Statistical Model Research Articles

A century of statistical Ecology.

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions

BackgroundDetecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge.ResultsHere, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs.ConclusionsHere, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP–SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer.Availability and implementation: https://github.com/scutdy/SSO/blob/master/SEEI.zip.

The impact of land use and cover changes on river flows in Wundanyi Catchment of Taita Hills, Kenya (1970–2030)

<p>Taita hills are one of the most important biodiversity hotspots of Kenya but experiencing a high rate of deforestation due conversion of its original forestland to agriculture and settlement during the last century. These landscape dynamics coupled with rainfall fluctuations in these critical ecosystems may significantly affect water resource distribution and food security in Taita Taveta County and its environs. This study aimed to establish the trends of land use/cover change (LUCC) in Wundanyi catchment from 1970 to 2030 and predict their specific and combined effects on surface runoff and stream flow in the same period. The analysis was based on statistical trend analysis and dynamic landscape modeling using both historical and primary data from Wundanyi catchment, and Landsat TM and ETM+ imagery of Taita Hills for 1990, 2000, and 2010. Results show highly variable mean seasonal and annual values of discharge in Wundanyi catchment probably attributed to environmental changes affecting Taita Hills in general and Wundanyi catchment in particular. Compared to 1990, major land use/cover changes in 2010 were featured by the expansion of built-up area (250%), plantation forest (23.7%), broadleaved forest (17.4%), and thicket (15.9%). Was also notable the decrease in woodland (−30.3%), cropland (−21.6%), and shrubland (−0.8%). Dynamic spatial trends by the year 2030 will be evidenced by increased thicket by 0.41% per annum (<em>R</em><sup>2</sup> = 81.6%), and by decreased plantation forests (−0.13%; <em>R</em><sup>2</sup> = 91.3%), woodland (−0.10%; <em>R</em><sup>2</sup> = 77.6%), shrubland (−0.11%; <em>R</em><sup>2</sup> = 85.2%), broadleaved forests (−0.03%; <em>R</em><sup>2</sup> = 56.6%) and cropland (−0.09%; <em>R</em><sup>2</sup> of 84.4). These changes will shape the catchment landscape and influence its hydrology, unless the existing forest and agricultural policy interventions are enforced. Hence, crop diversification, agroforestry, and soil and water conservation structures are recommended to maintain effective control of LUCC on hydrological processes going on in the Wundanyi catchment.<strong></strong></p>

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.

Comparison of Amount of Space Closure During En- Masse Retraction of Maxillary Anterior Teeth Using Three Accelerated Orthodontic Treatment Techniques- Micro Osteo-Perforation, Photo Bio-Modulation and Peizocision : A Retrospective Study

AIM: The study aimed at comparing the amount of space closure using peizocision (PZ) and micro- osteoperforation (MOP) and low-level laser therapy (LLLT). Materials and methods: A retrospective study consisted of 3 groups, sample number of minimum 8 patients per group were estimated using statistical model called an Analysis of Variance, or ANOVA model. Three sets of data were collected. Which included patients who were treated with upper first premolar extraction for en- masse retraction of maxillary anterior teeth using NiTi closed coil spring with regional acceleratory procedures. The data collected was divided into 3 groups, GROUP 1- micro osteo perforations (n=8), GROUP 2- photo bio modulation (n=8), GROUP 3- peizocision (n=8).sing digital Vernier calipers, a direct approach was used to measure all the stone casts obtained before canine retraction (T0) and after the retraction was completed after three months (T1). The statistical analysis was performed on the data acquired about the amount of space closure of en-masse retraction of maxillary anteriors in the PZ, LLLT, and MOP groups. Results: Compared to MOP (2.60mm) and LLLT (2.44mm), the PZ group had a greater mean amount of space closure (5.06mm). In conclusion, the quantity of space closure in piezocision was significantly higher than that in MOP and LLLT, although there was no statistically significant difference between the two. It is imperative that treatment duration be shortened. Finding the most effective and practical method to speed up tooth movement using the biomechanical mechanisms that are currently in place is therefore necessary

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.

Similarity Measures between Buying and Selling Prices of Various Exchange Rates with the inclusion of Multidimensionality and Multicollinearity

This paper introduces a new statistical model, the multidimensional measurement error model with multicollinearity, to study the relationship between buying and selling prices of foreign exchange rates of various currencies. Such a model is needed due to the possible rise in multidimensionality and multicollinearity issues that may occur due to the movement of the financial markets towards stock market integration of various countries since the occurrences of the financial crisis as well as the part result of globalization. As this integration involves the participation of various countries, it will affect the foreign exchange rate. Hence, the analyses are performed on seven currencies against the Malaysian Ringgit where four models’ performances are compared. From this research, it can be concluded that the proposed model comparatively performs better than the other models in representing the relationship of the stationary prices and performs as well as existing models towards non-stationary prices. It can also be seen that the Japanese Yen is the currency that has the strongest influence and closer similar trends towards other currencies while the Great British Pound showed otherwise.

Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting

Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture productionis highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail tomodel such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developedtime-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network andsupport vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai.As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have beenemployed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agraand Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions.Moreover, all the EMD-based models have performed better than the other competing models.

Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer

PurposeThe incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data.Methods2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software.ResultsMale, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed.ConclusionsGender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: A systematic review and meta-analysis.

To evaluate the performance of machine learning models in predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using computed tomography (CT) and magnetic resonance imaging (MRI). We searched PubMed, Embase, Cochrane Library, and Web of Science for studies published before January 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to assess the methodological quality of the included studies, random-effects models were used to calculate sensitivity and specificity, I2 values were used for heterogeneity measurements, and subgroup analyses were carried out to detect potential sources of heterogeneity. A total of 1690 patients from 24 studies were included. The meta-analysis calculated a pooled area under the curve (AUC) of 0.92 (95%CI-0.89-0.94), pooled sensitivity of 0.81 (95%CI-0.73-0.88), and pooled specificity of 0.88 (95%CI-0.82-0.92). We investigated 4 studies that mainly contributed to heterogeneity. After performing meta-analysis again excluding these 4 studies, the heterogeneity was significantly reduced. In subgroup analysis, the pooled AUC of the deep learning model was 0.95 and was 0.88 for the traditional statistical model; the pooled AUC of studies that used diffusion-weighted imaging (DWI) was 0.90, and was 0.92 in studies that did not use DWI; the pooled AUC of studies conducted in China was 0.94, and was 0.83 in studies conducted in other countries. Machine learning has promising potential in predicting tumor response to nCRT in patients with locally advanced rectal cancer. Together with clinical information, machine-learning based models may bring us closer toward precision medicine. Compared to traditional machine learning models, deep learning-based studies are able to obtain higher AUC, although they are less predominant and more heterogeneous. Together with clinical information, machine-learning based models may bring us closer toward precision medicine.

Co-localization of antibiotic resistance genes is widespread in the infant gut microbiome and associates with an immature gut microbial composition

BackgroundIn environmental bacteria, the selective advantage of antibiotic resistance genes (ARGs) can be increased through co-localization with genes such as other ARGs, biocide resistance genes, metal resistance genes, and virulence genes (VGs). The gut microbiome of infants has been shown to contain numerous ARGs, however, co-localization related to ARGs is unknown during early life despite frequent exposures to biocides and metals from an early age.ResultsWe conducted a comprehensive analysis of genetic co-localization of resistance genes in a cohort of 662 Danish children and examined the association between such co-localization and environmental factors as well as gut microbial maturation. Our study showed that co-localization of ARGs with other resistance and virulence genes is common in the early gut microbiome and is associated with gut bacteria that are indicative of low maturity. Statistical models showed that co-localization occurred mainly in the phylum Proteobacteria independent of high ARG content and contig length. We evaluated the stochasticity of co-localization occurrence using enrichment scores. The most common forms of co-localization involved tetracycline and fluoroquinolone resistance genes, and, on plasmids, co-localization predominantly occurred in the form of class 1 integrons. Antibiotic use caused a short-term increase in mobile ARGs, while non-mobile ARGs showed no significant change. Finally, we found that a high abundance of VGs was associated with low gut microbial maturity and that VGs showed even higher potential for mobility than ARGs.ConclusionsWe found that the phenomenon of co-localization between ARGs and other resistance and VGs was prevalent in the gut at the beginning of life. It reveals the diversity that sustains antibiotic resistance and therefore indirectly emphasizes the need to apply caution in the use of antimicrobial agents in clinical practice, animal husbandry, and daily life to mitigate the escalation of resistance.CR8ihftTez1HgYaufYhgHyVideo

Massively parallel dissection of RNA in RNA-protein interactions in vivo.

Many of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to biology. Here, we present massively parallel RNA assay combined with immunoprecipitation (MPRNA-IP) for in vivo high-throughput dissection of RNA-protein interactions and describe statistical models for identifying RNA domains and parsing the structural contributions of RNA. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, MPRNA-IP is able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest, including the long noncoding RNA NORAD, bacteriophage MS2 RNA, and human telomerase RNA, and we use it to interrogate the hitherto unknown sequence or structural RNA-binding preferences of the DNA-looping factor CTCF. By integrating systematic mutation analysis with crosslinking immunoprecipitation, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA-protein interactions in vivo.

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

Mangroves as Natural Shields: A Comprehensive Review of Their Role in Mitigating Natural Disasters and Conservation Strategies

Mangrove ecosystems play a crucial role in mitigating the impact of natural disasters on coastal regions worldwide. This comprehensive review paper synthesizes multidisciplinary studies to explore the contribution of mangroves in reducing the risk of catastrophes such as storm surges, tsunamis, hurricanes, and coastal erosion. By analysing the complex relationships between mangroves and natural disasters, we delve into factors such as species composition, forest structure, hydrological regimes, and coastal geomorphology. Additionally, we investigate the socioeconomic implications of mangrove conservation and restoration efforts, highlighting their potential to enhance resilience and sustainable development in coastal communities. The paper presents a detailed examination of wave characteristics, types, and attenuation mechanisms, focusing on how mangrove features such as prop roots, knee roots, and pneumatophores influence wave dissipation. We discuss various numerical and statistical models used to predict wave attenuation through mangroves, providing insights into their strengths and limitations. Furthermore, we explore global and India-specific mangrove cover status, important species, and conservation measures, including legal frameworks and initiatives by governmental and non-governmental organizations. Finally, the review underscores the urgency of conserving and restoring mangrove ecosystems to safeguard both human well-being and ecological integrity in the face of escalating climate-related threats. It advocates for evidence-based decision-making and policy development in disaster risk management and climate adaptation, emphasizing the pivotal role of mangroves as natural shields against natural calamities. Overall, this paper contributes to the understanding of mangrove-mediated hazard reduction and provides guidance for maximizing the resilience of coastal communities in an increasingly volatile world.

Multi sentence description of complex manipulation action videos

Automatic video description necessitates generating natural language statements that encapsulate the actions, events, and objects within a video. An essential human capability in describing videos is to vary the level of detail, a feature that existing automatic video description methods, which typically generate single, fixed-level detail sentences, often overlook. This work delves into video descriptions of manipulation actions, where varying levels of detail are crucial to conveying information about the hierarchical structure of actions, also pertinent to contemporary robot learning techniques. We initially propose two frameworks: a hybrid statistical model and an end-to-end approach. The hybrid method, requiring significantly less data, statistically models uncertainties within video clips. Conversely, the end-to-end method, more data-intensive, establishes a direct link between the visual encoder and the language decoder, bypassing any statistical processing. Furthermore, we introduce an Integrated Method, aiming to amalgamate the benefits of both the hybrid statistical and end-to-end approaches, enhancing the adaptability and depth of video descriptions across different data availability scenarios. All three frameworks utilize LSTM stacks to facilitate description granularity, allowing videos to be depicted through either succinct single sentences or elaborate multi-sentence narratives. Quantitative results demonstrate that these methods produce more realistic descriptions than other competing approaches.

Fabrication of Magnetic Chitosan-Benzil Biopolymer with Organoclay for Remazol Brilliant Blue R Dye Removal: a Statistical Modeling and Adsorption Mechanism

Fabrication of Magnetic Chitosan-Benzil Biopolymer with Organoclay for Remazol Brilliant Blue R Dye Removal: a Statistical Modeling and Adsorption Mechanism

VLC channel model in underground mining scenarios with the extinction effect and shadowing effect

In this work, a novel, to our knowledge, visible light communication (VLC) channel model is proposed for underground mining scenarios taking into account the impact of coal dust particles and obstacles. Specifically, the extinction effect of the coal dust particles is analyzed on the basis of the Mie theory, and the quantitative formula of the influence on channel direct current (DC) gain is derived. Meanwhile, the effect of a random shadowing phenomenon is investigated and quantified with the geometric and statistical model considering the position, size, and shape of the obstacles. The channel impulse response, path loss, root mean square delay spread, and bit error rate (BER) are further investigated in two different underground mining scenarios, namely, a mining roadway and coal mine working face. Simulation results show that the shadowing effect plays a major role in the influence of DC gain attenuation. Furthermore, the BER performance is noticeably degraded due to the presence of coal dust particles and obstacles, especially when the receiver is located far from the transmitter. This work will benefit the design of the VLC systems in underground mines.

Towards optimal model evaluation: enhancing active testing with actively improved estimators

With rapid advancements in machine learning and statistical models, ensuring the reliability of these models through accurate evaluation has become imperative. Traditional evaluation methods often rely on fully labeled test data, a requirement that is becoming increasingly impractical due to the growing size of datasets. In this work, we address this issue by extending existing work on active testing (AT) methods which are designed to sequentially sample and label data for evaluating pre-trained models. We propose two novel estimators: the Actively Improved Levelled Unbiased Risk (AILUR) and the Actively Improved Inverse Probability Weighting (AIIPW) estimators which are derived from nonparametric smoothing estimation. In addition, a model recalibration process is designed for the AIIPW estimator to optimize the sampling probability within the AT framework. We evaluate the proposed estimators on four real-world datasets and demonstrate that they consistently outperform existing AT methods. Our study also shows that the proposed methods are robust to changes in subsample sizes, and effective at reducing labeling costs.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.