfaction and complication rate were the secondary outcomes. Pooled rate and mean difference with matching 95% confidence level intervals (CIs) in the random effect model were applied in analysis using STATA 18.0 and RevMan 5.4. To ensure the impact of each study, a leave-one-out sensitivity analysis was performed. Results Of the 60 studies identified, three clinical trials and six observational studies with low and moderate risk of bias met the inclusion criteria. A total of 616 participants were eligible for meta-analysis. The results found that HA injection, with different techniques, can significantly increase IELT by 239.78 (95% CI 147.14 – 332.43), 202.76 (95% CI 121.86 – 283.66), 164.62 (95% CI 112.19 – 217.06), and 189.01 (95% CI 71.74 – 306.28) seconds at 1, 3, 6, and ≥12 months follow-up. Despite significant heterogeneity among the studies, the sensitivity analysis results were unaffected by the removal of any of the articles. As for safety, the most commonly reported complications were pain, ecchymosis, and papule formation with pooled incidence of 19% (95% CI: 15% – 24%), 8% (95% CI: 5% – 13%), and 5% (95% CI: 3% – 8%), respectively. The majority of adverse events were mild, temporary, and self-limited, although a single case of skin necrosis and vascular emboli had been reported. In addition, patients and their partners also reported increased satisfaction after HA injection. Conclusions HA injection in the penile can effectively prolong the ejaculation time by at least a year with tolerable adverse events. Nevertheless, the optimal injection technique, ideal patient candidate, and long-term outcome still need to be determined through additional well-designed trials. Disclosure No.

Prospective cohort study evaluating the association between influenza vaccination and neurodegenerative diseases

The effect of influenza vaccination (FluVac) on the risk of neurodegenerative diseases has not been well evaluated in prospective populations. We aimed to assess the association between FluVac and the risk of dementia and Parkinson’s disease (PD) in people aged 60 years or older through a prospective population-based cohort from the UK Biobank. A time-varying Cox regression model adjusted for baseline and repeatedly measured covariates was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) of the association between influenza vaccination and risk of dementia/PD. We took into account 70,938 participants in the cohort, including 38,328 participants who got vaccinated. During a median follow-up period of 12.2 years, 2087 incident dementia cases occurred, including 281 cases who received FluVac and 1806 cases who were not vaccinated. In addition, 742 incident PD cases occurred, among whom 131 cases received FluVac and 611 PD cases did not receive FluVac. FluVac was associated with reduced dementia risk with an HR of 0.83 (95% CI, 0.72–0.95) but was not associated with PD incidence (HR = 1.07; 95% CI, 0.87–1.32) after controlling baseline and repeatedly measured covariates. Further, among all dementia cases, there were 733 Alzheimer’s disease (AD) (94 vaccinated cases and 639 non-vaccinated cases), 307 vascular dementia (VD) (34 vaccinated cases and 273 non-vaccinated cases), and 1047 cases with other dementias (OD) (153 vaccinated cases and 894 non-vaccinated cases). The HRs for the associations between FluVac and AD, VD, and OD were 0.79 (95% CI, 0.63–1.00), 0.58 (95% CI, 0.39–0.86), and 0.94 (95% CI, 0.78–1.14) respectively. A dose-response relationship was found in the association between FluVac and dementia but not in the association with PD. A major limitation of the study is the low accuracy in the diagnosis of dementia subtypes, namely AD, VD, and OD. However, Results of sensitivity analyses were consistent with the primary analyses. In conclusion, influenza vaccination is significantly associated with a reduced risk of incident dementia but not PD in community-dwelling adults in the UK Biobank population.

Estimating most productive scale size decomposition in a fuzzy network data envelopment analysis model: assessing the sustainability and resilience of the supply chain

This paper estimates the Most Productive Scale Size (MPSS) in the NDEA model to appraise the sustainability and resilience of the supply chains. As the corresponding input and output criteria are not always accurately measurable, we also introduce the fuzzy version of our proposed NDEA model and apply the proposed model in a case study involving 10 Iranian supply chains of Companies Producing Soft Drinks (CPSDs). The considered-three-echelon supply chains include suppliers, manufacturers, and distributors. Mathematical analysis proves that the MPSS of the considered supply chain can be decomposed as the sum of the MPSS values of the individual stages. Thus, the supply chain is overall MPSS if and only if it is MPSS in every three stages. The results of this study reveal that the Behnoush supply chain is overall MPSS in all three stages, including supplier, manufacturer, and distributor, for any α ∈ {0.1, 0.5, 1}. A sensitivity analysis has been performed to measure the impact of each criterion on the entire supply chain performance. The sensitivity analysis results indicate that the social and resilience criteria significantly impact the performance and ranking of supply chains. Finally, we discuss how to improve the sustainability and resilience of non-MPSS supply chains.

The association between serum 25-hydroxyvitamin D levels and erectile dysfunction: a two-sample Mendelian randomization analysis.

Considering that vascular endothelial cell dysfunction is the pathological basis of erectile dysfunction (ED), and recognizing the beneficial effects of 25-hydroxyvitamin D (25(OH)D) on vascular endothelial cell protection, the researchers diligently investigated the causal relationship between serum 25(OH)D levels and ED. However, inconsistent clinical evidence has left the association between serum 25(OH)D levels and ED unclear. The objective of this work was to employ Mendelian randomization (MR) analysis to ascertain the potential causal relationship between serum 25(OH)D levels and ED. We conducted a two-sample MR analysis utilizing data from publicly available genome-wide association studies (GWASs). The primary analysis method for the MR analysis was the inverse-variance weighted (IVW) method, supplemented by the MR-Egger and weighted median methods. In addition, we evaluated heterogeneity with Cochran's Q test, assessed pleiotropy using the MR-Egger intercept test, and performed a leave-one-out analysis to identify single-nucleotide polymorphisms (SNPs) with potential effects. Outliers were detected using MR-pleiotropy residual sum and outlier (MR-PRESSO). Genetically predicted serum 25(OH)D levels were not found to be causally associated with ED in IVW method (OR = 1.028, 95% CI = 0.845-1.250, P = 0.785), MR-Egger method (OR = 1.057, 95% CI = 0.782-1.430, P = 0.720), and weighted median method (OR = 1.225, 95% CI = 0.920-1.633, P = 0.165). The results of sensitivity analyses reinforced our conclusion, indicating no evidence of heterogeneity or directional pleiotropy. In summary, our findings do not substantiate a genetic-level causal link between serum 25(OH)D levels and the prevalence of ED. Nonetheless, future research, including larger MR studies, clinical trials, and additional observational studies, is essential to validate and reinforce the outcomes of our present study.

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt

Intelligent methods including Machine Learning (ML) techniques have been increasingly employed in transportation mode choice modeling, which is more complex than other demand models, since it has to reliably and accurately reflect a wide range of related categorical and continuous variables, concerning the travelers, transportation system, and trip characteristics. ML techniques can capture such complex relationships. So that, they can provide a more nuanced understanding of the travelers’ decision process. Most research studies focused mainly on the evaluation of the model accuracy, where little has been done to evaluate the models’ performance toward transport attributes. This research aims to calibrate the transportation choice models using ML techniques, then conduct a comparison with the Multinomial Logit (MNL) model to identify the impact on the model accuracy and performance and quantify to what extent the ML models are sensitive to transport policies compared to the traditional MNL model. To this end, eight ML classifiers were examined. As a case study, the models were calibrated to reflect the choice behavior of trip makers in Alexandria City, Egypt. The models were successfully calibrated with satisfying accuracy; however, the ML models have better calibration results in terms of predictability, outperforming the MNL model, where the GBDT classifier records the best prediction accuracy. Finally, the sensitivity analysis test was performed to quantify the elasticity of the models to transport policies. The results show the ML models’ structure is more comprehensively and accurately built than the MNL model providing better indicative and reliable sensitivity analysis results.

Causal relationship between several autoimmune diseases and renal malignancies: A two-sample mendelian randomization study.

Observational studies have shown an association between systemic autoimmune disease (AD) and multiple malignancies. However, due to the difficulty indetermining the temporal nature of the order, their causal relationship remains elusive. Based on pooled data from a large population-wide genome-wide association study (GWAS), this study explores the genetic causality between systemic autoimmune disease and renal malignancy. We took a series of quality control steps from a large-scale genome-wide association study to select single nucleotide polymorphisms (SNPs) associated with systemic autoimmune disease as instrumental variables(IVs) to analyze genetic causality with renal malignancies. Inverse variance weighting (IVW), MR- Egger, weighted median, simple model and weighted model were used for analysis. The results were mainly based on IVW (Random Effects), followed by sensitivity analysis. Inverse-Variance Weighted(IVW) and MR-Egger were used to test for heterogeneity. MR- Egger is also used for pleiotropic testing. A single SNP analysis was used to identify single nucleotide polymorphisms (SNPs) with potential impact. Odds ratio (OR) and 95% confidence interval (CI) were used to evaluate causality, and sensitivity analysis was performed to evaluate pleiotropy and instrumental validity. Acute and subacute iridocylitis (P = 0.006, OR = 1.077), Ankylosing spondylitis (P = 0.002, OR = 1.051), and spondyloarthritis (P = 0.009, OR = 1.073) were positively associated with an increased risk of renal malignancy. Coxarthrosis (P = 0.008, OR = 0.483), Juvenile rheumatism (P = 0.011, OR = 0.897), and Systemic lupus erythematosus (P = 0.014, OR = 0.869) were negatively associated with an increased risk of renal malignancy. The results of sensitivity analysis were consistent without heterogeneity or pleiotropy. Our study suggests a causal relationship between different systemic autoimmune diseases and renal malignancies. These findings prompt health care providers to take seriously the potential risk of systemic autoimmune disease and provide new insights into the genetics of kidney malignancies.

Genetic insights into the gut microbiota and risk of facial skin aging: A Mendelian randomization study.

A growing number of experimental studies have shown an association between the gut microbiota (GM) and facial skin aging. However, the causal relationship between GM and facial skin aging remains unclear to date. We conducted a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between GM and facial skin aging. MR analysis was mainly performed using the inverse-variance weighting (IVW) method, complemented by the weighted median (MW) method, MR-Egger regression, and weighted mode, and sensitivity analysis was used to test the reliability of MR analysis results. Eleven GM taxa associated with facial skin aging were identified by IVW method analysis, Family Victivallaceae (p=0.010), Genus Eubacterium coprostanoligenes group (p=0.038), and Genus Parasutterella (p=0.011) were negatively associated with facial skin aging, while Phylum Verrucomicrobia (p=0.034), Family Lactobacillaceae (p=0.017) and its subgroups Genus Lactobacillus (p=0.038), Genus Parabacteroides (p=0.040), Genus Eggerthella (p=0.049), Genus Family XIII UCG001 (p=0.036), Genus Phascolarctobacterium (p=0.027), and Genus Ruminococcaceae UCG005 (p=0.012) were positively associated with facial skin aging. At Class and Order levels, we did not find a causal relationship between GM and facial skin aging. Results of sensitivity analyses did not show evidence of pleiotropy and heterogeneity. Our findings confirm the causal relationship between GM and facial skin aging, providing a new perspective on delaying facial aging.

Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP strategies often ignore the fluctuation of passenger occupancy in transit vehicles, leading to sub-optimal solutions for the entire system. The use of Connected Vehicle (CV) technology enables the adoption of a more fine-grained objective in optimizing traffic signals, such as person delay, by allowing real-time information on passenger occupancy to be obtained. In this study, a deep reinforcement learning algorithm, deep Q-network (DQN), is applied to develop a traffic signal controller that minimizes the average person delay. The proposed DQN controller is tested in a simulation environment modeled after a real-world intersection and compared with pretimed and actuated controllers. Results show that the proposed DQN controller has the best performance in terms of average person delay. Compared to the baseline, it reduces the average person delay by 18.77% in peak hours and 23.37% in off-peak hours. Furthermore, it also results in decreased average delays for both buses and cars. The sensitivity analysis results indicate that the proposed controller has the potential for practical applications, as it can effectively handle some dynamic changes.

Genetically predicted circulating interleukin-18 levels are associated with risk of systemic lupus erythematosus and type 1 diabetes.

Interleukin-18 (IL-18) is a proinflammatory cytokine. This study aims to determine whether there is a causal relationship between circulating IL-18 concentrations and the risk of inflammatory and autoimmune diseases. We collected significant single nucleotide polymorphisms (SNPs) associated with circulating IL-18 levels (p < 5 × 10-8) as instrumental variables (IVs) from a genome-wide association study (GWAS) involving 21,758 individuals of European descent. We mainly employed the inverse-variance weighed (IVW) method of two-sample Mendelian randomization (TSMR) analysis to estimate the causality of circulating IL-18 levels on inflammatory and autoimmune diseases. The IVW method results showed evidence of a causal relationship between IL-18 and the risk of systemic lupus erythematosus (SLE) (OR = 1.32; 95% CI 1.15, 1.50; p < .001) and type 1 diabetes (T1D) (OR = 1.22; 95% CI 1.06, 1.42; p = .007) in individuals of European ancestry. No significant heterogeneity or horizontal pleiotropy for SLE and T1D was detected. The sensitivity analysis, which involved removing confounding SNP, produced similar results for SLE and T1D. The results of sensitivity analysis using leave-one-out method indicated no single SNP significantly influenced the analysis results. However, we did not find any significant findings for multiple sclerosis, psoriasis, asthma, and osteoarthritis. Our analyses suggest that circulating IL-18 is significantly related to SLE and T1D and may serve as a potential target for the treatment of these diseases.

A performance comparison of the meta model methods for discharge coefficient prediction of labyrinth weirs

Weirs are hydraulic structures with various applications, such as flow measurement, flow diverting, and/or flow control. Labyrinth weir as a nonlinear weir with a folded crest enhances the spillway system discharge capacity by increasing the crest length for a given channel width. As a remarkable parameter, the discharge coefficient plays an important role in determining weirs' passing capacity. So, it is important that the discharge coefficient be accurately represented to ensure an appropriate and economical design. This research evaluates the prediction accuracy of four soft computing techniques, such as support vector machines (SVM), gene expression programming (GEP), software (QNET), and artificial intelligence networks (ANN), to predict the discharge coefficient of labyrinth weirs using three different scenarios. The model's input parameters included the total water head ratio (HT/P), magnification (Lc/W), and cycle wall angle (α). On reviewing the outcomes of the developed models, it appeared that each of them demonstrated satisfactory performance in predicting the discharge coefficient. However, ANN and SVM reveal more accuracy compared to other techniques. The prediction results reveal that the ANN as a superior model has the following performance metrics: R2 = 0.998, DC coefficient = 0.996, and RMSE = 0.006. The sensitivity analysis results indicate that HT/P is the most effective factor in determining the labyrinth weir discharge coefficient in all methods. This suggests that the ratio of HT (total water head) to P (weir height) has a significant impact on the overall model performance. Upon comparing the results of this research with those of other researchers, it is evident that the evaluation indicators for all methods employed in this study are relatively superior.

Simulating the bi-directional pedestrian flow under high densities by a floor field cellular automaton model

In this paper we propose one floor field cellular automaton model, which can simulate the bi-directional pedestrian flow at high densities. Based on the model rules proposed by Nowak and Schadschneider, we make some modifications, including changes of cell size, realistic velocity configurations and extended lateral movement. The best parameters are determined by the results of sensitivity analysis. Some data extracted from pedestrian flow experiments, including the fundamental diagrams and the lane numbers after lane formation, are used for model validations. When the corridor width is 2m, the simulation results show that the fundamental diagrams of bi-directional flow are quantitatively similar to the experimental results, and the lane formation could be successful at high densities. By comparing with the statistical results in the experiments, we find the probabilities of the occurrence of different lanes are similar to the experimental results: two-lane and three-lane states usually emerge, while the probability for more lanes is low. The inhomogeneous distributions of pedestrians also could be observed. We think our model could be a good choice for simulating the lane formation process at high densities.

Exposure to heavy metals and trace elements among pregnant women with twins: levels and association with twin growth discordance.

Twin growth discordance is one of the leading causes of perinatal mortality in twin pregnancies. Whether prenatal exposure to heavy metals and trace elements is associated with twin growth discordance has not been studied yet. To evaluate the prenatal level of heavy metals and trace elements in twin pregnancy and its relationship with twin growth discordance. This study involving 60 twin pairs and their mothers was conducted in Zhejiang Province, China, in 2020-2021. The concentration of heavy metals and trace elements in maternal blood, umbilical cord, and placenta were collected at delivery and measured by inductively coupled plasma tandem mass spectrometer. The association of prenatal level with twin growth discordance was evaluated using conditional logistic regression. High levels of heavy metal elements (thallium in maternal blood and umbilical cord blood of larger twins, vanadium in the placenta of larger twins) and trace elements (iodine in the placenta of larger twins) during pregnancy, as well as low levels of heavy metal elements (strontium in the umbilical cord blood of larger twins, strontium and chromium in the umbilical cord blood of smaller twins, strontium in the placenta of larger twins, molybdenum and lead in the placenta of smaller twins and difference of molybdenum in the placenta of twins), are associated with intertwin birthweight discordance. Univariate regression analyses showed a significant effect of gestational age at delivery and eleven trace element data on intertwin birthweight discordance. Multivariable logistic regression analysis with transformed variables as dichotomous risk factors combined with baseline demographic characteristics showed Tl in maternal blood as an independent risk factor. The model constructed by combining Tl in maternal blood (OR = 54.833, 95% CI, 3.839-83.156) with the gestational week (OR = 0.618, 95% CI, 0.463-0.824) had good predictive power for intertwin birthweight discordance (AUC = 0.871). The sensitivity analysis results indicate that the effect of maternal blood thallium on intertwin birthweight discordance is stable and reliable. To our knowledge, ours is the first case-control study to investigate the association between elevated maternal thallium levels before delivery and twin growth discordance.

Effect of digital storytelling intervention on resilience, self-efficacy and quality of life among patients with non-small cell lung cancer (NSCLC): A randomized controlled trial

ObjectiveTo verify and compare whether the digital stories could effectively improve the resilience, self-efficacy and quality of life of postoperative NSCLC patients. Materials and methodsA total of 90 participants at baseline were randomly assigned to two groups, 45 patients per group. The intervention group received the digital storytelling intervention which includes 4 videos on different topics: positive psychological quality, cultivating healthy living habits, establishing good social support, and insisting on scientific exercise, whereas the control group received only routine care. The resilience, self-efficacy, and quality of life were assessed at baseline (T0) (within 3 days before surgery), immediately after intervention (T1), one month after intervention (T2), and three months after intervention (T3). A linear mixed effects model was used to test the effects of the digital storytelling interventions on resilience, self-efficacy, and quality of life. ResultsThe intervention group reported significantly greater improvements in resilience, self-efficacy, and quality of life (all P < 0.001) at follow-ups than the control group after controlling for age, gender, and education level as covariates. Moreover, the sensitivity analysis results are consistent with the per-protocol, that overall time × group interactions effects were significantly different in resilience, self-efficacy, and quality of life (all P < 0.001). ConclusionThe digital storytelling intervention based on lung cancer survivors’ experience can effectively improve resilience, self-efficacy and quality of life in postoperative lung cancer patients. More comprehensive researches are needed to evaluate the longer-term impacts of the DST and its feasibility for those with more advanced cancer.

An advanced accident avoidance system based on imbalance-control ensemble and deep learning fusion design

The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems.

The causal effects of genetically predicted alcohol consumption on endometrial cancer risk from a Mendelian randomization study

Endometrial cancer (EC) is a common gynecological tumor in females with an increasing incidence over the past few decades. Alcohol consumption has been linked to the occurrence of various cancers; However, epidemiological studies have shown inconsistent associations between alcohol consumption and EC risk. In order to avoid the influence of potential confounding factors and reverse causality in traditional epidemiological studies, we used a method based on genetic principles-Mendelian randomization (MR) analysis to test whether there is a causal relationship between alcohol consumption and EC. MR analysis was conducted using publicly available summary-level data from genome-wide association studies (GWAS). Fifty-seven single nucleotide polymorphisms (SNPs) were extracted as instrumental variables for alcohol exposure from the GWAS and Sequencing Consortium of Alcohol and Nicotine GWAS summary data involving 941,287 participants of European ancestry. SNPs for EC were obtained from the Endometrial Cancer Association Consortium, the Endometrial Cancer Epidemiology Consortium, and the UK Biobank, involving 121,885 European participants. The inverse variance weighted (IVW) method was used as the primary method to estimate the causal effect, and the MR-Egger regression and weighted median method were used as supplementary methods. Sensitivity analyses were conducted using the Mendelian Randomization Pleiotropy RESidual Sum and Outlier global test, MR-Egger intercept test, and leave-one-out analysis to evaluate the impact of pleiotropy on causal estimates. An increase of 1 standard deviation of genetically predicted log-transformed alcoholic drinks per day was associated with a 43% reduction in EC risk [odds ratio (OR) = 0.57, 95% confidence interval (CI) 0.41–0.79, P < 0.001]. Subgroup analysis of EC revealed that alcohol consumption was a protective factor for endometrioid endometrial cancer (EEC) (OR = 0.56, 95% CI 0.38–0.83, P = 0.004) but not for non-endometrioid endometrial cancer (NEC) (OR = 1.36, 95% CI 0.40–4.66, P = 0.626). The MR-Egger regression and weighted median method yielded consistent causal effects with the IVW method. The consistent results of sensitivity analyses indicated the reliability of our causal estimates. Additionally, alcohol consumption was associated with decreased human chorionic gonadotropin (HCG) and insulin-like growth factor 1 (IGF1) levels. This MR study suggests that genetically predicted alcohol consumption is a protective factor for EC, particularly for EEC, and this protective effect may be mediated through the reduction of HCG and IGF1.

Performance analysis and modelling of circular jets aeration in an open channel using soft computing techniques

Dissolved oxygen (DO) is an important parameter in assessing water quality. The reduction in DO concentration is the result of eutrophication, which degrades the quality of water. Aeration is the best way to enhance the DO concentration. In the current study, the aeration efficiency (E20) of various numbers of circular jets in an open channel was experimentally investigated for different channel angle of inclination (θ), discharge (Q), number of jets (Jn), Froude number (Fr), and hydraulic radius of each jet (HRJn). The statistical results show that jets from 8 to 64 significantly provide aeration in the open channel. The aeration efficiency and input parameters are modelled into a linear relationship. Additionally, utilizing WEKA software, three soft computing models for predicting aeration efficiency were created with Artificial Neural Network (ANN), M5P, and Random Forest (RF). Performance evaluation results and box plot have shown that ANN is the outperforming model with correlation coefficient (CC) = 0.9823, mean absolute error (MAE) = 0.0098, and root mean square error (RMSE) = 0.0123 during the testing stage. In order to assess the influence of different input factors on the E20 of jets, a sensitivity analysis was conducted using the most effective model, i.e., ANN. The sensitivity analysis results indicate that the angle of inclination is the most influential input variable in predicting E20, followed by discharge and the number of jets.

A novel linear spectral unmixing-based method for tree decline monitoring by fusing UAV-RGB and optical space-borne data

ABSTRACT Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmembers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.