Information Criterion Research Articles

Predictors of Discordance Between Dry Eye Symptoms and Signs: Insights From the Sjögren's International Collaborative Clinical Alliance.

The diagnosis and management of dry eye disease (DED) could be complicated by the discordance between DED-related symptoms and signs. We performed a cross-sectional study to investigate the factors of and develop predictive models for the discrepancy in DED symptomatology. We used data from 3455 participants, 21 to 89 years old, from the Sjögren's International Collaborative Clinical Alliance study. We performed a multivariable stepwise linear regression model with backward elimination and Bayesian information criteria to select predictors for the discordance in DES symptomatology, which was defined as the difference between the rank score of Ocular Surface Disease Index 6 (OSDI-6) and the rank score of ocular staining score (OSS). Ten predictors, such as "vitality," "immunomodulating drugs," sensory symptoms," and "ethnicity," remained in the final models, achieving an adjusted R2 (aR2) of 0.35 (95% confidence interval [CI], 0.32-0.39). Specifically, medication use explained 19% (95% CI, 0.17-0.22) of the variance in the outcome, followed by medical history (aR2 = 0.18; 95% CI, 0.15-0.21). Health-related quality of life contributed 16% to the variance in the outcome (95% CI, 0.13-0.19), and, last, demographics contributed 11% (95% CI, 0.09-0.13). Our results suggest that individuals of Asian descent and those using immunomodulating medications often present with severe ocular signs that necessitate regular ophthalmological evaluations, even in the absence of proportionate ocular symptoms. Additionally, ocular symptoms, when accompanied by abnormal sensations in other parts of the body, could indicate systemic conditions that require further investigation and medical care.

Vegetation productivity determines the response of butterflies along elevation gradients in the trans‐Himalayas, Nepal

AbstractEnvironmental parameters along elevational gradients affect the number of butterflies and the variety of species. However, which variables play significant roles and how they operate can be difficult to untangle. Here, we examine the relationships between observed butterfly richness (overall and subgroups) at different elevation gradients and remotely sensed environmental variables (vegetation productivity, surface temperature, landscape heterogeneity, and moisture stress) using generalized linear models. We surveyed butterflies with a fixed‐point count method in 19 elevation bands within 1600–5200 m above sea level in Manang district, trans‐Himalayan region, north‐central Nepal. The number of butterflies in each elevation band was studied and estimated, then interpolated across the lowest and highest elevation to estimate butterfly species richness. Then, the selection of models was performed on butterfly richness and elevations to test the best model support based on the lowest value of the Akaike information criterion and a multimodel averaging for other environmental variables. Altogether, 94 butterfly species, representing 20 subfamilies and six families, were recorded throughout the study periods. We obtained cubic model support for overall species richness, Papilionidae, and Hesperiidae, quadratic to Nymphalidae and Pieridae, and the linear model to Lycaenidae. In our study, vegetation productivity was found to have a significant positive impact on butterfly communities. Our study further suggests species richness of Papilionidae and Hesperiidae has a strong positive correlation with surface temperature and landscape heterogeneity and negative associations with moisture stress but other subgroups of butterfly communities including overall species richness showed insignificant relationships with these variables. This study provides significant information related to the responses of montane butterflies to environmental variables along elevational gradients from the Himalayas Nepal. However, further detailed studies on the functional behaviors of butterflies potentially offer more insights into their distribution patterns and ecological relationship in the montane environment.

Some Properties of the Cosine Lomax Distribution with Applications

This paper introduces a new trigonometric distribution, named the Cosine Lomax (CLM) distribution. This distribution is a combination of the Lomax distribution and the Cosine-G family of distributions. Among the many mathematical moments, moment-generating function, incomplete moments, and the quantile function. We try to determine the model parameters using the maximum likelihood estimation method. Simulation studies evaluate the effectiveness of maximum likelihood estimators using bias and root mean square error. We applied the CLM distribution to two real-world datasets and tested for consistency using the Akaike information criterion, the consistent AIC, the Hannan-Quinn information criterion, the Bayesian information criterion, the Kolmogorov-Smirnov p-value, Cramer Von-Mises, and Andersen-Darling. The CLM model fared better when compared to the following distributions: Lomax, Inverse Lomax, Inverse Weibull, Burr Type X, Rayleigh, and Exponential, as well as measures of data set fit.

Identification of hotspots and cold-spots of groundwater potential using spatial statistics

Study regionThe Guna-Tana landscape is located in Ethiopia. This landscape is seriously facing water scarcity problems, that’s why we studied this landscape and provided the hotspots of groundwater potential areas in this region. Study focusIn this study the hotspots and cold-spots of groundwater potential at, 99, 95, and 90 % confidence levels has been deciphered. Using Gi-Bin values, four classes has been identified viz., 2–3 (highly favorable), 0–1 (fairly favorable), −2 to −1 (poorly favorable) and −3 (very poorly favorable). The hotspots was subjected to ordinary least squared (OLS) regression to understand the impact of chosen parameters (viz., geology, land-use, soil, rainfall, slope, and distance to rivers) towards groundwater potential. New hydrological insights for the regionThe absence of redundancy among the selected parameters was indicated by the VIF values of the parameters, which were determined to be less than 7.5. It was discovered that the Robust Probability (Robust_Pr) was statistically significant (p < 0.01). The OLS model appears to have captured the variability of exploratory variables, as evidenced by the decreased values of Akaike's Information Criterion (AICc). The Adjusted R-squared value of 0.9119 indicates that exploratory variables has successfully explained 91.19 % of the variance of the model.

Bayesian approach to estimate variance components and genetic parameters of average daily gains and Kleiber ratios in crossbred cattle.

The present study aimed to estimate variance components and genetic parameters of the average daily gains from birth to 3-months (ADG1), 3- to 6-months (ADG2), 6- to 9-months (ADG3) and 9- to 12-months (ADG4) of age and corresponding Kleiber ratios (KR1, KR2, KR3 and KR4, respectively) in Jersey crossbred calves. Data for this study were collected from National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, India during 2013 to 2021. Genetic parameters for the traits were estimated using Bayesian procedure through Gibbs sampling by fitting six animal models. The deviance information criterion (DIC) was employed to determine the most appropriate model for each trait under investigation. Direct heritability estimates for ADGs and KRs ranged from 0.19 (ADG3) to 0.59 (ADG2) and 0.14 (KR3) to 0.72 (KR1), respectively. Significant maternal heritability ( ) was observed for average daily gains (17-24%) and Kleiber ratios (13-18%) at different age intervals under the best model. Estimates of permanent environmental effects ( ) of dam for ADG1, ADG2, KR2 and KR4 were only 1-4% of the total phenotypic variance in this study. Large negative estimates of correlations, ranging from -0.87 to -0.98, between direct and maternal ) effects for ADGs and KRs (except KR3) at different age intervals were observed. Total heritability ( ) and maternal repeatability ) of the studied traits ranged 0.05-0.28 and 0.01-0.14, respectively. The moderate to high heritability estimates for all traits in this study indicate the possibility of genetic progress for these traits through selective breeding program.

Prognostic model for second progression-free survival and overall survival in patients with high-risk metastatic hormone-sensitive prostate cancer treated with abiraterone acetate and androgen deprivation therapy.

To develop and validate a prognostic risk model for high-risk metastatic hormone-sensitive prostate cancer (mHSPC) patients treated with upfront abiraterone acetate (ABI). This retrospective multicenter study involved 233 high-risk mHSPC patients who received upfront ABI, developed by three academic centers. The model was externally validated with an independent cohort of 282 patients. To identify independent prognostic factors for second progression-free survival (PFS2) and develop the best-fitted model, Cox proportional hazards regression, followed by the Akaike information criterion, was used. Patients were categorized into three groups based on their risk scores. PFS2 and overall survival (OS) were evaluated according to the risk groups in the discovery and validation cohorts. The median age was 72 (range 51-89) years, with a median follow-up duration of 27 months. Independent factors linked to PFS2 included an Eastern Cooperative Oncology Group performance status ≥2, a primary Gleason score of 5, an extent of disease score of ≥3 or liver metastasis, and lactate dehydrogenase >220 U/L. Median PFS2 for favorable-, intermediate-, and poor-risk groups were not reached, 43 months, and 16 months, respectively. The median OS was 29 months in the poor-risk group, whereas it was not reached in the favorable- and intermediate-risk groups. The 2-year OS rates in the favorable-, intermediate- and poor-risk groups were 94.5%, 80.1%, and 60.3%, respectively. The validation cohort confirmed the risk model's relationship with PFS2 and OS. The median PFS2 and OS in the high-risk group were 21 months and 32 months, respectively. Our prognostic model, including five clinical factors, is useful for patient care and treatment selection in high-risk mHSPC patients treated with ADT plus ABI. The developed model could provide more accurate information, guide treatment decisions, or classify patients in future clinical trials.

EFFECTS OF THE CLIMATE GRADIENT ONF THE WOOD DENSITY OF Cedrela odorata L. IN THE NEOTROPICAL REGION

Understanding wood properties is crucial for optimizing its technological potential, enhancing both the effectiveness of use and product quality. Among these properties, density is regarded as one of the most significant. Numerous studies have documented the relationship between wood density and climate. However, the influence of climatic factors on the density variation of native species remains largely unexplored. Cedrela odorata L., a species traditionally logged in Brazil and widely distributed across the Neotropical region, requires further analysis to understand its potential response to varying climate patterns, thereby informing its management. This study aimed to determine the relationship between the basic density of Cedrela odorata L. and the climatic conditions in the Neotropical region. A comprehensive database was compiled from published scientific literature. Statistical analyses were conducted using Generalized Linear Models (GLM), with the best models selected based on the corrected Akaike Information Criterion (AICc). The presence of spatial patterns was assessed using Moran’s Index to verify spatial autocorrelation. The study identified significant relationships between the wood density of C. odorata and variables such as mean annual temperature and annual precipitation. The findings indicate a strong interaction between the climatic variables analyzed and the basic density of Cedrela odorata L. wood, with density being more sensitive to mean annual temperature.

Rethinking the prognosis model of differentiated thyroid carcinoma.

The prediction efficiency of long-term cancer-specific survival (CSS) in guiding the treatment of differentiated thyroid carcinoma (DTC) patients is still unsatisfactory. We need to refine the system so that it more accurately correlates with survival. This is a retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database, and included patients who underwent surgical treatment and were diagnosed with DTC from 2004 to 2020. Patients were divided into a training cohort (2004-2015) and validation cohort (2016-2020). Decision tree methodology was used to build the model in the training cohort. The newly identified groups were verified in the validation cohort. DTC patient totals of 52,917 and 48,896 were included in the training and validation cohorts, respectively. Decision tree classification of DTC patients consisted of five categorical variables, which in order of importance were as follows: M categories, age, extrathyroidal extension, tumor size, and N categories. Then, we identified five TNM groups with similar within-group CSS. More patients were classified as stage I, and the number of stage IV patients decreased significantly. The new system had a higher proportion of variance explained (PVE) (5.04%) and lower Akaike information criterion (AIC) (18,331.906) than the 8th TNM staging system (a PVE of 4.11% and AIC of 18,692.282). In the validation cohort, the new system also showed better discrimination for survival. The new system for DTC appeared to be more accurate in distinguishing stages according to the risk of mortality and provided more accurate risk stratifications and potential treatment selections.

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.

Mapping Risk–Return Linkages and Volatility Spillover in BRICS Stock Markets through the Lens of Linear and Non-Linear GARCH Models

This paper explores the influence of the risk–return relationship and volatility spillover on stock market returns of emerging economies, with a particular focus on the BRICS countries. This research is undertaken in a context where discussions on de-dollarization and the expansion of BRICS membership are gaining momentum, making it a novel and distinct exercise compared to prior studies. Utilizing econometric techniques to investigate daily market returns from 1 April 2008 to 31 March 2023, a period that witnessed major events like the global financial crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict, linear and non-linear models like ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, are employed to assess stock return volatility behaviour, assuming a Gaussian distribution of error terms. The diagnostic test confirms that the distribution is non-normal, stationary, and heteroscedastic. The key findings indicate a lack of the risk–return relationship across all BRICS stock markets, except for South Africa; a more pronounced effect of unpleasant news over pleasant news; a slow mean-reverting process in volatility; the EGARCH model is the best fit model as evidenced by a higher log likelihood and lower Akaike information criterion and Schwardz information criterion parameters; and finally, the presence of significant bidirectional and unidirectional spillover effects in the majority of instances. These findings are valuable for investors, regulators, and policymakers in enhancing returns and mitigating risk through portfolio diversification and informed decision making.

Sexual violence against ever-married reproductive-age women in East Africa: further analysis of recent demographic and health surveys

BackgroundSexual violence is a violation of women’s rights, resulting in significant physical and psychological challenges and adverse reproductive health outcomes. Addressing these issues demands urgent public health interventions and support systems to mitigate the profound impact on individuals and societies. Thus, this study aimed to assess sexual violence against ever-married reproductive-age women in East Africa.MethodsData retrieved from the recent Demographic and Health Survey (DHS) of East African countries was used, and a weighted sample of 40,740 ever-married reproductive-age women was included. To identify factors associated with sexual violence, multilevel mixed-effects models utilizing robust Poisson regression were applied. Akaike’s and Bayesian information criteria, as well as deviance, were utilized to compare the models. In the multivariable regression model, adjusted prevalence ratios (APR) with 95% confidence intervals (CI) were used to estimate the strength of association, with statistical significance set at a p-value < 0.05.ResultThe pooled proportion of sexual violence among ever-married reproductive-age women in East Africa was 13.05% (95% CI: 12.74–13.36). The multivariable multilevel robust Poisson regression revealed that age at first cohabitation/marriage, having a primary educational level, being employed, residing in a female-headed household, having a husband/partner who drinks alcohol, and living in rural areas were positively associated with sexual violence. On the contrary, having secondary and higher educational levels and living in communities with a high proportion of uneducated women were negatively associated with sexual violence.ConclusionEmpowering girls and women through education reduces their vulnerability. Effective programs should prioritize workplace safety, financial independence, and robust legal protections against harassment and abuse. Raising awareness about the impact of alcohol abuse on relationships and the heightened risk of sexual violence is crucial. Moreover, enhancing access to support services and community networks, especially in rural areas, is essential for preventing and responding to sexual violence.

Comparison of four approaches in eliciting health state utilities with SF-6Dv2.

To empirically compare four preference elicitation approaches, the discrete choice experiment with time (DCETTO), the Best-Worst Scaling with time (BWSTTO), DCETTO with BWSTTO (DCEBWS), and the Standard Gamble (SG) method, in valuing health states using the SF-6Dv2. A representative sample of the general population in Quebec, Canada, completed 6 SG tasks or 13 DCEBWS (i.e., 10 DCETTO followed by 3 BWSTTO). Choice tasks were designed with the SF-6Dv2. Several models were used to estimate SG data, and the conditional logit model was used for the DCE or BWS data. The performance of SG models was assessed using prediction accuracy (mean absolute error [MAE]), goodness of fit using Bayesian information criterion (BIC),t-test, Jarque-Bera (JB) test, Ljung-Box (LB) test, the logical consistency of the parameters, and significance levels. Comparison between approaches was conducted using acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The variance scale factor was computed to assess individuals' consistency in their choices for DCE and BWS approaches. Out of 828 people who completed SG and 1208 for DCEBWS tasks, a total of 724 participants for SG and 1153 for DCE tasks were included for analysis. Although no significant difference was observed in self-reported difficulties and qualities in answers among approaches, the SG had the longest completion time and excluded participants in SG were more prone to report difficulties in answering. The range of standard errors of the SG was the narrowest (0.012 to 0.015), followed by BWSTTO (0.023 to 0.035), DCEBWS (0.028 to 0.050), and DCETTO (0.028 to 0.052). The highest number of insignificant and illogical parameters was for BWSTTO. Pain dimension was the most important across dimensions in all approaches. The correlation between SG and DCEBWS utility values was the strongest (0.928), followed by the SG and BWSTTO values (0.889), and the SG and DCETTO (0.849). The range of utility values generated by SG tended to be shorter (-0.143 to 1) than those generated by the other three methods, whereas BWSTTO (-0.505 to 1) range values were shorter than DCETTO (-1.063 to 1) and DCEBWS (-0.637 to 1). The variance scale factor suggests that respondents had almost similar level of certainty or confidence in both DCE and BWS responses. The SG had the narrowest value set, the lowest completion rates, the longest completion time, the best prediction accuracy, and produced an unexpected sign for one level. The BWSTTO had a narrower value set, lower completion time, higher parameter inconsistency, and higher insignificant levels compared to DCETTO and DCEBWS. The results of DCEBWS were more similar to SG in number of insignificant and illogical parameters, and correlation.

Unraveling the Relationship between Soil Nutrients and Maize Leaf Disease Occurrences in Mopani District Municipality, Limpopo Province, South Africa

Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorus-deficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p &lt; 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops.

Modeling the importance of physician training in practice location for Ohio otolaryngologists.

Underserved communities in southeastern Ohio and Appalachia face significant healthcare accessibility challenges, with the Midwest offering a lower density of healthcare providers compared to coastal regions. Specifically, underserved communities in southeastern Ohio and Appalachia are disadvantaged in otolaryngology care. This analysis aims to identify factors that lead otolaryngologists to a respective practice location, and if any of that influence comes from where otolaryngologists completed their medical education. The proportion of otolaryngologists who performed medical school, residency, and/or fellowship in Ohio was analyzed utilizing a three-sample test for equality. Multivariate logistic regression and Pearson prediction models were produced to analyze the impact of performing medical training (medical school, residency, and fellowship) in Ohio. Going to medical school in Ohio significantly increases the odds of going to an otolaryngology residency in the state (p<0.001). Moreover, between medical school and residency, medical school was a significantly better predictor of otolaryngologists practicing in Appalachia (Δ Bayesian Information Criterion [BIC]>2) and southeast Ohio (ΔBIC>10). Medical school in state was also a better predictor of percent rural and median household income than residency (ΔBIC>10). The multivariate model of medical school and residency was significantly better than either predictor alone for the population (ΔBIC>2). All models predicting percent rural were significantly improved with the addition of a Doctor of Osteopathy (DO) degree (ΔBIC>10). Where physicians complete their medical training (medical school, residency, and fellowship) in state has a significant impact on predicting their future place of practice. This study found that the location of such training has a positive predictive nature as to whether that physician will practice in a rural and underserved area in the future. Notably, the addition of being licensed as a DO also increased the probability of that physician practicing in a rural area.

Comprehensive analysis of soil electrical conductivity: an experimental and machine learning approach

Experimental test is conducted to find a relation between soil electrical conductivity with some soil properties. Five input parameters namely water content (w), dry density of soil (γd), degree of saturation (Sr), porosity (η) and voltage (V) are considered to compute electrical conductivity of soil (SEC). The effect of w, γd, Sr and η on SEC are analyzed. These datasets are used in soft computing technique to predict SEC. Five hybrid models namely ANN-GA, ANN-PSO, ANN-FA, ANN-GWO and ANN-MFO are used to predict SEC. To check the performance of these hybrid model, numerous statistical performance parameters (R2, a-10 index, VAF, LMI, RMSE, MAE, MAD and U95) are used. On the basis of these performance parameters, ANN-GA performs better (Due to higher value of R2, VAF, a-10 index and LMI and lower value of RMSE, MAE, MAD and U95) as compare to the other proposed model. The model's performance is also examined using rank analysis, regression curve, Williams plot, error matrix, objective function (OBJ) criterion, and Akaike information criterion (AIC). By applying all the above criteria, it has been observed that ANN-GA model outperforms than the other model to predict SEC. This was recognized to its maximum R2 = 0.9998 and the lowest RMSE = 0.0041 during the training phase, as well as R2 = 0.9975 and RMSE = 0.0112 during the testing phase. The model's reliability index (β) is calculated using the first-order second moment (FOSM) approach, and the result is compared to its actual condition. Additionally, a sensitivity analysis is carried out to ascertain the impact of each input parameter on the output (SEC).Based on the results, the hybrid model of ANN i.e., ANN-GA is potential to assist engineers to predict SEC in the design phase of high voltage buried power cables, ground modification techniques or in the field of agriculture.

Optimized Spectral Clustering Methods For Potentially Divergent Biological Sequences

Various recent researches in bioinformatics demonstrated that clustering is a very efficient technique for sequence analysis. Spectral clustering is particularly efficient for highly divergent sequences and GMMs (Gaussian Mixture Models) are often able to cluster overlapping groups if given an adequately designed embedding. The current study used spectral embedding and Mixture Models for clustering potentially divergent biological sequences. The research approach resulted in a pipeline consisting of the following four steps. The first step consists of aligning the biological sequences. The pairwise affinity of the sequences is computed in the second step. Then the Laplacian Eigenmap embedding of the data is performed in the third step. Finally, the last step consists of a GMM-based clustering. Improving the quality of the generated clustering and the performance of this approach is directly related to the enhancement of each one of these four steps. The main contribution is proposing four GMM-based algorithms for automatically selecting the optimal number of clusters and optimizing the clustering quality. A clustering quality assessment method, based on phylogenetic trees, is also proposed. Moreover, a performance study and analysis have been conducted while testing different clustering methods and GMM implementations. Experimental results demonstrated the superiority of using the BIC (Bayesian Information Criterion) for selecting the optimal GMM configuration. Significant processing speed improvements were also recorded for the implementation of the proposed algorithms.

Model ensemble techniques of machine learning algorithms for soil moisture constants in the semi‐arid climate conditions

AbstractIn recent years, the use of prediction models based on intelligent algorithms has become widespread in soil science. However, each algorithm has advantages and disadvantages, and variable results can occur on different datasets. The evaluation of ensemble techniques for solving these problems is the current approach. Water problems will arise due to global warming, and soil water will become more important. This study aims to evaluate the predictive accuracy of different machine learning algorithms (support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), and multivariate linear regression (MLR)) and ensemble techniques (equal weight [EQ], Bates–Granger‐BG), Granger–Ramanathan (GR), Akaike information criterion (AIC), and Bayesian information criterion (BIC)) on the field capacity (FC), wilting point (WP) and available water content (AWC) of soils. As a result, higher prediction accuracy was obtained with the RF algorithm than with the value machine learning algorithm in the estimation of moisture constants. The coefficients of determination (R2) obtained for the prediction of FC, WP, and AWC via the RF algorithms were 0.624, 0.759 and 0.641, respectively. MLR had the highest error rate. Among the ensemble techniques, GR was the most successful. Lin's concordance correlation coefficient (LCCC) values obtained from the estimation of FC, WP, and AWC with the GR model were 0.801, 0.894, and 0.801, respectively. The root mean squared error (RMSE) and mean absolute error (MAE) values obtained in the estimation of the available water content with the MLR algorithm were 1.905 and 1.435, respectively, whereas these values were 1.173 and 0.767, respectively, when the GR model was used. As a result of the present study, better predictive results were obtained with ensemble techniques instead of evaluating the algorithms individually.

Predictive ARIMA Model with a Machine Learning (ML) Approach for COVID-19 Data in Pakistan

This study is based on the application of an ARIMA (p, d, q) based machine learning (ML) approach to evaluate the dynamics of COVID-19 pandemic. The focus is on estimating epidemic trends and performing diagnostic scrutiny with model fitting. The data including all four waves of the pandemic pertaining to Pakistan, covering all four provinces (Sindh, Punjab, Khyber Pakhtunkhwa, Balochistan, as well as Gilgit Baltistan, Azad Jammu Kashmir, and the capital city Islamabad, collected from February 26, 2020, to September 30, 2021, is analyzed. The ML algorithm is used to optimize the results of ADF, unit root test which ensures the minimum of ACF, and PACF graphs intention of the data series. The results employ the fitted ARIMA models (1, 1, 1) and (1, 1, 7) for the 1st to 4th waves, confirming daily infected cases across the entire dataset of Pakistan. The cumulative trained observations are from the 1st wave (February 26, 2020, to October 20, 2020), 2nd wave (October 21, 2020, to March 16, 2021), 3rd wave (March 17, 2021, to July 10, 2021), and 4th wave (July 11, 2021, to September 30, 2021), with a further 14-day forecast (from October 1 to October 14, 2021). The results show a strong correlation between the trained and predicted values, ranging from 0.8789 to 0.99236. To select predictive model parameters, the model that results in the minimum Bayesian Information Criterion (BIC) value and residuals from the datasets obtained after detaching the unnecessary errors and the 95^% CI for the forecasting error ( ) are calculated. These values would help to decide the best fitted predictive model.

Development and validation of a prognostic nomogram for predicting liver metastasis in thyroid cancer: a study based on the surveillance, epidemiology, and end results database

This study aimed to create a prognostic nomogram to predict the risk of liver metastasis (LM) in thyroid cancer (TC) patients and assess survival outcomes for those with LM. Data were collected from the SEER database, covering TC patients from 2010 to 2020, totaling 110,039 individuals, including 142 with LM. Logistic regression and stepwise regression based on the Akaike information criterion (AIC) identified significant factors influencing LM occurrence: age, histological type, tumor size, bone metastasis, lung metastasis, and T stage (p < 0.05). A nomogram was constructed using these factors, achieving a Cindex of 0.977, with ROC curve analysis showing an area under the curve (AUC) of 0.977. For patients with TCLM, follicular TC, medullary TC, papillary TC, and examined regional nodes were associated with better prognosis (p < 0.001, HR < 1), while concurrent brain metastasis indicated poorer outcomes (HR = 2.747, p = 0.037). In conclusion, this nomogram effectively predicts LM risk and evaluates prognosis for TCLM patients, aiding clinicians in personalized treatment decisions

Evaluating genotype by environment interaction for growth traits in Limousine cattle

Environmental conditions affect the growth and health of animals, making it crucial to quantify heat stress and the genetic basis of heat tolerance in animal breeding. The main objective of this study was to evaluate heat stress on beef cattle growth and investigate the genetic background of tolerance to harsh environmental conditions in the Italian Limousine beef cattle. Three growth traits were analysed: average daily gain (ADG), weaning weight (WW), and yearling weight (YW). Data were collected from animals raised between 1991 and 2022 and combined with 14 environmental covariates. Records for ADG, WW, and YW encompassed 108 205, 100 058, and 24 939 individuals, respectively, with 4 617, 4 670, and 2 048 genotyped individuals. Climatic variables were compared for inclusion in a linear mixed model using the Deviance Information Criterion. Multiple-trait models and genomic information incorporated environmental conditions with the largest impact on the studied traits Genotype by environment interaction (G × E) was detected in all the studied traits, showing substantial heterogeneity of the variance components across the different environments (Env). Heritability for WW remains constant among Env; instead, for ADG and YW decreased under uncomfortable environmental conditions. YW showed the lowest genetic correlation (0.28) between divergent conditions (Env 2 and Env 5,) for ADG and WW correlations dropped below 0.50 among Env. The values of genetic correlations indicate that growth traits are moderately to strongly affected by G × E. Eigenvalue decomposition of the additive genetic (co)variance matrix for ADG, WW, and YW indicated that three components accounted for over 0.80 of the proportion of the variance explained, suggesting different animal performances across Env. Spearman rank correlations showed potential re-ranking of genotyped sires, because ADG, WW, and YW showed correlations between Env below 0.80. The accuracy of single-step genomic EBV was higher compared to EBV for al traits. Overall, the result confirms the existence of G × E for growth traits in the Italian Limousine population. Including G × E in the model allows for more environment-aware predictions, helping breeders understand how different genetic bases respond to varying conditions. Genomic predictions incorporating G × E could accelerate genetic gains and improve response to selection for heat tolerance.