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Objectives: The aim of this study is to construct a nomogram for predicting subsequent early pregnancy loss in women with a history of pregnancy loss, which may increase well-being and the capacity for managing reproductive options. Materials and methods: We conducted a retrospective analysis of medical records from women with a history of pregnancy loss at the Reproductive Medicine Center of Lanzhou University Second Hospital between January 2019 and December 2022. A cohort of 718 patients was selected for the study. We structured our data into a training set of 575 cases (80% of the cohort) and a test set of 143 cases (20%). To identify significant predictors, we applied a stepwise forward algorithm guided by the Akaike Information Criterion (AIC) to the training set. Model validation was conducted using the test set. For the validation process, we employed various methods to assess the predictive power and accuracy of the model. Receiver Operating Characteristic (ROC) curves provided insights into the model's ability to distinguish between outcomes effectively. Calibration curves assessed the accuracy of the probability predictions against actual outcomes. The clinical utility of the model was further evaluated through Decision Curve Analysis, which quantified the net benefits at various threshold probabilities. In addition, a nomogram was developed to visually represent the risk factors. Results: Among the 36 candidate variables initially considered, 10 key predictors were identified through logistic regression analysis and incorporated into the nomogram. These selected variables include age, education, thrombin time (TT), antithrombin III (AT-III), D-dimer levels, 25-hydroxy Vitamin D, immunoglobulin G(IgG), complement components C4, anti-cardiolipin antibody (ACA) and lupus anticoagulant (LA). In addition, based on clinical experience, the number of previous pregnancy losses was also included as a predictive variable. The prediction model revealed an area under the curve (AUC) of approximately 0.717 for the training set and 0.725 for the validation set. Calibration analysis indicated satisfactory goodness-of-fit, with a Hosmer–Lemeshow test yielding a χ2 value of 7.78 (p = 0.55). Decision curve analysis confirmed the clinical utility of the nomogram. Internal validation confirmed the robust performance of the predictive model. Conclusions: The constructed nomogram provides a valuable tool for predicting subsequent early pregnancy loss in women with a history of pregnancy loss. This nomogram can assist clinicians and patients in making informed decisions regarding the management of pregnancy and improving clinical outcomes. Trial Registration: This study was registered in the Chinese Clinical Trial Registry under the registration number ChiCTR2000039414 on October 27, 2020. The registration was done retrospectively. [ABSTRACT FROM AUTHOR]

Calmodulin is a calcium binding protein that is essential in calcium signalling in the brain. There are many computational models of calcium-calmodulin binding that capture various calmodulin features. However, existing models have generally been fit to different data sets, with some publications not reporting their training and validation performance. Moreover, there is no model comparison using a common benchmark data set as is common practice in other modeling domains. Finally, some calmodulin models have been fit as a part of a larger kinetic scheme, which may have resulted in parameters being underdetermined. We address these three limitations of previous models by fitting the published calcium-calmodulin schemes to a common calcium-calmodulin data set comprising equilibrium data from Shifman et al. and dynamical data from Faas et al. Due to technical limitations, the amount of uncaged calcium in Faas et al. data could not be predicted with certainty. To find good parameter fits, despite this uncertainty, we used non-linear mixed effects modelling as implemented in the Pumas.jl package. The Akaike information criterion values for our reaction rate constants were significantly lower than for the published parameters, indicating that the published parameters are suboptimal. Moreover, there were significant differences in calmodulin activation, both between the schemes and between our reaction rate and those previously published. A kinetic scheme with independent lobes and unique, rather than identical, binding sites fit the data best. Our results support two hypotheses: (1) partially bound calmodulin is important in cellular signalling; and (2) calcium binding sites within a calmodulin lobe are kinetically distinct rather than identical. We conclude that more attention should be given to validation and comparison of models of individual molecules. [ABSTRACT FROM AUTHOR]

Background: Each breast cancer (BC) risk factor has different effects on different populations. However, there are no well-studied and validated BC risk prediction models for Japanese women. We developed accessible predictive models for Japanese women with optimal variables to evaluate risk factors for use by both medical institutions and women for primary BC prevention and to increase the BC screening rate. We evaluated the characteristics and distribution diversity of risk factors in this population. Methods: This retrospective case–control study of 2,494 Japanese women included data from an original, paper-based questionnaire. The logistic regression models included 18 variables from 6 risk factors based on menopausal status (PRE, premenopausal; PERI, perimenopausal; and POST, postmenopausal). Models were evaluated based on the Akaike Information Criterion, area under the receiver operating characteristic curve (AUC), and internal validation. Bootstrap methods for bias correction in discrimination and calibration and standard deviations were calculated by the modified case–control ratio. Results: We created and evaluated 432 candidate models for each group. Notably, BMI, parity, FHx, and smoking history were found to increase risk in all groups. Risk-reducing factors included breastfeeding duration in the PRE and PERI models and regular alcohol consumption in the PERI and POST models. Age reduced risk in the PERI model but increased risk in the POST model. Differences were observed between PRE and PERI versus POST with respect to variable selection in parity and FHx. Our models had moderate discriminatory accuracy. AUCs (confidence intervals) of the PRE, PERI, and POST models were 0.669 (0.625–0.715), 0.669 (0.632–0.702), and 0.659 (0.627–0.693), respectively. Bias-corrected AUCs (standard deviations) were 0.697 (0.041) for PRE, 0.684 (0.033) for PERI, and 0.674 (0.031) for POST, respectively. Our models were well-calibrated after bias correction. Conclusion: Our widely available, simple, and cost-effective models with optimal variables could indicate the characteristics of certain genetic and environmental risk factors for BC in Japanese women. [ABSTRACT FROM AUTHOR]

Lung cancer plays a significant role in the rising rates of cancer around the world, particularly in Japan and China. In Japan, it is the second most common type of cancer and the fourth leading cause of cancer deaths for both men and women. Meanwhile, in China, lung cancer is the top cause of cancer-related deaths, as highlighted by the National Cancer Center’s 2022 report. This study seeks to find out how many genetic mutations are necessary for lung cancer to develop in men and women from these countries. By examining age-specific lung cancer data, we construct multistage models to estimate mutation rates and how cancer cells grow. We also look into the effects of genomic instability and mutator phenotypes on cancer progression. To test our hypotheses, we apply various statistical methods like P-values, Chi-square tests and the Akaike Information Criterion (AIC), where lower P-values provide stronger evidence against the null hypothesis. Our findings indicate that reducing the rate at which cells proliferate could be more effective in lowering lung cancer risk than merely focusing on mutation rates. The number of driver mutations needed for lung cancer varies: in Japan, it ranges from two to nine mutations, around five in China, and between two and six in the United States. These variations are influenced by a mix of lifestyle choices, genetic backgrounds and environmental factors, with gender differences also playing a role in how lung cancer develops. [ABSTRACT FROM AUTHOR]

Background: This issue represents a major global public health concern, accounting for approximately 703,000 deaths each year. Despite Mozambique having the 9th highest suicide rate in the world and the highest in Africa, there is no national data quantifying the burden of suicide attempts. Therefore, this study aimed to assess the prevalence of suicide attempts and identify individual and community-level factors associated with them using mixed-effects logistic regression. This study is crucial for developing early interventions, which can help reduce the risk of suicide and enhance overall mental health outcomes for young people. Method: The data used in this study were drawn from the 2022/2023 Mozambique Demographic and Health Survey (MDHS). A weighted sample of 10,909 individuals (7,716 males and 3,149 females) aged 15–29 was included. The fitted model was evaluated using AIC and BIC, with the model having the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) is another statistical measure used to assess the quality of a model, similar to the Akaike Information Criterion (AIC). The results of the final model were presented as Adjusted Odds Ratios (AOR) with 95% confidence intervals (CI). Variables were considered statistically significant if their p-value was less than 0.05 in the multivariable analysis. Results: About 3.60% (95% CI: 3.33%-4.03%) of participants were seriously considered suicide attempt in the past 12 months of before the survey. Educational status, occupation, marital status, depression, anxiety, and geographic region were significant factors associated with suicidal attempt. Conclusion: The findings of this study enhance our understanding of the intricate relationships between suicide attempts and their predictors. Additionally, the results highlight the need for targeted interventions and mental health promotion strategies that consider the identified individual and community-level factors to reduce suicide rates in Mozambique. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of regional terrestrial water storage (TWS) changes and drought characteristics in Southwest China, encompassing Sichuan Province, Chongqing Municipality, Yunnan Province, and Guizhou Province. Existing geodetic datasets, such as those from the Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On), as well as Global Navigation Satellite System (GNSS) data, face significant challenges related to limited spatial resolution and insufficient station distribution. To address these issues, we propose a novel inversion method that integrates GNSS and GRACE/GFO data by establishing virtual stations for GRACE/GFO data and determining the weight values between GNSS and GRACE/GFO using the Akaike Bayesian Information Criterion (ABIC). This method allows for estimating the TWS changes from December 2010 to June 2023 and monitoring drought conditions in conjunction with hydrometeorological data (precipitation, evapotranspiration, and runoff). The results show strong correlations between TWS changes from the joint inversion and GNSS (0.98) and GRACE/GFO (0.69). The Joint Drought Severity Index (Joint-DSI) indicates five major drought events, with the most severe occurring from July 2022 to June 2023, with an average deficit of 86.133 km³. Extreme drought primarily impacts Sichuan and Yunnan, driven by abnormal precipitation deficits. The joint inversion methodology presented in this study provides a practical approach for monitoring TWS changes and assessing drought characteristics in Southwest China. This paper leverages the complementary strengths of GNSS and GRACE/GFO data and offers new insights into regional water resource management and drought detection. [ABSTRACT FROM AUTHOR]

Background Gorlin syndrome (GS) is an autosomal dominant disorder characterized by a predisposition to basal cell carcinoma and developmental defects. It is caused by pathogenic variants in the PTCH1 or SUFU genes. Objectives To ascertain the effectiveness of molecular screening in a cohort of patients with a suspicion of GS and to describe the patients' clinical and genetic characteristics. Methods In total, 110 patients with a suspicion of GS were studied. The patients were seen at the genetic department of Bichat University Hospital for molecular screening. The patients' clinical and paraclinical data were collected and analysed according to Evans' diagnostic criteria and were compared with molecular information. Results Among 110 probands, only 56% fulfilled Evans' diagnostic criteria. Overall, 75% of the patients who fulfilled those criteria carried a pathogenic variation in PTCH1 or SUFU. We compared the clinical and paraclinical data of 54 probands carrying a PTCH1 or SUFU mutation with 56 probands without identified mutations. Among patients carrying a pathogenic variation in the PTCH1 or SUFU genes, 30 years appears to be the cut-off age after which all patients have clear clinical GS. Indeed, after age 30 years, all patients carrying a PTCH1 or SUFU mutation fulfilled the diagnostic criteria of Evans (82% met the clinical criteria, reaching 100% with complementary examinations such as X-rays and ultrasound). Before 30 years of age, only 37% of patients with mutated genes fulfilled the clinical diagnostic criteria, reaching only 62% with simple complementary exams. We also report 22 new mutations in PTCH1. Conclusions Molecular screening of patients with GS who do not fulfil Evans' diagnostic criteria should only be offered in the first instance to patients under 30 years of age. After age 30 years, careful clinical examination and complementary radiological exams should be enough to eliminate the diagnosis of GS among patients who do not fulfil the diagnostic criteria. [ABSTRACT FROM AUTHOR]

As national emphasis on coal mine safety intensifies, microseismic monitoring technology has become increasingly prevalent. This study presents an integrated algorithm that combines the Short-Term Average/Long-Term Average (STA/LTA) method with the Akaike Information Criterion (AIC) method to enhance the accuracy of P-wave onset detection in traditional microseismic monitoring. The novel algorithm employs an advanced STA/LTA approach to swiftly identify the approximate timing of the microseismic P-wave's initial arrival. Subsequently, it selects an effective time window encompassing the microseismic signal and applies the AIC method to precisely determine the P-wave's onset time. Experiments conducted on noisy microseismic signals from a coal mine demonstrate the algorithm's superior accuracy in initial time picking, even under the complex noise conditions typically encountered in coal mining environments. [ABSTRACT FROM AUTHOR]

Background: Despite the relatively high prevalence of Sickle cell trait (SCT) in Nigeria, there has been little research into the correlates of having children with SCT among Nigerian mothers, particularly in terms of socio-demographic differentials. This study aims to investigate the maternal socio-demographic correlates of having under-five children with SCT in Nigeria. Method: Data from the 2018 Nigeria Demographic and Health Survey (Household Person Recode and Children Recode) were merged. Mothers with at least one under-five child whose genotype was known (n = 7,493) served as the unit of analysis. Three forms of outcome variable were explored. First was the number of children with SCT by each mother. Second, the number of children with SCT was categorized as zero, one, two or more. Lastly, each mother was categorized as either having no child(ren) with SCT or having at least one child with SCT. Subsequently, we assessed multilevel Poisson, ordinal and binary logit models to identify the best fitting model using Akaike and Bayesian Information Criterion. Multilevel binary logistic regression model was identified as best fit used to identify factors associated with having children with SCT. Adjusted Odds Ratio with 95% Confidence Interval (CI) were reported as measures of association. Result: Nearly 62% of the mothers lived in rural areas, 38.2% had no formal education and 37.4% had ever given birth to at least five children. About 26.1% (95% CI = 25.2–26.9) of the mothers had children with SCT. By geographical variation, the Northwest region had the highest proportion of mothers of under-five children with SCT. Results from the multilevel binary logistic regression revealed that women who were traditionalists (AOR = 1.77; 95% CI = 1.04–3.02) were more likely of having children with SCT. Conclusion: Though SCT is a genetic outcome, findings from this study suggest that important socio-demographic factors such as religion, and region of residence are significantly associated with having children with SCT in Nigeria. Sustained efforts on awareness campaigns on SCT are recommended. [ABSTRACT FROM AUTHOR]

Background and objectives: The National Institute for Health and Care Excellence (England's health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However there is limited literature on methods to estimate the ATE, particularly in the case of survival outcomes. Single-arm trials and real-world data are playing an increasing role in health technology assessments, particularly in oncology/rare diseases, generating a need for new ATE estimation methods. This study aimed to present the adaptation and utility of this methodology for survival outcomes. Methods: The approach is based on a "doubly robust" method combining matching with regression adjustment (Austin 2020) using a Weibull model (lowest Akaike information criteria [AIC] specification) to estimate counterfactual event times. As a case study, we compared mosunetuzumab versus rituximab/bendamustine, as a proxy for rituximab/chemotherapy, in 3L+ relapsed/refractory follicular lymphoma. Individual patient data for mosunetuzumab (NCT02500407) and a combination of two rituximab/bendamustine 3L+ follicular lymphoma cohorts (NCT02187861/NCT02257567) were used. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test robustness to different distributional assumptions (log-normal, log-logistic and exponential) or model specifications (second, third and fourth lowest AIC) for event times. Results: The case study found improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.13, 0.91]) and OS (HR 0.30 [95% CI: 0.05, 5.28]) for mosunetuzumab. Consistent findings (HR range 0.25–0.47 and 0.21–0.50 with all CIs excluding/including 1 for PFS/OS, respectively) were observed in sensitivity analyses. Discussion/conclusions: The proposed adaptation expands the range of available approaches for the estimation of the (local) ATE for survival outcomes in health technology assessments using "doubly robust" methods. This approach appeared relatively robust to modelling decisions in our case study. [ABSTRACT FROM AUTHOR]

Summer habitats are critical to bat population persistence as they support multiple life history stages, including maternity colonies, nursery sites, and foraging locations. The tricolored bat (Perimyotis subflavus) is a hibernating North American bat species that uses forested landscapes during summer months; however, information on the summer habitat requirements is limited. The objective of this work was to quantify the characteristics of roost sites selected by tricolored bats during summer months. We captured, tagged, and tracked 15 bats using radio-telemetry to 55 roost locations. At each roost, we recorded roost habitat characteristics and other forest characteristics within a 0.1 ha circular plot surrounding the roost tree and a 1 km buffer at the landscape scale. We repeated these measurements for three random trees per roost tree to characterize available habitat for selection. We used a suite of mixed conditional logistic regression models to test multiple factors known to influence roost-site selection for various bat species and compared using Akaike information criterion to select the best model. The top model at the roost scale demonstrated that roost selection was influenced by roost tree height, while the landscape scale was influenced by deciduous forest and distance to roads. There is a critical information gap for the ongoing recovery of tricolored bats; better understanding of summer habitat and proper forest management implications, as well as information on scale-specific habitat selection, is needed to better understand tricolored bat management needs. [ABSTRACT FROM AUTHOR]

Deep-seabed polymetallic nodules have been recognized as a potential solution to the depletion of many metals that are produced by terrestrial minerals. Mineral resources obtained by deep-seabed mining vehicles significantly affect the economic viability of underwater mining activities. Therefore, an accurate prediction of the harvested mineral resources is significantly important. Probabilistic approach-based prediction, which enhances the accuracy of the economic evaluation, requires a statistical model of the variability of each metal element in the harvested polymetallic nodules. However, the probability distribution of the metal elements in the polymetallic nodules has rarely been studied thus far. A multivariate joint probability distribution must be adopted because the variabilities of these metal elements is correlated with each other. However, multivariate statistical approaches have not been actively studied owing to their highly sophisticated theories. The objective of this study was to establish a systematic framework for modeling a multivariate joint probability distribution of correlated random variables. A case study was performed to characterize the metal elements of the polymetallic nodules using the proposed approach. [ABSTRACT FROM AUTHOR]

Empirical models have been found to be inadequate in both accounting for breakthrough behaviors and reflecting the performance of fixed-bed systems, primarily due to their lack of a robust theoretical foundation. This limitation severely restricts their practical application. To address this difficulty, the adjustable parameters of six empirical models were first determined using the Levenberg–Marquardt iteration algorithm. The fitting quality of these models was subsequently evaluated by several error statistics, including the reduced chi-square (χ2), adjusted coefficient of determination (Adj. R2), residual sum of squares (RSS) and root of mean squared error (RMSE). In addition, the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were employed to further compare these empirical models with the different parameters. The equilibrium loading, breakthrough capacity and saturation capacity were then solved by the int command of MATLAB 2023b software. Meanwhile, the breakthrough time and saturation time were determined by its fzero command. Regardless of whether empirical or mechanistic models were used, the model with the asymmetric S-shaped curve could well describe the measured breakthrough curves. Based on the parallel sigmoidal model, the predicted equilibrium loadings were 101.11, 116.69 and 129.50 mg g−1, respectively, at adsorbent masses of 0.1, 0.3 and 0.5 g. This study aimed to conveniently obtain the critical process parameters through MATLAB software using empirical breakthrough models, thereby providing reliable information for the design and optimization of fixed-bed adsorbers. [ABSTRACT FROM AUTHOR]

Since the Kyoto Protocol entered into force in 2005, researchers have investigated whether it could achieve the expected results. This paper explores the potential structural break caused by the Kyoto Protocol and its impact on the environmental Kuznets curve (EKC) via a novel procedure in three groups of countries using 1951–2017 data. Our proposed procedure is to use the varying coefficient model (VCM) along with the improved Akaike information criterion to detect yearly structural breaks from 1997, the year the Kyoto Protocol was adopted, to 2012, the year the first commitment came to an end. The VCM also allows us to analyze the magnitude and direction of a break, and its estimation technique further enables us to plot an empirical EKC trajectory. The empirical results show that a downward structural break is identified in 2007 for those countries that ratified the Kyoto Protocol and stayed bound to their reduction targets. For these countries, the EKC displays an inverted-U shape with a turning period, not a point. Since the break occurs during this transition, it appears to accelerate the falling rate of emission intensity and promote their progression into the stage of environmental improvement. Therefore, domestic ratification and compliance are the keys to the downward transition of the EKC. Our results shed light on the policy implications of international regulation, especially regarding the importance of domestic compliance with the recent EU carbon border adjustment mechanism. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach aimed at enhancing software reliability by integrating testing coverage within a nonhomogeneous Poisson process (NHPP). The reliability of software holds paramount significance for both developers and users, hinging on precise reliability estimations. In this paper, three real-world datasets are used to examine goodness-of-fit of the proposed model and the performance is compared with 11 other existing NHPP models. The performance of all the models is evaluated using five goodness-of-fit criteria including mean square error (MSE), Akaike's information criterion (AIC), Bayesian information criterion (BIC), predictive risk ratio (PRR) and Pham's criterion (PC). The results reveal that, when it comes to predictive power and goodness-of-fit, our proposed model surpasses the other models. [ABSTRACT FROM AUTHOR]

Although leukemia in the Japanese atomic bomb survivor data has long exhibited upward curvature, until recently this appeared not to be the case for solid cancer. It has been suggested that the recently observed upward curvature in the dose response for the Japanese atomic bomb survivor solid cancer mortality data may be accounted for by flattening of the dose response in the moderate dose range (0.3–0.7 Gy). To investigate this, the latest version available of the solid cancer mortality and incidence datasets (with follow-up over the years 1950–2003 and 1958–2009 respectively) for the Life Span Study cohort of atomic bomb survivors was used to assess possible departures from linearity in the moderate dose range. Linear-spline models were fitted, also up to 6th order polynomial models in dose (higher order polynomials tended not to converge). The organ dose used for all solid cancers was weighted dose to the colon. There are modest indications of departures from linearity for the mortality data, whether using polynomial or linear-spline models. Use of the Akaike information criterion (AIC) suggests that the optimal model for the mortality data is given by a 5th order polynomial in dose. There is borderline significant (P = 0.071) indication of improvement provided by a linear-spline model in the mortality data. The low-dose extrapolation factor (LDEF), which measures the degree of overestimation of low-dose linear slope by the linear slope fitted over some specified dose range, is generally between 1.1–2.0 depending on the dose range, with upper confidence limits that sometimes exceed 10; although LDEF < 1 for the lowest dose range (<0.5 Gy), there are substantial uncertainties, with an upper confidence limit that exceeds 1.6. There are generally only modest indications of departures from linearity for the solid cancer incidence data, whether using polynomial or linear-spline models. In contrast to the mortality data, there are much weaker indications of improvement in fit provided by higher order polynomials, and only weak indications (P > 0.2) of improvement provided by linear-spline models. Nevertheless, use of AIC suggests that the optimal model for the incidence data is given by a 3rd order polynomial. LDEF evaluated over various dose ranges is generally between 1.2–1.4 with upper confidence limits that generally exceed 1.6; although LDEF < 1 for the lowest dose range (<0.5 Gy), there are substantial uncertainties, with an upper confidence limit that substantially exceeds 2.0. In summary, the evidence we have presented for higher order powers than the second in the dose response is not overwhelmingly strong, and is to some extent dependent on dose range. A feature of the dose response, which is reflected in the higher-order polynomials fitted to the data, is a leveling off or even a downturn in the response at doses >2 Gy. The linear-quadratic model is very widely used for modeling of dose response, and has been widely used in radiotherapy oncology applications as part of treatment planning. There is a theoretical basis for this model, based on the two-target model, although the data used to validate this has been mainly in vitro; there may be more complicated interactions than are implied by a two-target model, but the contributions made by these, which would contribute to higher order (than quadratic) powers of dose, may not be very pronounced over moderate ranges of dose. [ABSTRACT FROM AUTHOR]

Rationale: Serum IgG deficiency is associated with morbidity in chronic obstructive pulmonary disease (COPD), but it is unclear whether concentrations in the lower end of the normal range still confer risk. Objectives: To determine if levels above traditional cutoffs for serum IgG deficiency are associated with exacerbations among current and former smokers with or at risk for COPD. Methods: Former and current smokers in SPIROMICS (the Subpopulations and Intermediate Outcome Measures of COPD study) (n = 1,497) were studied: 1,026 with COPD and 471 at risk for COPD. In a subset (n = 1,031), IgG subclasses were measured. Associations between total IgG or subclasses and prospective exacerbations were evaluated with multivariable models adjusting for demographic characteristics, current smoking, smoking history, FEV1 percent predicted, inhaled corticosteroids, and serum IgA. Measurements and Main Results: The 35th percentile (1,225 mg/dl in this cohort) of IgG was the best cutoff by Akaike information criterion. Below this, there was increased exacerbation risk (incidence rate ratio [IRR], 1.28; 95% confidence interval [CI], 1.08–1.51). Among subclasses, IgG1 and IgG2 below the 35th percentile (354 and 105 mg/dl, respectively) were associated with increased risks of severe exacerbation (IgG1, IRR, 1.39; 95% CI, 1.06–1.84; IgG2, IRR, 1.50; 95% CI, 1.14–1.1.97). These associations remained significant when additionally adjusting for a history of exacerbations. Conclusions: Lower serum IgG is prospectively associated with exacerbations in individuals with or at risk for COPD. Among subclasses, lower IgG1 and IgG2 are prospectively associated with severe exacerbations. The optimal IgG cutoff was substantially higher than traditional cutoffs for deficiency, suggesting that subtle impairment of humoral immunity may be associated with exacerbations. [ABSTRACT FROM AUTHOR]

Extreme flood events, laden with significant sediment loads, pose substantial risks to reservoir flood control and sediment deposition management. A multivariate frequency analysis incorporating peak sediment concentration (SSC), peak flood discharge (Q), and 5-day maximum flood volume (V) during the flood serves as a critical foundation for determining the return periods of such extreme events. Currently, the frequency analysis of extreme sediment events at a flood scale predominantly relies on traditional copula functions. However, these conventional copulas, constrained by a single parameter, are inadequate for capturing the intricate correlations among variables, thus impeding the attainment of satisfactory model performance. To address this gap, the present study introduces a vine copula–based approach for the multivariate frequency analysis of extreme sediment-flood events, focusing on modeling the joint behavior of SSC, Q , and V , and simulating the variation in the joint return periods of compound sediment-flood events. Utilizing the Tongguan Station on the Yellow River in China as a case study for model application, the results indicate that (1) the vine copula model exhibits superior performance compared with traditional copula models, with evaluation metrics root-mean square error (RMSE), R2 , and Akaike information criterion (AIC) values of 0.038, 0.972, and −447.7 , respectively, compared with 0.045, 0.957, and −423.2 , providing a more precise description of the joint distribution of SSC-Q-V ; (2) in both bivariate and trivariate joint return period scenarios, an increase in any variable among SSC, Q , or V leads to a rise in their joint return period; in conditional return period scenarios, an increase in any variable results in a decrease in the conditional return period of the remaining variables in combination with that variable; and (3) a specific joint return period corresponds to multiple SSC-Q-V events, and the joint return period for trivariate joint return "AND" scenarios being greater than that for univariate return periods. The research findings provide model support for risk assessment of extreme flood-sediment events and water-sediment regulation schemes. [ABSTRACT FROM AUTHOR]

Aim: To construct a predictive model based on the LODDS stage established for patients with late-onset colon adenocarcinoma to enhance survival stratification. Methods: Late-onset colon adenocarcinoma data were obtained from the public database. After determining the optimal LODDS truncation value for the training set via X-tile software, we created a new staging system by integrating the T stage and M stage. Nomograms of the prognostic model were created after Cox analyses identified independent risk factors for overall survival (OS) and cause-specific survival (CSS) and were validated internally and externally. The efficacy of the nomograms was assessed by calibration, time-dependent area under the curve (AUC) and decision curve analysis (DCA). Finally, the prognoses of the patients were compared by plotting survival curves on the basis of risk scores. Results: A total of 103,291 and 100 patients with late-onset colon adenocarcinoma (50–80 years old) were screened from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. Cox regression analysis revealed independent risk factors for OS and CSS, including age, gender, race, size, LODDS stage, PLN stage, LNR stage, and TNM stage. A comparison of the four models constructed on the basis of different stages revealed that the model constructed with the LODDS stage had the minimum AIC (Akaike information criterion), maximum C-index (concordance index) and time-dependent AUC. Nomograms based on the LODDS stage were constructed and successfully validated for accuracy and clinical utility. Conclusion: For patients with late-onset colon adenocarcinoma, LODDS may achieve optimal predictive performance. Furthermore, compared to the 8th edition of the AJCC classification system, the nomogram based on LODDS stage may demonstrate superior survival prediction capabilities. [ABSTRACT FROM AUTHOR]

AbstractUncertain time series is time-ordered series in which the observation at each moment is explained by uncertain variable. In this study, ridge estimation is applied to estimate unknown parameters in uncertain threshold autoregressive (UTAR) model. The fitted UTAR model is utilized to calculate the predicted value and confidence interval for future data. In addition, the Akaike information criterion (AIC) is proposed to determine the optimum order of the model and delay parameter. Then, a comparative analysis is conducted to indicate the efficacy of the ridge method. Moreover, a numerical example and two practical examples applied to raw coal production and CPI data are used to validate the plausibility of uncertain time series analysis compared to classical time series analysis. Finally, the superiority of the UTAR model is verified by comparing it with a linear time series model. [ABSTRACT FROM AUTHOR]

Recruitment input-output curves of transspinal evoked potentials that represent the net output of spinal neuronal networks during which cortical, spinal and peripheral inputs are integrated as well as motor evoked potentials and H-reflexes are used extensively in research as neurophysiological biomarkers to establish physiological or pathological motor behavior and post-treatment recovery. A comparison between different sigmoidal models to fit the transspinal evoked potentials recruitment curve and estimate the parameters of physiological importance has not been performed. This study sought to address this gap by fitting eight sigmoidal models (Boltzmann, Hill, Log-Logistic, Log-Normal, Weibull-1, Weibull-2, Gompertz, Extreme Value Function) to the transspinal evoked potentials recruitment curves of soleus and tibialis anterior recorded under four different cathodal stimulation settings. The sigmoidal models were ranked based on the Akaike information criterion, and their performance was assessed in terms of Akaike differences and weights values. Additionally, an interclass correlation coefficient between the predicted parameters derived from the best models fitted to the recruitment curves was also established. A Bland-Altman analysis was conducted to evaluate the agreement between the predicted parameters from the best models. The findings revealed a muscle dependency, with the Boltzmann and Hill models identified as the best fits for the soleus, while the Extreme Value Function and Boltzmann models were optimal for the tibialis anterior transspinal evoked potentials recruitment curves. Excellent agreement for the upper asymptote, slope, and inflection point parameters was found between Boltzmann and Hill models for the soleus, and for the slope and inflection point parameters between Extreme Value Function and Boltzmann models for the tibialis anterior. Notably, the Boltzmann model for soleus and the Extreme Value Function model for tibialis anterior exhibited less susceptibility to inaccuracies in estimated parameters. Based on these findings, we suggest the Boltzmann and the Extreme Value Function models for fitting the soleus and the tibialis anterior transspinal evoked potentials recruitment curve, respectively. [ABSTRACT FROM AUTHOR]

Background: The prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries' capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs. Methods: Eight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector. We performed LCA of these indicators, testing 1–5 class solutions, and selecting the best model based on Bayesian Information Criteria (BIC), entropy, corrected Akaike Information Criteria (cAIC), as well as theoretical interpretability. Multivariable linear regression was used to assess the association between capacity to manage diabetes (based on the latent class a country belongs) and diabetes-related deaths and healthcare costs. Results: We included 194 countries in this secondary analysis. Countries were classified into "high capacity" (88.7%) and "limited capacity" (11.3%) countries based on the two-class solution of the LCA (entropy = 0.91, cAIC = 1895.93, BIC = 1862.93). Limited capacity countries were mostly in Africa. Limited capacity countries had significantly higher percentage of their deaths attributable to diabetes (adjusted beta = 1.34; 95% CI: 0.15, 2.53; p = 0.027) compared to high capacity countries even after adjusting for income status and diabetes prevalence. Conclusions: Our findings support the report by the Lancet commission on diabetes, which suggests that differences in diabetes outcomes among countries may be explained by variations in the capacity of and investments made in their health systems. Future studies should evaluate initiatives such as the WHO Global Diabetes Compact that are currently underway to improve the capacity of resource-limited countries. [ABSTRACT FROM AUTHOR]

The distribution of rural settlements is a complex outcome of human adaptation to natural conditions and socioeconomic development throughout history. Scientifically revealing the spatially varying relationships between the distribution of rural settlements and the related factors is fundamental for effective planning and management. In this study, we focus on the North China Plain to analyze the spatially varying relationships between the distribution of rural settlements and the related factors using both traditional statistical and geographically weighted regression models. Our findings reveal that both the number and the area of rural settlements at the county level are increasing from north to south and from west to east. The results of the traditional regression model suggest that total area, total population, road density, precipitation, road length, slope, longitude, and temperature significantly influence the rural settlement area, while those influencing the number of rural settlements are longitude, latitude, road length, road density, river length, and river density. Moreover, the regression coefficients are constant in the global model, while both the magnitude and the sign of the corresponding parameters in the local model are spatially varying. However, the value of the coefficients in the global model are within the range of the coefficients in the local model and most coefficients in the local model share the same sign with that the global model. Our results also reveal that the local model outperforms the global model with the same explanatory variables, indicating a smaller Akaike's information criterion (AIC) and a reduced Moran's I in model residual. Finally, this study also highlights the importance of the cautious and scientific interpretation of the varying relationships, especially when the unexpected results are obtained. [ABSTRACT FROM AUTHOR]

AbstractThis paper analyses competing risk models with high dimensional covariates when the number of observations is comparatively quite small. This gives rise to a very typical situation where most statistical estimates become unstable and non-unique. Thus dimension reduction by carrying out variable selection becomes a very imperative part of the study. This is performed using both classical and Bayesian tools. Lifetimes under each risk is assumed to follow either the Weibull or the exponential distribution and a total of four models is formed using a combination of these risks. Bayesian analysis of the resulting four models are performed via Markov chain Monte Carlo methods. A real life application is provided for adenocarcinoma micro-array data. Finally, model selection is carried out using deviance information criterion. [ABSTRACT FROM AUTHOR]

Objective: Accurate and early prediction of neurodevelopmental impairment is a crucial endeavor in caring for very preterm infants (<31 weeks' gestation). Sequential cranial ultrasound is the standard of care for the evaluation of preterm brain injury. However, there is no consensus on the timing and frequency of ultrasound screening. At Izaak Walton Killam (IWK) Health Centre, Halifax, Canada, four-time points for routine ultrasound of very preterm infants are performed at weeks 1, 2, 6, and term age. The hypothesis behind this work is that a three-time-point model will be appropriate for neurodevelopmental impairment prognostication. Materials and methods: In this retrospective cohort, all very preterm infants (220–306 weeks) born between January 2004 and December 2018 with a neurodevelopmental assessment at 36 months corrected age were included. Three prediction models of neurodevelopmental impairment were compared: A reference model including the gestational age, infant sex, and 2-week and 6-week ultrasound A model including the gestational age, infant sex, and 6-week ultrasound A model including the gestational age, infant sex, and 2-week ultrasound Results: Of 786 eligible preterm infants born during the study period, 656/786 survivors were included in the analysis (mean gestational age 275 weeks, mean birth weight 1,133 g, and 55% male infants). At 36 months of corrected age, 30% developed neurodevelopmental impairment. All three models provided comparable discrimination areas under the curve (AUC) of neurodevelopmental impairment at 36 months of corrected age. Both the 6-week and the reference model had similar AUC of 0.68 (95% CI 0.63–0.72) and were not noticeably different from the 2-week model (AUC 0.66 (95% CI 0.61–0.70)). The 6-week model provided the best prediction with the lowest Akaike information criterion (AIC) of 766 for the 6-week-only model, AIC 769 for combined weeks 2 and 6 (reference model), and AIC 784 for the 2-week-only model. Conclusion: In this cohort of very preterm infants, a model including 6-week ultrasound only was comparable to a reference model combining 2-week and 6-week ultrasound and showed nearly identical predictive performance of neurodevelopmental impairment at 36 months corrected age across a broad set of metrics; thus, it is redundant to do both the 2-week and 6-week ultrasound. Clinical relevance statement: Late ultrasound at 6 weeks of age provided comparable diagnostic and prognostic information to a reference model combining 2-week and 6-week ultrasound and, if anything, was slightly superior to the 2-week ultrasound model, across a broad set of metrics. The 2-week ultrasound can be eliminated with no impact on the prediction of neurodevelopmental impairment at 36 months, promoting prudent resource allocation and stewardship in healthcare. [ABSTRACT FROM AUTHOR]

Purpose: Appendicular skeleton markers are commonly used for maturity assessment for Adolescent Idiopathic Scoliosis (AIS) patients. Traditionally, Risser has been a standard skeletal maturity assessment method. More recently, Sanders classification (SSMS), as a more comprehensive system, became popular, especially in decision-making for Vertebral Body Tethering (VBT). Thumb-Ossification Composite Index (TOCI), using ossification of thumb epiphyses, has been claimed to more accurately stage patients around their peak height velocity. However, growth peaks may occur separately at lower limbs and trunk. Hence, Cervical Vertebral Maturity (CVM), using cervical spine morphology, possesses a potential to better estimate spinal growth as it uses axial skeleton markers instead of appendicular skeleton markers. The aim of the study was to compare various axial and appendicular skeletal maturity assessment methods for longitudinal growth and curve modulation after VBT. Methods: A retrospective analysis of prospectively collected data was conducted. Skeletal maturity was determined using Risser, SSMS, TOCI and CVM for each patient. Crosstabulations of axial vs. appendicular markers were formed to analyze their concordance and discordance. Logistic and logarithmic regression models were run to assess longitudinal growth (postoperative height gain and leg-length growth) and curve modulation (follow-up instrumented Cobb correction after index operation), respectively. Models were compared using Akaike information criterion (AIC). Results: 34 patients (32 F/2 M, mean age: 12.8 ± 1.5 years, mean follow-up: 47.7 (24–80) months) were included. The median preoperative maturity stages were: Risser: 1 (-1–4), SSMS: 4 (1–7), TOCI: 6 (1–8) and CVM: 4 (1–6). At latest follow-up, all patients reached skeletal maturity. Concordance and discordance were observed between axial vs. appendicular systems that demonstrated a range of possible distributions of CVM, where trunk peak height velocity occurred before, simultaneously with or after the standing height peak height velocity. R-squared values for Risser, SSMS, TOCI and CVM were 0.701, 0.783, 0.810 and 0.811, respectively, for prediction of final height; 0.759, 0.821, 0.831 and 0.775 for final leg-length, and 0.507, 0.588, 0.668 and 0.673 for curve modulation. Delta AIC values demonstrated that different skeletal maturity assessment methods provided distinctive information regarding follow-up height gain, leg-length growth and curve behavior. Conclusions: Risser score provided considerably less information for all three outcome variables. TOCI and SSMS provided substantial information regarding remaining leg-length assessments, while in terms of assessment of total height gain and curve modulation after surgery, CVM and TOCI offered substantial information and SSMS offered strong information. Mutual use of axial and appendicular markers may provide valuable insight concerning timing of surgery and magnitude of surgical correction. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) sea level retrievals and tide gauges at 40 globally distributed stations spanning from about 4.5 to 18 years are compared on a site-by-site basis, in terms of noise background, rate and seasonal variations by using the weighted least squares estimation (LSE) along with the Maximum likelihood estimation (MLE). The result shows that monthly GNSS-IR data agree well with tide gauges for most stations except the site Tuktoyaktuk, Canada. The mean correlation is 0.95 and the mean root mean square difference is 2.9 cm, respectively. The discrepancies of rate and seasonal amplitude estimates are within ± 1 cm for most stations. We confirm both the two sea level data exhibit temporal correlation, which has a great effect on the rate uncertainty estimates. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) favor Matérn and first-order autoregressive (AR1) the preferred stochastic model for the daily and monthly mean sea level time series, respectively. Owing to the data span dependence for the rate uncertainty estimates, to get an accuracy of sub-mm/yr in linear rate using the weighted LSE, at least 30 years of data (depends on data quality) is required. We recommend using long time series and a proper stochastic model for the rate estimation of sea level. [ABSTRACT FROM AUTHOR]

The spatial correlation of coseismic slip is a necessary input for generating stochastic seismic rupture models, which are commonly used in seismic and tsunami hazard assessments. To date, the spatial correlation of individual earthquakes is characterized using finite fault models by finding the combination of parameters of a von Kármán autocorrelation function that best fits the observed autocorrelation function of the finite fault model. However, because a priori spatial correlation conditions (i.e. not in the data) are generally applied in finite fault model generation, the results obtained using this method may be biased. Additionally, robust uncertainty estimates for spatial correlations of coseismic slip are generally not performed. Considering these limitations in the classic method, here, a method is developed based on a Bayesian formulation of Finite Fault Inversion (FFI) with positivity constraints. This method allows for characterizing the spatial correlation of coseismic slip and its uncertainties for an earthquake by using samples of coseismic slip from a posterior probability density function (PDF). Furthermore, a Bayesian model selection criterion called Akaike Bayesian Information Criterion (ABIC) is applied to objectively choose between different prior spatial correlation schemes before computing the posterior, to reduce subjectivity due to this prior condition. The ABIC is calculated using an approximate analytical expression of Bayesian evidence. The method is applied to simulated P waves, demonstrating that model selection allows for objectively estimating the most suitable prior spatial correlation scheme in FFI. Additionally, the target spatial correlation of coseismic slip is accurately recovered using samples from the posterior PDF, as well as their uncertainties. Moreover, in the simulated experiment, it is shown that a non-robust choice of the prior spatial correlation scheme can significantly bias the estimated spatial correlations of coseismic slip. We apply our method to observed P waves from the 2015, Illapel earthquake (⁠|$M_{\rm w} = 8.3$|⁠), finding that the spatial correlation of coseismic slip of this earthquake is better described by a von Kármán ACF, with mean correlation lengths of around 47 km and Hurst parameter of 0.58. We conclude that using our method reduces biases associated with prior spatial correlation conditions and allows for robust estimation of spatial correlations of coseismic slip and their uncertainties. [ABSTRACT FROM AUTHOR]

Background: Overall, the prognosis of patients with chronic lymphocytic leukemia (CLL) in the early phase of the disease (Rai 0, Binet A) is favorable; some patients never require therapy. However, some patients require intervention shortly after diagnosis. In the past decade, several risk scores (RS) have been developed to predict disease progression, yet some patients are misclassified. On the other hand, IGHV subset 2 (IGHV2) predicts poor outcomes. Methods: A retrospective and multicentric study was conducted to compare the accuracy of five different RS (IPS‐E, CR0, AIPS‐E, CLL‐IPI, and Barcelona‐Brno) to predict disease progression in 781 stage A previously untreated patients with CLL. As an exploratory analysis, it was further investigated whether the inclusion of the IGHV2 as a poor prognostic parameter improved the accuracy of RS. Results: All the scores identified a similar group of patients with CLL in early stage with low‐, intermediate‐, and high‐risk progression. Discrimination was high and similar in all RS (c‐index = 0.74–0.79, area under the curve = 0.7–0.75), as well as calibration (p =.98) and parsimony, although CLL‐IPI showed the best results (Akaike information criterion = 441). A total of 34.4% of patients were categorized within the same RS and concordance was at least moderate between RS. Conclusion: Moreover, the results suggest that IGHV2 may improve the accuracy of RS. [ABSTRACT FROM AUTHOR]

To offer guidance for using Brazilian tensile strength (BTS) to estimate UCS of heterogeneous rocks, this study uses sandstone (fine or coarse grain) and gneiss (0°, 45°, 90° inclined anisotropy) to investigate the influence of grain size or anisotropy on the correlations of UCS-BTS. According to the regression analysis, there is no significant equation of UCS-BTS for rocks with vertical anisotropy. The grain size variation or multidirectional anisotropy can result in a decrease in the determination coefficient value of correlations. Then, coarse grain size or vertical anisotropy deteriorates the statistical performance of correlations between UCS and BTS, reflected by the Akaike Information Criterion and performance index. For rocks with fine grain size or 45° inclined anisotropy, the data points of estimated UCS are clustered uniformly around the exact estimation line. Finally, the accuracy of predicted UCS via BTS declines obviously following the varying grain size or different anisotropy orientations. Using empirical formulas with different grain sizes or anisotropy properties can generate significant errors in estimated UCS. To predict UCS, BTS should be extracted from rocks with single grain size magnitude or unidirectional anisotropy. Moreover, the Brazilian test parallel to the anisotropy cannot be used to derive the correlation of UCS-BTS. [ABSTRACT FROM AUTHOR]

In the current era of immune therapy, lenvatinib (LEN) continues to be vital for treating unresectable hepatocellular carcinoma (uHCC) patients. This study investigates the importance of nutritional status in the prognosis of uHCC patients receiving LEN and evaluates a new prognostic scoring system that combines the geriatric nutritional risk index (GNRI) and systemic inflammatory response. From 2018 to 2022, 484 uHCC patients treated with LEN (384 males, median age 73). Prognostic value was compared between GNRI and C-reactive protein (CRP) scoring (GNRI-C score), GNRI, and neo-Glasgow prognostic score (neo-GPS). Evaluation was based on the Akaike information criterion (AIC) and concordance index(c-index). Median progression-free survival (mPFS) was 9.3/6.8/4.6 months for GNRI no-risk/low-risk/moderate-to-major risk (p < 0.01, AIC 4742.4/c-index 0.585). Median overall survival (mOS) was 27.8/15.2/9.5 months (p < 0.01, AIC 3433.34/c-index 0.639). For GNRI-C score, mPFS was 10.8/7.1/5.6/4.0 months (score 0/1/2/3) (p < 0.01, AIC 4732.82/c-index 0.6), while neo-GPS showed mPFS of 8.5/5.1/5.2 months (p < 0.01, AIC 4745.89/c-index 0.562). For mOS, GNRI-C score demonstrated 28.6/20.0/10.1/8.4 months (score 0/1/2/3) (p < 0.01, AIC 3420.27/c-index 0.652), while neo-GPS indicated 21.0/12.4/4.5 months (p < 0.01, AIC 3468.84/c-index 0.564). The newly devised GNRI-C score, incorporating nutritional and inflammatory markers, could offer improved prognostic predictions for uHCC patients treated with LEN. [ABSTRACT FROM AUTHOR]

In this study, the best parameter estimator for the scale parameter (θ) of the inverse Rayleigh distribution was determined based on a comparison of the maximum likelihood estimator (MLE) method, the Bayesian generalized squared error loss function (SELF), the Bayesian linear exponential loss function (LINEX LF), and the Bayesian entropy loss function (ELF). The prior distribution chosen was the non-informative prior, namely the Jeffrey prior, and the informative prior using the exponential distribution. The estimator evaluation method used was based on the smallest value of the Akaike information criterion (AIC), corrected Akaike information criterion (AICc), and Bayesian information criterion (BIC). Based on simulation studies and real data, it was found that the best parameter estimator on the data for the scale parameter (θ) of the inverse Rayleigh distribution is the Bayes ELF prior exponential (̂θEE) [ABSTRACT FROM AUTHOR]

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log‐likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability (hmi2) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability (ha2) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between −0.75 ± 0.15 (P%‐SCS) and 0.95 ± 0.11 (MY‐ADM). Additive genetic correlations (ra) ranged between −0.54 ± 0.05 (MY‐P%) and 0.99 ± 0.01 (MY‐ADM) and phenotypic correlations (rp) ranged from −0.30 ± 0.01 (MY‐F%) to 0.93 ± 0.01 (MY‐ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top‐ranked animals implying re‐ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re‐ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended. [ABSTRACT FROM AUTHOR]

Objective: The therapeutic alliance is one of the most stable predictors of symptom burden over the course of therapy. So far, this effect has only been examined on the basis of sessions. Continuous-time models (CTM) allow this relationship to be modeled as a continuous process in which the actual time interval between measurements is considered. The aim of the present study was to compare the fit of discrete-time models (DTM) of the alliance–symptom relationship with CTM using different time variables (sessions vs. actual time interval). Method: Data from 1,413 patients at a university psychotherapy outpatient clinic were analyzed. The alliance and symptom burden were assessed each session with the Bernese Session Report and the Hopkins Symptom Checklist-Short-Form, respectively. Different DTM and CTM were estimated using the R-package ctsem and compared in their fit via the Akaike information criterion. Results: CTMs with session as the time unit fitted the data best. Significant negative within-person effects of alliance and symptom burden were found. These effects showed a significant positive correlation, implying that individuals with a stronger effect of the alliance on symptom severity also showed a stronger effect of symptom severity on the alliance. Conclusions: When modeling the relationship of symptom severity and alliance, it seems to be of more importance to capture the fact that a session occurred than to capture the exact time intervals between sessions. Future studies should examine this finding for other psychotherapeutic factors. Interpersonal factors might explain the positive association of the reciprocal alliance–symptom effects. What is the public health significance of this article?: This study underscores the critical role of the therapeutic alliance in predicting symptom burden during psychotherapy. Moreover, the findings from this study suggest that for patients who particularly benefit from an improvement in the alliance, the therapeutic relationship in turn suffers more from a worsening of symptoms. The utilization of continuous-time models in assessing the alliance–symptom relationship, with session as the time unit, reveals a nuanced understanding of this dynamic process. [ABSTRACT FROM AUTHOR]

The editorial discusses the rise of methodological reviews, particularly mixed methods research systematic methodological reviews (MMR-SMRs), which analyze trends in mixed methods research across various disciplines. The authors highlight challenges faced in conducting MMR-SMRs, such as defining scope, inclusion criteria, search strategies, data extraction, and publication. They suggest ways to address these challenges and emphasize the importance of improving reporting quality and developing guidelines for MMR-SMRs. The editorial aims to raise awareness of pitfalls in conducting MMR-SMRs and provide recommendations for researchers to enhance the rigor and efficiency of their reviews. [Extracted from the article]

Requirements to understand and forecast the behavior of complex macroeconomic interactions mandate the use of high-dimensional macroeconometric models. The Global Vector Autoregressive (GVAR) modeling technique is very popular among them and it allows researchers and policymakers to take into account both the complex interdependencies that exist between various economic entities and the global economy through the world's trade and financial channels. However, determining the cross-section unit size while using this approach is not a trivial task. In order to address this issue, we suggest an objective procedure for the detection of the size of the cross-country aggregation in GVAR models. While doing so, we depart from the Akaike Information Criterion (AIC) and propose an analytical modification to it, mainly employing an ad hoc approach without violating Akaike's main principles. To supplement the theoretical results, small sample performances of those procedures are studied in Monte Carlo experiments as well as implementing our approach on real data. The numerical results suggest that our ad hoc modification of AIC can be used to determine the structure of the cross-section unit dimension in GVAR models, allowing the researchers and policymakers to build parsimonious models. [ABSTRACT FROM AUTHOR]

Utilising biomass waste as a renewable energy source has gained a lot of interest as a means of reducing reliance on fossil fuels. Among the many types of biomass, tamarind fruit peel (Tamarindus indica), which is commonly discarded, holds promise as a feedstock for briquette production due to its favorable combustion properties. In order to ascertain the higher heating value (HHV) of briquettes made from tamarind peel waste, this study employs proximate analysis, which takes into account the materials' moisture content, ash content, volatile matter, and fixed carbon. In this research, three models Wahid, Nhuchhen and Afzal, and Kieseler were comparatively analyzed to predict the HHV of tamarind peel briquettes. The study also explored the effects of particle size and binder ratio on briquette performance, specifically on HHV and combustion properties. Tamarind peel was processed into different powder sizes, mixed with varying binder ratios, and formed into briquettes. The three predictive models were statistically evaluated using R2, the Bayesian information criterion (BIC), and the akaike information criterion (AIC) after the briquettes were proximally analysed. With an R2 of 0.96, the Wahid model showed the highest prediction accuracy, followed by Nhuchhen (0.93) and Kieseler (0.78), according to the data. Wahid's model also had the lowest AIC (45.3) and BIC (47.1), indicating it is the most efficient model for predicting the HHV of tamarind peel briquettes. According to the study, the best combinations for improved briquette performance were determined when particle size and binder ratio were found to have a substantial impact on the combustion characteristics. By turning leftover tamarind peel into a renewable energy source, this study promotes environmentally friendly waste management while also fostering energy innovation. The findings provide valuable insights into the optimization of biomass briquette production and highlight the potential of tamarind peel as an underutilized biomass resource. [ABSTRACT FROM AUTHOR]

A new one-parameter distribution is proposed for circular data based on wrapping. Most distributions constructed via wrapping do not yield elementary expressions for their mathematical properties. Yet the new distribution yields elementary expressions for all of its mathematical properties. Better fits of the new distribution over the three-parameter distribution due to Jones and Pewsey10 and six other wrapped distributions including four that have two parameters each are shown for at least two data sets. Better fits were assessed in terms of probability plots, density plots, values of Akaike information criterion and values of Bayesian information criterion. [ABSTRACT FROM AUTHOR]

Background: Despite declining COVID-19 incidence, healthcare workers (HCWs) still face an elevated risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. We developed a diagnostic multivariate model to predict positive reverse transcription polymerase chain reaction (RT-PCR) results in HCWs with suspected SARS-CoV-2 infection. Methods: We conducted a cross-sectional study on episodes involving suspected SARS-CoV-2 symptoms or close contact among HCWs in Bogotá, Colombia. Potential predictors were chosen based on clinical relevance, expert knowledge, and literature review. Logistic regression was used, and the best model was selected by evaluating model fit with Akaike Information Criterion (AIC), deviance, and maximum likelihood. Results: The study included 2498 episodes occurring between March 6, 2020, to February 2, 2022. The selected variables were age, socioeconomic status, occupation, service, symptoms (fever, cough, fatigue/weakness, diarrhea, anosmia or dysgeusia), asthma, history of SARS-CoV-2, vaccination status, and population-level RT-PCR positivity. The model achieved an AUC of 0.79 (95% CI 0.77–0.81), with 93% specificity, 36% sensitivity, and satisfactory calibration. Conclusions: We present an innovative diagnostic prediction model that as a special feature includes a variable that represents SARS-CoV-2 epidemiological situation. Given its performance, we suggest using the model differently based on the level of viral circulation in the population. In low SARS-CoV-2 circulation periods, the model could serve as a replacement diagnostic test to classify HCWs as infected or not, potentially reducing the need for RT-PCR. Conversely, in high viral circulation periods, the model could be used as a triage test due to its high specificity. If the model predicts a high probability of a positive RT-PCR result, the HCW may be considered infected, and no further testing is performed. If the model indicates a low probability, the HCW should undergo a COVID-19 test. In resource-limited settings, this model can help prioritize testing and reduce expenses. [ABSTRACT FROM AUTHOR]

Introduction: Although the Tumor-Node-Metastasis (TNM) staging system is widely used for staging lung squamous cell carcinoma (LSCC), the TNM system primarily emphasizes tumor size and metastasis, without adequately considering lymph node involvement. Consequently, incorporating lymph node metastasis as an additional prognostic factor is essential for predicting outcomes in LSCC patients. Methods: This retrospective study included patients diagnosed with LSCC between 2004 and 2018 and was based on data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The primary endpoint of the study was cancer-specific survival (CSS), and demographic characteristics, tumor characteristics, and treatment regimens were incorporated into the predictive model. The study focused on the value of indicators related to pathological lymph node testing, including the lymph node ratio (LNR), regional node positivity (RNP), and lymph node examination count (RNE), in the prediction of cancer-specific survival in LSCC. A prognostic model was established using a multivariate Cox regression model, and the model was evaluated using the C index, Kaplan–Meier, the Akaike information criterion (AIC), decision curve analysis (DCA), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive efficacy of different models was compared. Results: A total of 14,200 LSCC patients (2004–2018) were divided into training and validation cohorts. The 10-year CSS rate was approximately 50%, with no significant survival differences between cohorts (p = 0.8). The prognostic analysis revealed that models incorporating LNR, RNP, and RNE demonstrated superior performance over the TNM model. The LNR and RNP models demonstrated better model fit, discrimination, and reclassification, with AUC values of 0.695 (training) and 0.665 (validation). The RNP and LNR models showed similar predictive performance, significantly outperforming the TNM and RNE models. Calibration curves and decision curve analysis confirmed the clinical utility and net benefit of the LNR and RNP models in predicting long-term CSS for LSCC patients, highlighting their value in clinical decision-making. Conclusion: This study confirms that RNP status is an independent prognostic factor for CSS in LSCC, with predictive efficacy comparable to LNR, with both models enhancing survival prediction beyond TNM staging. [ABSTRACT FROM AUTHOR]