Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model\'s prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

UNASSIGNED: Systemic immune-inflammation index (SII) is a novel comprehensive inflammatory marker. Inflammation is associated with impaired lung function. We aimed to explore the possible relationship between SII and lung function to examine the potential of SII in predicting lung function decline.
UNASSIGNED: A cross-sectional survey was conducted using the data of the NHANES from 2007 to 2012. Multiple linear regression models were used to analyze the linear relationship between SII and pulmonary functions. Sensitivity analyses, subgroup analyses, and interaction tests were used to examine the robustness of this relationship across populations. Fitted smooth curves and threshold effect analysis were used to describe the nonlinear relationships.
UNASSIGNED: A total of 10,125 patients were included in this study. After adjusting for all covariates, multiple linear regression model analysis showed that high Log2-SII level was significantly associated with decreased FVC(β, -23.4061; 95% CI, -42.2805- -4.5317), FEV1(β, -46.7730; 95% CI, -63.3371- -30.2089), FEV1%(β, -0.7923; 95% CI, -1.1635- -0.4211), FEV1/FVC(β, -0.6366; 95% CI, -0.8328- -0.4404) and PEF(β, -121.4468; 95% CI,-164.1939- -78.6998). The negative correlation between Log2-SII and pulmonary function indexes remained stable in trend test and stratified analysis. Inverted U-shaped relationships between Log2-SII and FVC, FEV1, FEV1%, and PEF were observed, while a negative linear correlation existed between FEV1/FVC and Log2-SII. The cutoff values of the nonlinear relationship between Log2-SII and FVC, FEV1, FEV1%, PEF were 8.3736, 8.0688, 8.3745, and 8.5255, respectively. When SII exceeded the critical value, the lung function decreased significantly.
UNASSIGNED: This study found a close correlation between SII and pulmonary function indicators. This study investigated the SII threshold when lung functions began to decline in the overall population. SII may become a promising serological indicator for predicting lung function decline. However, prospective studies were needed further to establish the causal relationship between these two factors.

In the mirabegron (MIR) synthesis, the N-nitroso mirabegron (NNM) is obtained during synthetic process of MIR; water is being used in reaction under acidic condition. Nitrite source is from water, and secondary amine source is from MIR as it has secondary amine; NNM is generated as an impurity during the synthesis of MIR. The presence of NNM in MIR could potentially affect its effectiveness. The purpose of this study was to establish a Ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) methodology to identify NNM in MIR samples. The method for NNM analysis was developed on Acquity HSS T3 (100*2.1) mm 1.8 μm column with gradient elution using mobile phase consisted of 0.1% formic acid in water (mobile phase A) and 0.1% formic acid in methanol (mobile phase B). Mass spectrometer with electrospray ionization operated in the MRM mode was used in the analysis of NNM (m/ z 426.20 → 170.00). The UPLC-MS/MS methodology proposed showed a good linearity (0.02 to 0.72 ppm), good system precision (RSD = 0.57%), good method precision (RSD = 0.87%), acceptable accuracy (94.5-116.5%), low detection limit (0.006 ppm) and low quantification limit (0.02 ppm) for NNM. The UPLC-MS/MS methodology proposed can be utilized to assess the quality of MIR sample for the presence of NNM impurity.

OBJECTIVE: To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models.
METHODS: A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared.
RESULTS: Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck.
CONCLUSIONS: When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.

OBJECTIVE: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods.
METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed.
RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models.
CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the Çiftlik District of Niğde province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-[Formula: see text]), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. [Formula: see text]The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-[Formula: see text], RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36 mg/L and 44.72 mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.