Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods.

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Bayesian joint modeling of multivariate longitudinal and survival outcomes using Gaussian copulas.

There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial.

Factors associated with different types of freight crashes: A macro-level analysis.

INTRODUCTION: Despite evidence showing higher fatality rates in freight-related crashes, there has been limited exploration of their spatial distribution and factors associated with such distribution. This gap in the literature primarily stems from the focus of existing studies on micro-level factors predicting the frequency or severity of injuries in freight crashes. The present study delves into the factors contributing to freight crashes at the neighborhood level, particularly focusing on different types of freight crashes: collisions involving a freight vehicle and a passenger vehicle, crashes between freight vehicles, and freight vehicle-non-motorized crashes. METHOD: This study analyzes traffic crash data from the urbanized region of Seoul, collected between 2016 and 2019. To effectively deal with spatial autocorrelation and model different types of crashes in a unified framework, a Bayesian multivariate conditional autoregressive model was employed. RESULTS: Findings show substantial differences in the factors associated with various types of freight crashes. The predictors for crashes between freight vehicles diverge significantly from those for freight vehicle-non-motorized crashes. Crashes between freight vehicles are relatively more influenced by road network structure, while freight crashes involving non-motorized users are relatively more affected by the built environment and freight facilities than the other crash types examined. Freight vehicle-passenger vehicle crashes fall into an intermediate category, sharing most predictors with either of the other two types of freight crashes. CONCLUSIONS AND PRACTICAL APPLICATIONS: The findings of this study offer valuable lessons for transportation practitioners and policymakers. They can guide the formulation of effective land use policies and infrastructure planning, specifically designed to address the unique characteristics of different types of freight crashes.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Career stage-specific predictors of reflective ability among clinical nurses.

AIMS: To identify career stage-specific factors that influence reflective ability in clinical nurses and the relative strength of these effects. DESIGN: Exploratory cross-sectional study. METHODS: Between August and September 2019, nursing professionals working at general hospitals (n = 1169) completed a questionnaire on reflective ability and its suspected influencing factors. Participants were grouped by career stage as defined by years of nursing experience. Each factor's predictive strength regarding different dimensions of reflective ability was analysed separately in each group via stepwise multiple regression. RESULTS: Reflective ability was significantly influenced by support for personal growth from superiors and seniors among first-year participants and professional identity formation among those in their second or later years. Furthermore, it was significantly influenced by self-confidence in nursing practice in years 4-5, effort to improve knowledge and skills in years 6-9 and role model presence in years 10-19. CONCLUSION: Career stage-specific predictors of reflective ability were related to nurses' environment and changes in the roles expected of them. Support measures aimed at improving this capacity should emphasize factors characteristic of the career stage(s) of nursing professionals. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Identifying the influencing factors of nurses' reflective ability can improve the same, deepen nurses' views on nursing, help them develop an intentional nursing practice and contribute to the improvement of the quality of nursing practice. IMPACT: This study is the first to identify career stage-specific predictors of reflective ability in clinical nurses and the relative strength of their effects. Reflective ability was affected by growth support from superiors and seniors in first-year nurses and formation of nursing identity in second-year nurses. Additionally, nurses' environment and various roles affected their reflective ability. Hospitals should build an appropriate environment for nurses and develop the concept of 'oneself as a nurse' among nurses. PATIENT OR PUBLIC CONTRIBUTION: This study was conducted with the approval of an ethical review committee that included general citizens. Furthermore, the research results were reviewed by general citizens prior to dissemination, and we received their opinions as to whether the writing was sufficiently clear and whether the information required by the audience was included. We improved the content to be disseminated based on relevant opinions provided.

ShrinkCRISPR: a flexible method for differential fitness analysis of CRISPR-Cas9 screen data.

BACKGROUND: CRISPR screens provide large-scale assessment of cellular gene functions. Pooled libraries typically consist of several single guide RNAs (sgRNAs) per gene, for a large number of genes, which are transduced in such a way that every cell receives at most one sgRNA, resulting in the disruption of a single gene in that cell. This approach is often used to investigate effects on cellular fitness, by measuring sgRNA abundance at different time points. Comparing gene knockout effects between different cell populations is challenging due to variable cell-type specific parameters and between replicates variation. Failure to take those into account can lead to inflated or false discoveries. RESULTS: We propose a new, flexible approach called ShrinkCRISPR that can take into account multiple sources of variation. Impact on cellular fitness between conditions is inferred by using a mixed-effects model, which allows to test for gene-knockout effects while taking into account sgRNA-specific variation. Estimates are obtained using an empirical Bayesian approach. ShrinkCRISPR can be applied to a variety of experimental designs, including multiple factors. In simulation studies, we compared ShrinkCRISPR results with those of drugZ and MAGeCK, common methods used to detect differential effect on cell fitness. ShrinkCRISPR yielded as many true discoveries as drugZ using a paired screen design, and outperformed both drugZ and MAGeCK for an independent screen design. Although conservative, ShrinkCRISPR was the only approach that kept false discoveries under control at the desired level, for both designs. Using data from several publicly available screens, we showed that ShrinkCRISPR can take data for several time points into account simultaneously, helping to detect early and late differential effects. CONCLUSIONS: ShrinkCRISPR is a robust and flexible approach, able to incorporate different sources of variations and to test for differential effect on cell fitness at the gene level. These improve power to find effects on cell fitness, while keeping multiple testing under the correct control level and helping to improve reproducibility. ShrinkCrispr can be applied to different study designs and incorporate multiple time points, making it a complete and reliable tool to analyze CRISPR screen data.

Predictors for late genitourinary toxicity in men receiving radiotherapy for high-risk prostate cancer using planned and accumulated dose.

Background and purpose: Significant deviations between bladder dose planned (DP) and dose accumulated (DA) have been reported in patients receiving radiotherapy for prostate cancer. This study aimed to construct multivariate analysis (MVA) models to predict the risk of late genitourinary (GU) toxicity with clinical and DP or DA as dose-volume (DV) variables. Materials and methods: Bladder DA obtained from 150 patients were compared with DP. MVA models were built from significant clinical and DV variables (p < 0.05) at univariate analysis. Previously developed dose-based-region-of-interest (DB-ROI) metrics using expanded ring structures from the prostate were included. Goodness-of-fit test and calibration plots were generated to determine model performance. Internal validation was accomplished using Bootstrapping. Results: Intermediate-high DA (V30-65 Gy and DB-ROI-20-50 mm) for bladder increased compared to DP. However, at the very high dose region, DA (D0.003 cc, V75 Gy, and DB-ROI-5-10 mm) were significantly lower. In MVA, single variable models were generated with odds ratio (OR) < 1. DB-ROI-50 mm was predictive of Grade ≥ 1 GU toxicity for DA and DP (DA and DP; OR: 0.96, p: 0.04) and achieved an area under the receiver operating curve (AUC) of > 0.6. Prostate volume (OR: 0.87, p: 0.01) was significant in predicting Grade 2 GU toxicity with a high AUC of 0.81. Conclusions: Higher DA (V30-65 Gy) received by the bladder were not translated to higher late GU toxicity. DB-ROIs demonstrated higher predictive power than standard DV metrics in associating Grade ≥ 1 toxicity. Smaller prostate volumes have a minor protective effect on late Grade 2 GU toxicity.

Correlation between metabolic syndrome and hyperuricemia in perimenopausal women / 中华内分泌代谢杂志

Objective:To assess the prevalence of metabolic syndrome(MS) and its relationship with hyperuricemia(HUA) in perimenopausal women in Anning city, Yunnan province.Methods:This is a cross-sectional survey. In May 2021, a multi-stage stratified sampling method was used to collect demographics and clinical data [ethnicity, living community, height, weight, waist circumference, blood pressure, fasting plasma glucose, triglycerides(TG), serum uric acid, high density lipoprotein-cholesterol(HDL-C), alanine transaminase(ALT), etc] in a total of 6 721 perimenopausal women aged 45-60 years.Results:A total of 6 721 perimenopausal women were included in this study. The prevalences of MS and HUA were 14.05%(95% CI 13.22%-14.88%) and 6.46%(95% CI 5.88%-7.07%), respectively. The average age, HDL-C, urea, direct bilirubin, and albumin levels in the perimenstrual HUA population were lower than those in the non-HUA population while the levels of TG, ALT, heart rate, body mass index(BMI), and creatinine were higher(all P<0.05). The prevalence of HUA in perimenopausal women with ethnic minorities and family history of chronic diseases was higher than that in Han nationality and without family history of chronic diseases. The prevalence of MS in perimenopausal women was increased with the increase of serum uric acid( Z=-15.313 8, P<0.001). Multivariate logistic regression model showed that HUA was positively correlated with MS( OR=1.526, 95% CI 1.192-1.954) after adjusting for covariates such as BMI and ethnicity, and the incidence of MS in perimenopausal women in HUA group was 1.526 folds higher than that in non-hyperuricemia group. Conclusion:HUA is highly positively correlated with MS in perimenopausal women. The management of uric acid level in perimenopausal women should be strengthened.

Examining the impact of sex differences and the COVID-19 pandemic on health and health care: findings from a national cross-sectional study.

Objective: To examine the association of the coronavirus disease 2019 (COVID-19) pandemic, the association of sex, and the joint association of sex and the COVID-19 pandemic with health communication, physical activity, mental health, and behavioral health. Materials and Methods: We drew data from the National Cancer Institute's 2020 Health Information National Trends Survey. We described and compared the characteristics of social determinants of health, physical activity, mental health, alcohol use, patterns of social networking service use, and health information data sharing. Analyses were weighted to provide nationally representative estimates. Multivariate models (multiple linear regression, multiple logistic regression, and multinomial logistic model) were used to assess the sole and joint association with sex and pandemic. In addition, we applied the Bonferroni correction to adjust P values to decrease the risks of type I errors when making multiple statistical tests. Results: Females were more likely to use mobile health and health communication technologies than males, and the difference increased after the pandemic. The association between sex and mental health was significant after the COVID-19 pandemic. Females were more likely to experience depression or anxiety disorders. Both males and females had a slight decrease in terms of the quantity and intensity of physical activity and females were less likely to perform moderate exercise and strength training regularly. Males were likely to drink more alcohol than females. Conclusion: The COVID-19 pandemic amplifies the differences between males and females in health communication, physical activity, mental health, and behavioral health. Intersectional analyses of sex are integral to addressing issues that arise and mitigating the exacerbation of inequities. Responses to the pandemic should consider diverse perspectives, including sex and gender.

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: An eco-epidemiological community-based cohort study in Brazil.

Background: Zoonotic spillover from animal reservoirs is responsible for a significant global public health burden, but the processes that promote spillover events are poorly understood in complex urban settings. Endemic transmission of Leptospira, the agent of leptospirosis, in marginalised urban communities occurs through human exposure to an environment contaminated by bacteria shed in the urine of the rat reservoir. However, it is unclear to what extent transmission is driven by variation in the distribution of rats or by the dispersal of bacteria in rainwater runoff and overflow from open sewer systems. Methods: We conducted an eco-epidemiological study in a high-risk community in Salvador, Brazil, by prospectively following a cohort of 1401 residents to ascertain serological evidence for leptospiral infections. A concurrent rat ecology study was used to collect information on the fine-scale spatial distribution of 'rattiness', our proxy for rat abundance and exposure of interest. We developed and applied a novel geostatistical framework for joint spatial modelling of multiple indices of disease reservoir abundance and human infection risk. Results: The estimated infection rate was 51.4 (95%CI 40.4, 64.2) infections per 1000 follow-up events. Infection risk increased with age until 30 years of age and was associated with male gender. Rattiness was positively associated with infection risk for residents across the entire study area, but this effect was stronger in higher elevation areas (OR 3.27 95% CI 1.68, 19.07) than in lower elevation areas (OR 1.14 95% CI 1.05, 1.53). Conclusions: These findings suggest that, while frequent flooding events may disperse bacteria in regions of low elevation, environmental risk in higher elevation areas is more localised and directly driven by the distribution of local rat populations. The modelling framework developed may have broad applications in delineating complex animal-environment-human interactions during zoonotic spillover and identifying opportunities for public health intervention. Funding: This work was supported by the Oswaldo Cruz Foundation and Secretariat of Health Surveillance, Brazilian Ministry of Health, the National Institutes of Health of the United States (grant numbers F31 AI114245, R01 AI052473, U01 AI088752, R01 TW009504 and R25 TW009338); the Wellcome Trust (102330/Z/13/Z), and by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/JCB0020/2016). MTE was supported by a Medical Research UK doctorate studentship. FBS participated in this study under a FAPESB doctorate scholarship.

Identification of early predictors for infected necrosis in acute pancreatitis.

BACKGROUND: In acute pancreatitis, secondary infection of pancreatic necrosis is a complication that mostly necessitates interventional therapy. A reliable prediction of infected necrotizing pancreatitis would enable an early identification of patients at risk, which however, is not possible yet. METHODS: This study aims to identify parameters that are useful for the prediction of infected necrosis and to develop a prediction model for early detection. We conducted a retrospective analysis from the hospital information and reimbursement data system and screened 705 patients hospitalized with diagnosis of acute pancreatitis who underwent contrast-enhanced computed tomography and additional diagnostic puncture or drainage of necrotic collections. Both clinical and laboratory parameters were analyzed for an association with a microbiologically confirmed infected pancreatic necrosis. A prediction model was developed using a logistic regression analysis with stepwise inclusion of significant variables. The model quality was tested by receiver operating characteristics analysis and compared to single parameters and APACHE II score. RESULTS: We identified a total of 89 patients with necrotizing pancreatitis, diagnosed by computed tomography, who additionally received biopsy or drainage. Out of these, 59 individuals had an infected necrosis. Eleven parameters showed a significant association with an infection including C-reactive protein, albumin, creatinine, and alcoholic etiology, which were independent variables in a predictive model. This model showed an area under the curve of 0.819, a sensitivity of 0.692 (95%-CI [0.547-0.809]), and a specificity of 0.840 (95%-CI [0.631-0.947]), outperforming single laboratory markers and APACHE II score. Even in cases of missing values predictability was reliable. CONCLUSION: A model consisting of a few single blood parameters and etiology of pancreatitis might help for differentiation between infected and non-infected pancreatic necrosis and assist medical therapy in acute necrotizing pancreatitis.

Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea.

Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high-throughput phenotyping (normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31-0.71) and narrow-sense heritability (0.13-0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0-S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6-50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.

[Application of multivariate models in whole process control of solid preparations].

The production of solid preparations is a multi-unit and multi-step system and is a whole process chain. Its quality is affected by many factors such as material properties and process parameters. As an important analysis tool, multivariate models play an important role in pharmaceutical monitoring. Besides, multivariate models can comprehensively understand the multi-factor relationship between material properties, process parameters, and quality attributes of products, thereby promoting the whole process optimization and controlling the drug production quality. This paper summarized the application of commonly used multivariate models in the process of solid preparations, which provides a certain reference for the process modeling of Chinese medicinal preparations.

phyloMDA: an R package for phylogeny-aware microbiome data analysis.

BACKGROUND: Modern sequencing technologies have generated low-cost microbiome survey datasets, across sample sites, conditions, and treatments, on an unprecedented scale and throughput. These datasets often come with a phylogenetic tree that provides a unique opportunity to examine how shared evolutionary history affects the different patterns in host-associated microbial communities. RESULTS: In this paper, we describe an R package, phyloMDA, for phylogeny-aware microbiome data analysis. It includes the Dirichlet-tree multinomial model for multivariate abundance data, tree-guided empirical Bayes estimation of microbial compositions, and tree-based multiscale regression methods with relative abundances as predictors. CONCLUSION: phyloMDA is a versatile and user-friendly tool to analyze microbiome data while incorporating the phylogenetic information and addressing some of the challenges posed by the data.

EviCor: Interactive Web Platform for Exploration of Molecular Features and Response to Anti-cancer Drugs.

Experimental biologists are often left alone with the task to download, process, and analyze big datasets in order to perform correlation or other simpler analyses. To address these issues, we introduce EviCor, a handy toolbox for exploration of data from large public resources such as The Cancer Genome Atlas and The Cancer Cell Line Encyclopedia, complemented with follow-up information on same samples, which couples omics datasets with drug response profiles (https://www.evicor.org/). The data was processed for easy retrieval from the server-side database and includes pre-computed drug-feature correlation tables. Using information from multiple independent sources, the task-oriented web interface presents relations between phenotype, single-molecule, and pathway variables with graphical, statistical, and network analysis tools. Building custom multivariate models is enabled via user-friendly web interface and programmatic access via RESTinterface. Project code is available at https://github.com/aveviort/HyperSet.

Forecasting carbon emissions using MGM(1,m|λ,γ) model with the similar meteorological condition.

Carbon emission is a common concern of the international community and effectively predicting its future trend is necessary for emission reduction planning. Considering that the change trend of carbon emissions is unstable, more attention should be paid to the correction effect of new information on the development trend. Therefore, based on the traditional MGM(1,m) model, this paper introduces the new information priority operator λ and nonlinear parameter γ to strengthen the role of new information, further constructs three comparison models of MGM(1,m|λ), MGM(1,m|γ) and MGM(1,m|λ,γ).Then we apply the new model to the carbon emission prediction of different regions (cities, countries and continents) and different trends (fluctuating, rising and declining). The results illustrate that the new model has higher prediction accuracy, and adding dynamic parameters is a scientific and practical method to improve the forecasting ability of the grey forecasting model. Finally, we analyze the current situation and future development trend of carbon emissions, and put forward reasonable suggestions.

Cross-National Trends of Chronic Back Pain in Adolescents: Results From the HBSC Study, 2001-2014.

Chronic back pain is a common problem that negatively impacts the wellbeing of many adolescents. Prior research suggests that the prevalence of chronic back pain has increased over the last decades, but research on this issue is scarce, single country-based, and has yielded inconsistent results. This study aimed to examine trends in the prevalence of chronic back pain over time in adolescents aged 11, 13 and 15, using data from the Health Behavior in School-aged Children (HBSC) survey. We conducted a secondary analysis of data from 650,851 adolescents, retrieved from four waves (2001/02, 2005/06, 2009/10 and 2013/14) of HBSC data from 33 countries or regions.  The prevalence of back pain was higher (1) in each successive survey over time (18.3% in 2001/02, 19.3% in 2005/06, 20.4% in 2009/10 and 21.6% in 2013/14), (2) in girls (21.9%) compared to boys (17.8%), and (3) in older adolescents compared to younger ones (14.5% in 11-year-olds, 19.6% in 13-year-olds and 25.5% in 15-year-olds). The increase in prevalence from 2001/02 to 2013/14 was more marked in older girls compared to younger girls, and in older boys compared to younger boys, and it ranged between 1% for 11-year-old boys and 7% for 15-year-old girls. More resources should be allocated to the prevention and treatment of chronic back pain in adolescents, especially for older girls. PERSPECTIVE: The prevalence of chronic back pain in adolescents has increased from 2001-2002 to 2013-2014, especially in older adolescent girls. These findings underline the need of further research to understand the reason behind the increasing trend, and what programs are better suited to prevent chronic back pain among adolescents.

Study on Meso-Structure Evolution in Granular Matters Based on the Contact Loop Recognition and Determination Technique.

On the mesoscopic scale, granular matter is tessellated into contact loops by a contact network. The stability of granular matter is highly dependent on the evolution of contact loops, including the number and area evolutions of contact loops with different geometric shapes (which can reflect the mechanical variables in the macroscale). For the features of numerous loops with complex geometry shapes in contact network images, a contact loop recognition and determination technique was developed in this study. Then, numerical biaxial compression tests were performed by the discrete element method (DEM) to investigate how the meso-structural indexes evolve along with the macro-mechanical indexes. The results show that the proposed Q-Y algorithm is effective in determining the geometric types of contact loops from contact network images. The evolution of contact loops is most active in the hardening stage, during which the number percentages of L3 (loops with three sides) and L6+ (loops with six or more sides) show opposite evolution patterns. For the area percentage, only L6+ increases while others decrease. Considering the meso-structural indexes (number percentage and area percentage of loops) are sensitive to the change of macro-mechanical indexes (deviatoric stress, axial strain, and volumetric strain) in the hardening stage. Multivariate models were established to build a bridge between the meso-structure and the macro-mechanics.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology.

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan.

PURPOSE: The clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans. METHODS: We retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa. RESULTS: Univariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case. CONCLUSION: Our multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.