Akaike Information Criterion Value Research Articles

Prediction of Ki-67 expression and malignant potential in gastrointestinal stromal tumors: novel models based on CE-CT and serological indicators.

To identify more reliable imaging and serological indicators for predicting Ki-67 expression and malignant potential in gastrointestinal stromal tumors, as well as to develop a preoperative prediction model with clinical utility. This study retrospectively analyzed patients with gastrointestinal stromal tumors (GIST) diagnosed at the First Affiliated Hospital of Jinzhou Medical University between May 2018 and May 2024. Univariate logistic analyses, two-way stepwise regression, P-value stepwise regression, and LASSO regression were employed to screen for Ki-67 high expression and high malignant potential risk factors associated with GIST. Models were established using various regression methods; Nomograms, calibration curves, and clinical decision curves were generated for the two best prediction models. Two-way stepwise regression analysis revealed that diameter (P=0.037; OR=1.22; 95% CI: 1.01 - 1.46), growth pattern (extraluminal type: P=0.028; OR=3.54; 95% CI: 1.14 - 10.94), enhancement model (P=0.099; OR=0.39; 95% CI: 0.12 - 1.20), EVFDM (P=0.069; OR=0.43; 95% CI: 0.17 - 1.07), PLR (P=0.099; OR=3.06; 95% CI: 0.81 - 11.59), and OPNI (P=0.058; OR=2.38; 95% CI: 0.97 - 5.84) are identified as independent risk factors for Ki-67 expression. Utilizing the two-way stepwise regression model to predict Ki-67 expression, the area under the curve (AUC) for the training group was 0.865 (95% CI: 0.807-0.922), while for the validation group it was 0.784 (95% CI: 0.631-0.937). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for the training group were 153.360 and 174.619, respectively. Two-way stepwise regression analysis revealed that volume (P < .001, OR = 1.06; 95% CI: 1.03 - 1.09), contour (P = 0.066; OR = 0.17; 95% CI: 0.05 - 0.62), ulcer (P = 0.094; OR = 0.16; 95% CI: 0.03 - 0.98), IBSC (P = 0.008; OR = 5.27; 95% CI: 1.57 - 17.69), and OPNI (P = 0.045; OR = 0.22; 95% CI: 0.05 - 0.96) are independent risk factors for malignant potential. Utilizing the two-way stepwise regression model to predict malignant potential, the AUC for the training group was 0.950 (95% CI: 0.920 - 0.980), while for the validation group it was 0.936 (95% CI: 0.867 - 1.000). The AIC and BIC values for the training group were 96.330 and 114.552, respectively. Diameter, growth pattern, enhancement pattern, EVFDM, PLR, and OPNI are independent risk factors for GIST with high Ki-67 expression. Additionally, volume, contour, ulceration, IBSC, and OPNI serve as independent risk factors for GIST with high malignant potential. The preoperative models developed using CT images can predict the malignant potential and Ki-67 expression status of GIST to a certain extent. When combined with serological indicators, these models' predictive performance can be further enhanced.

Predicting the body weight of crossbred Holstein × Zebu dairy cows using multivariate adaptive regression splines algorithm.

This study aimed to estimate live body weight from body measurements for Holstein × Zebu dairy cows (n = 156) reared under conditions of humid tropics in Mexico using multivariate adaptive regression splines algorithm (MARS) with several train-test proportions. The body measurements included withers height, rump height, hip width, heart girth, body length and diagonal body length. The data were divided into 65:35, 70:30 and 80:20 split data for training and testing sets, respectively. The MARS algorithm was used to construct a prediction model, which predicted the body weight from the body measurements of the test dataset. The results emphasized that the MARS algorithm had an explanation rate for 80:20 train and test set of 0.836 and 0.711, respectively, with minimum Akaike information criterion values. This indicates that it is a reliable way of predicting body weight from body measurements. The results suggest that body weight prediction can be performed with the MARS algorithm in a reliable way, therefore, this algorithm may be a useful tool for animal breeders and researchers in the development of feeding and selection-aimed approaches.

Estimation of dominance genetic effects on pre-weaning efficiency-related traits in Baluchi sheep

Abstract This study was conducted to estimate the relative contribution of dominance genetic effects to efficiency-related traits including Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Baluchi sheep. To this end, each trait was analysed with a series of 12 animal models which were identical for fixed and additive genetic effects but differed for combinations of dominance genetic and maternal effects. The Akaike's information criterion (AIC) was used to rank models. (Co)variances between traits were estimated using bivariate analyses. For all traits studied, according to AIC values, models containing the dominance genetic effects provided a better data fit than otherwise identical models. By including dominance genetic effects in the model, additive genetic variance did not change, but a significant decrease was observed in the residual variance (24, 19 and 25% for KR, EF and RGR, respectively). Estimates of dominance heritability $( {\boldsymbol h}_{\boldsymbol d}^ 2 )$ were 0.20 ± 0.05, 0.17 ± 0.05 and 0.19 ± 0.07 for KR, EF and RGR, respectively, more than corresponding estimates of additive heritability ${\bf ( }{\boldsymbol h}_{\boldsymbol a}^{\bf 2} {\bf ) }$ as 0.14 ± 0.02, 0.09 ± 0.03 and 0.13 ± 0.02, respectively. Dominance genetic correlations between traits were 0.89 ± 0.17 (KR-EF), 0.86 ± 0.20 (KR-RGR) and 0.93 ± 0.21 (EF-RGR). Additive genetic correlations between traits were 0.84 ± 0.05 (KR-EF), 0.78 ± 0.04 (KR-RGR) and 0.83 ± 0.04 (EF-RGR). The Spearman correlation between additive breeding values including and excluding dominance genetic effects were close to unity either for all animals or top ranked animals. Since presence of dominance genetic effects increased the model power to fit the data, inclusion of these effects in the genetic evaluation models for Baluchi sheep was recommended.

Meadow degradation reduces microbial β diversity and network complexity while enhancing network stability

Continuing meadow degradation under the dual impacts of climate change and human activities has altered microbially-mediated biogeochemical cycles. However, microbial diversity and network stability in response to meadow degradation and their relationships to environmental variables remain insufficiently understood. This study focused on alpine meadows with varying degrees of degradation in the hinterland of the Qinghai-Tibetan Plateau. It analyzed the soil microbial diversity (α and β diversity) and ecological network topology characteristics under the degradation of alpine meadows. Furthermore, the extent to which these factors elucidate the environmental changes observed during degradation was explored. The results demonstrated that: (1) the α diversity of soil microbial under degradation of alpine meadows exhibited an increasing trend, although not significantly. The β diversity (community dissimilarity) exhibited a significant decreasing trend. These indicated that meadow degradation resulted in microbial homogenization intra- and inter-community. In the light degradation stage, the β diversity of bacteria and fungi significantly increased with geographic and environmental distance rise. Only bacterial β diversity significantly increased with geographical distance in the moderate degradation stage and environmental distance in the heavy degradation stage, respectively. This indicated that meadow degradation possibly altered microbial assembly, especially for fungi. (2) The degree of soil bacterial nodes exhibited a significant decreasing trend with degradation and fungal eigencentrality demonstrated a significant decreasing trend. After removing the identical nodes, the natural connectivity of the fungal network exhibited a significant decreasing trend with degradation. This suggested that the stability of the soil microbial network increased while the network complexity decreased under meadow degradation. These changes possibly marked the irreversible course of alpine meadow degradation. (3) The Mantel test indicated that bacterial diversity and network topological features were more strongly associated with environmental factors than those of fungi. Akaike information criterion (AIC) values and the upset matrix plots of variance partitioning based on Redundancy analysis (RDA) indicated that higher rates of soil factors, such as soil pH, organic carbon (SOC) content, and total potassium (TK), elucidated changes in microbial community diversity and network topological features. This study highlights the importance of soil microorganisms for ecological stability and the role of microorganisms should be emphasized in future efforts to restore alpine grasslands.

Constraints on Interacting Dark Energy Models from the DESI Baryon Acoustic Oscillation and DES Supernovae Data

Abstract The recent results from the first-year baryon acoustic oscillations (BAO) data released by the Dark Energy Spectroscopic Instrument (DESI), combined with cosmic microwave background (CMB) and Type Ia supernova (SN) data, have shown a detection of significant deviation from a cosmological constant for dark energy. In this work, we utilize the latest DESI BAO data in combination with the SN data from the full 5 yr observations of the Dark Energy Survey and the CMB data from the Planck satellite to explore potential interactions between dark energy and dark matter. We consider four typical forms of the interaction term Q. Our findings suggest that interacting dark energy (IDE) models with Q ∝ ρ de support the presence of an interaction where dark energy decays into dark matter. Specifically, the deviation from ΛCDM for the IDE model with Q = β H 0 ρ de reaches the 3σ level. These models yield a lower value of Akaike information criterion than the ΛCDM model, indicating a preference for these IDE models based on the current observational data. For IDE models with Q ∝ ρ c, the existence of interaction depends on the form of the proportionality coefficient Γ. The IDE model with Q = β H ρ c yields β = 0.0003 ± 0.0011, which essentially does not support the presence of the interaction. In general, whether the observational data support the existence of interaction is closely related to the model. Our analysis helps to elucidate which type of IDE model can better explain the current observational data.

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

Effect of chemical modification on hydraulic conductivity of stratified porous media

ABSTRACT Understanding hydraulic conductivity is essential in porous media as it dictates the capacity of fluids to permeate through these substances. This research investigated the impact of chemical modification using fly ash on hydraulic conductivity. The coefficient of hydraulic conductivity along the bedding plane at different angles of orientations was predicted by employing machine learning techniques such as gene expression programming (GEP), artificial neural networks (ANNs), and regression tree (RT), considering various influencing parameters. As the amount of fly ash in the porous medium rises, the hydraulic conductivity decreases. The statistical results demonstrate that the proposed ANN model effectively forecasted the horizontal hydraulic conductivity. It achieved the highest R2 value (0.989), the lowest mean absolute percentage error value (5.772), the lowest scatter index value (0.040), and the lowest Akaike Information Criterion value (−2,359.914) compared to the GEP, RT, and previously established approaches. Moreover, a novel equation has been proposed for determining horizontal hydraulic conductivity, which can be used for any stratified medium, distinguishing this work from previous ones. These findings can inform global practices in mitigating groundwater contamination, enhancing irrigation efficiency, and improving the design of subsurface fluid flow systems, making it a valuable contribution to both academic and practical applications worldwide.

Time-series forecasting of particulate organic carbon on the Sunda Shelf: Comparative performance of the SARIMA and SARIMAX models

Conducting in-depth studies of carbon dynamics in the Southeast Asia’s Sunda Shelf waters is challenging. Particulate organic carbon (POC) is a key component that can be monitored in the region, but ocean assessment requires advanced research and POC monitoring, especially in relation to ocean dynamics and forecasts. Accurate forecasting of POC in marine environments is difficult due to the complexity of oceanic processes and the influence of various environmental factors. Statistical forecasting models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) or SARIMA with Exogenous Factors (SARIMAX) are useful for this purpose. This study compares these two time-series forecasting models to assess the role of exogenous factors in predicting POC variability in Indonesian seas. The models were specified as SARIMA (3, 1, 3)x(2, 0, 0, 60) and SARIMAX (3, 1, 3)x(2, 0, 0, 60). We obtained Akaike Information Criterion (AIC) values of 1237.767 for the SARIMA model and 1207.341 for SARIMAX[exog=SST]. The SARIMA model's validation metrics were 6.94 % for MAPE, 10.80 for RMSE, and 0.65 for the correlation coefficient, outperforming the SARIMAX model. While SARIMAX incorporates additional environmental variables, SARIMA outperforms it based on MAPE, RMSE, and correlation coefficients. Our findings reveal that only sea surface temperature can significantly influence POC forecasts, thus providing a new perspective on oceanic carbon dynamics. From 2022–2030, POC levels are expected to range between 108.3 and 135.9 mg/m³, with a mean value of 120.9±5.4 mg/m³, lower than that observed from 2002 to 2022, which was 118.8±11.9 mg/m³. The highest peak in POC is predicted for the end of 2026 and 2027. The annual trend shows that the highest POC values correspond to the northwest monsoon season, with the lowest during the intermonsoon period.

Comparative Performance of ARIMA and ARCH Models On Time Series of Monthly Libyan Brent Oil Price

Many economic and financial time series exhibit a phenomenon known as heteroscedastic, where the variance of the series changes over time. This research study focuses on financial time series modelling, with special application to modelling the price of Libyan Brent Oil. In particular, the theory of univariate nonlinear time series analysis is explored and applied to the price Libyan of Brent Oil, spanning from January 2000 to December 2010. The data was obtained from Bullent of the Platts Market Wire of Statistics. This study aims to evaluate the performance of ARIMA as a linear model and ARCH as a nonlinear modelling data. Multiple time series models were considered for fitting the data, and the best ARIMA models were selected based on the Akaike Information Criteria (AIC). ARIMA (0, 1, 1), and ARIMA (1, 1, 0) were identified as the best models. After estimating the parameters of the selected models, model checking revealed that these models were not suitable for modeling the data, as they lacked validity according to the test of squared residuals. The goodness of fit was assessed using the AIC, and based on minimum AIC values, the best fit ARCH models were found to be ARIMA (0, 1, 1) - ARCH (1). After estimating the parameters of the selected model, a series of diagnostic and forecast accuracy tests were performed. Based on this model, a twelve-month forecast of the price of Libyan Brent crude was made.

Assessment of heart rate measurements by commercial wearable fitness trackers for early identification of metabolic syndrome risk

Metabolic syndrome increases the risks of cardiovascular diseases, type 2 diabetes, and certain cancers. The early detection of metabolic syndrome is clinically relevant, as it enables timely and targeted interventions. In the current study, we aimed to investigate the association between metabolic syndrome and heart rate measured using wearable devices in a real-world setting and compare this association with that for clinical resting heart rate. Data from 564 middle-aged adults who wore wearable devices for at least 7 days with a minimum daily wear time of 20 h were analyzed. The results showed significantly elevated all-day, sleeping, minimum, and inactive heart rates in men with pre-metabolic or metabolic syndrome compared with those in normal individuals, whereas sleeping heart rate and heart rate dips were significantly increased and decreased, respectively, in women with metabolic syndrome. After adjusting for confounders, every 10-beats-per-minute increment in all-day, sleeping, minimum, and inactive heart rates in men corresponded to odds ratios of 2.80 (95% confidence interval 1.53–5.44), 3.06 (1.57–6.40), 4.21 (1.87–10.47), and 3.09 (1.64–6.29), respectively, for the presence of pre-metabolic or metabolic syndrome. In women, the association was significant only for heart rate dips (odds ratio = 0.49 [95% confidence interval 0.25–0.96] for every 10% increment). Models incorporating inactive or minimum heart rate in men and heart rate dip in women demonstrated better fits, as indicated by lower Akaike information criterion values (170.3 in men and 364.9 in women), compared with models that included clinical resting heart rate (173.4 in men and 369.1 in women). These findings suggest that the heart rate indices obtained from wearable devices may facilitate early identification of metabolic syndrome.

New method to model the wind speed distribution: method of decile

Energy is a primary need for individuals and societies; the new era has revolutionized everything, from cell phones to daily used equipment in houses; all need electrical energy. Advanced countries have already managed energy demand according to their need. Renewable energy is better than conventional energy sources such as gas, coal, and nuclear fuel. Researchers have continuously worked on renewable energy to make it as helpful as possible. Mathematical modeling and new methods of harnessing energy are the leading fields researchers are working in. Wind energy is ubiquitous; however, it depends on a cut-off speed. In numerous places worldwide, the wind speed surpasses the cut-off speed. Such locations are good candidates for generating electricity from wind energy. Proper modeling of wind energy enables one to know available wind potential. An effort has been made to model wind speed; a new method known as the method of decile has been developed. The method works using the first and last decile of the wind distribution. The Weibull parameters, average wind speed, and errors have been calculated and compared to five other methods. Akaike information criterion (AIC) was used to see the suitability of the new method. The AIC values for the new method were calculated for 12 months of wind speed data in 2016; the minimum values occurred for the novel method. It shows that the new method is the best among the other five methods. The annual output energy is estimated using E-5 (by Ryse Energy company) horizontal axis wind turbine and evaluated as 6900 kWh at Hyderabad, the highest among the three sites.

Cosmology in f (R, T) modified gravity : unified dark matter and dark energy model constrained by current observations

The proposed cosmological model deals with modified Chaplygin gas (MCG) in f(R, T) = R + ξ(T) gravity, where R is the Ricci Scalar and T is the trace of energy-momentum tensor. The function ξ(T) is chosen as the linear combination of power law and logarithmic form under flat Friedmann-Lemaitre-Robertson-Walker space-time. The model is compatible with current observational data (Pantheon Type Ia Supernova) and confronts the deceleration and state parameters effectively. The model can predict the Big Rip in future infinity and can also tackle the difficulties related to the fine-tuning and the coincidence problem practically. Further, we have numerically solved the modified Friedmann equations in f(R, T) gravity and also performed a Markov Chain Monte Carlo analysis to obtain the best fit parameters of this current cosmological model. These best parameters are then used to compute the cosmographic parameters, i.e., the deceleration parameter, the jerk parameter and the snap parameter. Significantly, the cosmographic test has given valuable insights into the dynamics of the current cosmological model and also enriched us to understand about the cosmic evolution of the accelerated Universe. Additionally, the Statefinder diagnostics and O m diagnostics have provided deeper insights into the dynamics of the cosmic expansion and also provided information to distinguish between both the cosmological frameworks. Furthermore, these tests also reveal that at late times, the current model goes beyond the phantom region. Again, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) have provided enough support for the current model under consideration, indicating that the present model exhibits a plausible explanation. However, the ΛCDM model has emerged with the lowest AIC value which suggests its relatively superior fit compared to the current model. Finally, our current model aligns well with several recent observations and unveils various intriguing features about the late time accelerated Universe.

FORECASTING CBOE OPENING VALUES: AN EFFECTIVE ARIMA APPROACH WITH TRANSFORMATION AND MODEL DIAGNOSTICS

This study investigates the use of an ARIMA model, coupled with Monte Carlo simulation, to forecast the opening value of a Volatility Index (VIX) time series. The data obtained from the Chicago Board Options Exchange (CBOE) for the years 1992–2019 have been transformed into stationary data using a detrend method and first-order difference. The Augmented Dickey-Fuller (ADF) test is used to ensure the data are adequately transformed. The autocorrelation function (ACF) and partial ACF (PACF) are then used to identify series with serial correlation and determine whether an autoregressive (AR) model is appropriate. Significant moving average (MA) lags are also determined for model identification. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) are used to find the best-fit ARIMA model. Among the evaluated ARIMA models, which ranged from a random walk ARIMA(0, 1, 0) to ARIMA(2, 1, 2), the ARIMA(2, 1, 2) model was found to be the most optimal, exhibiting the lowest AIC and BIC values. This model was then used to forecast the opening value for the year 2014, using 2013 data as the real data. The generated ARIMA(2, 1, 2) model demonstrates reasonable alignment with the actual 2014 data, suggesting its potential for forecasting in this context. Additionally, Monte Carlo simulations are employed to assess the model’s robustness by generating a range of potential outcomes, providing a more comprehensive understanding of the uncertainty associated with the forecasts.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches

BackgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.MethodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).ResultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.ConclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure.Trials registrationORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Evaluation of lymph node ratios and log odds of positive nodes as prognostic indicators in primary organ malignancy

BackgroundStaging of malignant tumours based on positive lymph node number and not taking into account the actual number of examined lymph nodes during surgery may lead to bias and fallacious results. The ratio of positive and resected lymph nodes (LNR) and log odds of positive nodes (LODDS) emerged as novel prognostic markers in deciding the future course of management better than the parameter pN in predicting cancer-specific survival. We aim to evaluate and compare the prognostic value of pN, LNR and LODDS in common primary organ malignancies like breast, head and neck, gastric and cervical cancers. MethodsThis was an ambidirectional observational study conducted from September 2018 to August 2021 with one year (from 2017 to 2018) for retrospective data collection archived from records. It included patients affected by breast, head and neck, gastric and cervical cancers and were followed up for a period of 3 years from the date of diagnosis to perform a holistic survival analysis. The sample was grouped by American Joint Committee on Cancer N stage, LNR and LODDS, and analysed using Kaplan–Meier and multivariate Cox proportional hazard models. ResultsThe study included 202 breast cancer patients, 85 head and neck cancer patients, 100 gastric cancer patients and 52 cervical cancer patients. Multivariate Cox regressions analysis demonstrated a lower Akaike Information Criterion (AIC) value for LODDS and LNR followed by pN stage. ConclusionThe novel indicators LNR and LODDS better predicted the biological behaviour irrespective of lymph node number and allowed clinicians to correctly stratify patients for better postoperative management.

Nomogram based on burn characteristics and the national early warning score to predict survival in severely burned patients

BackgroundExtensive burns are associated with a high mortality rate. Early prediction and action can reduce mortality. The National Early Warning Score (NEWS) is considered the best early warning score for predicting mortality. However, there has been no assessment conducted on the clinical prognostic significance of NEWS in individuals suffering from severe burns. The objective of this research was to establish a nomogram based on burn characteristics and the NEWS to predict survival in severely burned patients. MethodsA retrospective analysis was performed on 335 patients diagnosed with extensive burns from 2005 to 2021 in the Department of Burn Surgery of XX Hospital. Univariate and multivariate analyses were used to determine independent prognostic factors. A nomogram was developed using these prognostic factors and its internal validity was assessed through bootstrap resampling. ResultsThe results of multivariate analysis showed that the independent factors affecting the prognosis of severe burn patients were age, full-thickness burn, creatinine, absorptive injury, tracheotomy, and the NEWS, all of which were identified to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion values of the nomogram demonstrated superior goodness-of-fit in predicting severe burns compared to NEWS, with lower scores (195.21 vs. 201.24; 221.91 vs. 224.12, respectively). The bootstrap-adjusted concordance index (C-index) of the nomogram yielded a higher value of 0.923 (95% CI 0.892–0.953), compared to NEWS which had a C-index of 0.699 (95% CI 0.628–0.770). The calibration curves demonstrated excellent agreement between predicted probabilities and observed outcomes in the nomogram analysis. Furthermore, decision curve analysis indicated promising clinical utility for the proposed nomogram model. By applying an appropriate cutoff value derived from receiver operating characteristics curve analysis, it was observed that the high-risk group identified by the nomogram exhibited a significantly higher mortality rate than the low-risk group. ConclusionThis study introduces an innovative nomogram that predicts the survival rate of individuals with severe burn injuries by combining clinical attributes and laboratory examinations, demonstrating superior efficacy compared to conventional NEWS systems.

Modelling Environmental Tree Species’ Volume using some selected Skewed Distributions

Ecological requirements' knowledge in determining tree species' distributions is a precondition for sustainable forest management. Tree species in all regions are threatened by climate change but some are more vulnerable than others. Rightly skewed distributions were used to take care of the environmental data set obtained from FRIN using five species which are: Beech Wood, White Afara, Opepe, Afon and Teak. Appropriate statistical tools distributions like descriptive analysis, Akaike Information Criterion (AIC), Goodness of Fit, Probability Functions, Kolmogorov Smirnovt-test, Gamma, Weibull, Log-normal and Beta-weibull were carried out to determine the best distribution for each selected specie in this research. It was established from the distributional pattern of the tree species' volume used in this research that the Gamma distribution was a better fit for the Beech wood with the AIC of 617.21, Beta-weibull distribution was a better _t for the White Afara, Opepe and Teak Species with the AIC values of 580.772, 630.84 and 733.60 respectively while the Weibull distribution was a better fit for the Afon specie with the AIC value of 752.07. Conclusively, the implication of the analysis is that the Beta-weibull distribution described the tree species' volume best among the four distributions used. In line with the findings, it is recommended that Beta-weibull should be the appropriate distribution to model forest specie when it comes to modelling because of its four (4) parameters and its goodness when fitting.

A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts.

Using the Montgomery-Koyama-Smith equation to calculate the stomatal area per unit lamina area for 12 Magnoliaceae species.

The Montgomery-Koyama-Smith (MKS) equation predicts that total leaf area per shoot is proportional to the product of the sum of individual leaf widths and maximum individual leaf length, which has been validated for some herbaceous and woody plants. The equation is also predicted to be valid in describing the relationship between the total stomatal area per micrograph (AT) and the product of the sum of individual stomatal widths (denoted as LKS) and maximum individual stomatal length (denoted by WKS) in any particular micrograph. To test the validity of the MKS equation, 69,931 stomata (from 720 stomatal micrographs from 12 Magnoliaceae species) were examined. The area of each stoma was calculated using empirical measurements of stomatal length and width multiplied by a constant. Six equations describing the relationships among AT, LKS, and WKS were compared. The root-mean-square (RMSE) and the Akaike information criterion (AIC) were used to measure the goodness of fit, and the trade-off between the goodness of fit and the structural complexity of each model, respectively. Analyses supported the validity of the MKS equation and the power-law equation AT ∝ (LKS∙WKS)α, where a is a scaling exponent. The estimated values of α at the species level and for the pooled data were all statistically smaller than unity, which did not support the hypothesis that AT ∝ LTS∙WTS. The power-law equation had smaller RMSE and AIC values than the MKS equation for the data from the 12 individual species and the pooled data. These results indicate that AT tends to allometrically scale with LKS∙WKS, and that increases in AT do not keep pace with increases in LTS∙WTS. In addition, using the product of LKS and WKS is better than using only one of the two variables.

308 Isoenergetic reduction of dietary macronutrients reveals distinct fecal metabolomic signatures in lean and obese cats fed at maintenance

Abstract Obesity has become a concerning problem in cats. Increasing evidence demonstrates the utility of metabolomics in identifying disease biomarkers and assessing the effects of nutritional interventions, although limited information exists regarding alterations in fecal metabolites in lean and obese domestic cats in response to different macronutrient content. In the current study, three extruded diets were formulated based on adult maintenance using an isoenergentic approach. These diets were created by adjusting the levels of identical ingredients to achieve a low-protein [LP; protein 28% metabolizable energy (ME), fat 40%ME, NFE 32% ME), a low-fat (LF; protein 42% ME, fat 30% ME, NFE 30% ME), and a low-carbohydrate (LC; protein 36% ME, fat 41% ME, NFE 23% ME] diet. Healthy neutered male adult cats [n = 18; body condition scores (BCS) 4 or 5 (lean, n = 9) and 8 or 9 (obese, n = 9)] were fed each diet for a period of 4 wk in a 3 x 3 Latin square design. Fecal samples from each cat were collected between d 22 and 28 of each treatment period. A total of 67 fecal metabolites in six groups (35 amino acids, 11 fatty acids, 10 sugars and sugar metabolites, 5 alcohols, 3 nitrogenous bases, and 3 others) were analyzed using 1H NMR spectroscopy. To evaluate the impact of body condition, diet and their interaction on each metabolite, proc GLIMMIX covariance structure was selected by the smallest Akaike information criterion value in SAS. The Shapiro-Wilk test was used to check normality and lognormal or beta distribution were applied when appropriate. Bonferroni correction post-hoc test was followed to correct Type I error. Statistical significance was considered at P &amp;lt; 0.05. In lean cats, significantly greater concentrations of fecal proteinogenic and branched-chain amino acids, such as methionine, valine, and L-lysine, as well as pyruvic acid and sugars (P &amp;lt; 0.05) were observed. This suggests that these metabolites may either be more actively fermented by bacteria or not utilized as extensively as in obese cats, potentially contributing to a reduction in obesity status. Regardless of body condition, there is a possibility of an enhanced activity in the tryptophan degradation pathway, leading to the greatest concentrations of fecal tryptophan (P = 0.04) in the LF diet, while the greatest fecal D-galactose (P = 0.04) in the LP diet was likely due to the greater dietary carbohydrate content. These observed results suggested that fecal metabolites were influenced by both body condition and diet; however, body condition had a more substantial impact on metabolite changes in cats, whereas no body condition and diet interaction were noted. Overall, the study indicated distinct fecal metabolic signatures in lean and obese cats subjected to different macronutrient diets and further research is warranted to validate the observed findings. Funding was provided by Champion Petfoods, Natural Sciences and Engineering Council of Canada and Mitacs Accelerate.