Statistical Learning Methods Research Articles

Prediction of Cell Survival Rate Based on Physical Characteristics of Heavy Ion Radiation.

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910-2, the local regression 9.2110-2, and the random forest 1.96 × 10-2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Feasibility of virtual reality and machine learning to assess personality traits in an organizational environment.

Personality plays a crucial role in shaping an individual's interactions with the world. The Big Five personality traits are widely used frameworks that help describe people's psychological behaviours. These traits predict how individuals behave within an organizational setting. In this article, we introduce a virtual reality (VR) strategy for relatively scoring an individual's personality to evaluate the feasibility of predicting personality traits from implicit measures captured from users interacting in VR simulations of different organizational situations. Specifically, eye-tracking and decision-making patterns were used to classify individuals according to their level in each of the Big Five dimensions using statistical machine learning (ML) methods. The virtual environment was designed using an evidence-centered design approach. The dimensions were assessed using NEO-FFI inventory. A random forest ML model provided 83% accuracy in predicting agreeableness. A k-nearest neighbour ML model provided 75%, 75%, and 77% accuracy in predicting openness, neuroticism, and conscientiousness, respectively. A support vector machine model provided 85% accuracy for predicting extraversion. These analyses indicated that the dimensions could be differentiated by eye-gaze patterns and behaviours during immersive VR. Eye-tracking measures contributed more significantly to this differentiation than the behavioural metrics. Currently, we have obtained promising results with our group of participants, but to ensure the robustness and generalizability of our findings, it is imperative to replicate the study with a considerably larger sample. This study demonstrates the potential of VR and ML to recognize personality traits.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System

Demand forecasting is a critical component of supply chain management and business operations, enabling organizations to make informed decisions about production, inventory management, and resource allocation. In recent years, predictive analytics has emerged as a powerful tool for enhancing the accuracy and efficiency of demand forecasting. This review paper explores the transformative role of predictive analytics and deep learning in demand forecasting. It examines how these advanced techniques have evolved from traditional models based on past sales data, offering nuanced predictions through sophisticated statistical and machine learning methods. Deep learning, with its neural network structures, brings automatic feature learning, complex pattern handling, and scalability, enhancing forecasting in sectors like retail, manufacturing, and healthcare. The paper reviews various deep learning models, compares them with traditional methods, and discusses their impact on business operations and decision-making. It concludes by looking at future trends in predictive analytics and deep learning in demand forecasting.

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

LociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations

The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .

A Comparison of Standard Statistical, Machine Learning and Deep Learning Methods in Forecasting the Time Series

A Comparison of Standard Statistical, Machine Learning and Deep Learning Methods in Forecasting the Time Series

The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

Functional data analysis using deep neural networks

AbstractFunctional data analysis is an evolving field focused on analyzing data that reveals insights into curves, surfaces, or entities within a continuous domain. This type of data is typically distinguished by the inherent dependence and smoothness observed within each data curve. Traditional functional data analysis approaches have predominantly relied on linear models, which, while foundational, often fall short in capturing the intricate, nonlinear relationships within the data. This paper seeks to bridge this gap by reviewing the integration of deep neural networks into functional data analysis. Deep neural networks present a transformative approach to navigating these complexities, excelling particularly in high‐dimensional spaces and demonstrating unparalleled flexibility in managing diverse data constructs. This review aims to advance functional data regression, classification, and representation by integrating deep neural networks with functional data analysis, fostering a harmonious and synergistic union between these two fields. The remarkable ability of deep neural networks to adeptly navigate the intricate functional data highlights a wealth of opportunities for ongoing exploration and research across various interdisciplinary areas.This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

Robust clustering based on trimming

AbstractClustering is one of the most widely used unsupervised learning techniques. However, it is well‐known that outliers can have a significantly adverse impact on commonly applied clustering methods. On the other hand, clustered outliers can be particularly detrimental to (even robust) statistical procedures. Therefore, it makes sense to combine concepts from Robust Statistics and Cluster Analysis to deal with both clusters and outliers simultaneously through robust clustering approaches. Among the existing robust clustering techniques, we focus on those that rely on (impartial) trimming. Trimming offers the user an easy interpretation, as standard well‐known clustering methods are applied after a fraction of the potentially most outlying observations is removed. This trimming approach, when combined with appropriate constraints on the clusters' dispersion parameters, has shown a good performance and can be implemented efficiently thorough available algorithms.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical and Graphical Methods of Data Analysis > Robust Methods

An unsupervised statistical representation learning method for human activity recognition

An unsupervised statistical representation learning method for human activity recognition

Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning

Environmental and operational variations present a significant challenge in the long-term health monitoring of bridge structures due to their potential to cause serious confounding influences and wrong decisions. These influences may lead to false alarms of damage during normal operation of a civil structure or, conversely, mask actual damage, resulting in a failure to correctly signal a warning. Consequently, a critical task in structural health monitoring, particularly for bridges, is to mitigate the negative impact of environmental variability. An initial approach is to measure the most influential environmental and operational factors and then apply statistical tools to eliminate their effects. However, this approach may not be effective under all conditions. Therefore, the primary objective of this paper is to propose an innovative unsupervised statistical learning method that addresses the challenges posed by environmental and operational variability by considering unmeasured factors. The proposed method involves three steps: data clustering, unsupervised feature selection, and novelty detection. The initial step utilizes k-means clustering to categorize features (modal frequencies) into five types of clusters, reflecting the influence of five environmental and operational factors on bridge structures; that is, temperature, humidity/rain/snow, wind speed/direction, traffic, and damage. These clustered features are then processed through an unsupervised feature selection algorithm based on reconstruction independent component analysis to extract new features. These reduced features are subsequently employed in a novelty detection model, developed using the Mahalanobis-squared distance, to analyze the environmental and operational effects. Real dynamic data of a steel arch bridge is utilized to verify the proposed method. Results demonstrate that this method can effectively handle the demanding issue stemming from unmeasured environmental and operational variability conditions.

Progress in the application of artificial intelligence-assisted molecular modification of enzymes

Natural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.

Using shell shape analysis based on landmarks to trace the geographical origin of the common cockle (Cerastoderma edule)

Determining the geographical origin of seafood is crucial for regulators and fishing industries who seek to prevent commercial fraud, enforce food safety regulations, and encourage high standards in sustainable fisheries management. The cockle, Cerastoderma edule (Linnaeus 1768), is a key species in estuarine ecosystems and is harvested all over Europe. Therefore, traceability tools using quick and inexpensive techniques to identify the origin of this bivalve are of paramount importance to support law enforcement. In this work, we explore the potential of using Geometric Morphometric (GM) methods to identify the geographical origin of cockle specimens. This method is based on landmarks identified in the shell to trace the origin of specimens obtained in nearby aquatic systems (from <35 km to <250 km distance). Specimens were collected in five aquatic systems (Ria de Aveiro, the Tagus and Sado estuaries, and the Albufeira and Óbidos coastal lagoons) in Portugal. Shells were digitalized and 16 landmarks were identified in each right valve and analyzed using Generalized Procrustes Superimposition. The discriminating power for traceability of 12 statistical and machine learning methods was assessed based on the corresponding shape variables, using R and Python (Linear Discriminant Analysis (LDA), Canonical Variable Analysis (CVA), Principal Component Analysis (PCA), Between-Group PCA (bgPCA), Partial Least Squares Discriminant (PLSD), Classification Regression Tree (CRT), Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), K Nearest Neighbors (KNN), Support Vector Machines (SVM), Extending Gradient Boosting (XGBoost) and Neural Networks (NNET). LDA, CVA, SVM, and NNET demonstrated greater accuracy and a F1-score >80%, even with a small and unbalanced sample size. The highest percentage of correctly assigned individuals was obtained in the Tagus estuary (mean 89%) and in the Albufeira lagoon (mean 93%), which were also the systems with more specimens measured (174 and 59 respectively), whereas the worst results were obtained in the Sado estuary (50%, 56 specimens). In the Albufeira coastal lagoon, the best classification methods reached 100% correct classifications. It further highlights the importance of establishing statistical standards, such as the ones developed in the current work, to evaluate different methods, as small changes in the procedure may cause substantial differences in the results and conclusions. The revision of previous works (presented as a table) showed often >90% of correct classification in both bivalves and gastropods, highlighting the potential of the techniques for other mollusks. Our results support the use of GM based on landmarks as a reliable tool for bivalve's traceability, since it is a quick, simple and inexpensive approach. Further research should extend these findings to other species and other shape analysis techniques.

Combining Statistical and Machine Learning Methods to Identify Predictors of Brazilian Students’ Proficiency in PISA 2018

Brazil’s education system lags behind international standards, with two-fifths of students scoring below the minimum level of proficiency in mathematics, science, and reading. Thus, this study combined machine learning with traditional statistics to identify the most important predictors and to interpret their effects on proficiency in the PISA 2018 mathematics, science, and reading tests. Predictors encompassed a wide range of variables, sociodemographic characteristics, teaching and learning processes, and non-cognitive skills. The outcome of the present study was proficiency in mathematics, science, and reading. PISA proficiency levels were grouped into “low proficiency” and “proficient” categories, using a classification system commonly employed in PISA reports. Using random forest analysis, a machine learning method, I compared the importance of predictors for proficiency in mathematics, science, and reading. I then adjusted multilevel logistic regression analyses to investigate the relationship between the top predictors and the outcomes. Among the top predictors for the three outcomes identified, annual household income, parents’ highest occupational status, and early childhood education and care were positively associated with proficiency in mathematics, science, and reading, while grade repetition and additional instruction were negatively associated with these outcomes. These findings urge Brazilian policymakers and educators to prioritize initiatives that strengthen early childhood programs, minimize grade repetition, and promote effective learning strategies.

Statistical modelling of height growth in urban forestry plantations

ABSTRACT Urban plantation dynamics in different topographical and climatic conditions in Odisha were evaluated using linear model selection and regularisation techniques. The main objective was to evaluate how and to what extent the urban plantations respond to various climatic and edaphic conditions. The relationship between vegetation growth and climatic and soil parameters was studied using four statistical learning tools, subset selection, ridge regression, lasso, and partial least squares regression, and their performance was compared to a multiple regression model. The test MSE for the subset selection, ridge regression, lasso, and partial least squares regression models was evaluated to be 16,261.54, 12245.11, 16263.79 and 14,317.21, respectively. Results proved that statistical learning methods, namely subset selection, lasso, ridge regressions and partial least squares regression, were more accurate than multiple linear regression. From the results, it can be safely concluded that temperature shows greater correlation with the growth parameters. Precipitation also plays a vital role in vegetation dynamics. Soil parameters indicate a positive correlation with that of the growth.

A retrospective identification of severely injured patients using ICD 10 diagnoses codes : Part of the project "Quality of life and ability to work after severe trauma" (LeAf Trauma)

Due to continuous improvements in treatment, more and more severely and seriously injured patients are surviving. The complexity of the injury patterns of these patients means that they are difficult to map in routine data. The aim of the data exploration was to identify ICD 10 diagnoses that show an association with an injury severity score (ISS) ≥ 16 and could therefore be used to operationalize severely injured patients in routine data. The coded four-digit ICD 10 S diagnoses and the calculated ISS of trauma patients from the Armed Forces Central Hospital Koblenz (BwZKrhs) and the University Hospital Düsseldorf (UKD) were analyzed using statistical association measures (phi and Cramer'sV), linear regressions and machine learning methods (e.g., random forest). The S diagnoses of facial, head, thoracic and pelvic injuries, associated with an ISS ≥ 16 were identified. Some S diagnoses showed an association with an ISS ≥ 16 in only 1 of the 2 datasets. Likewise, facial, head, thoracic and pelvic injuries were found in the subgroup of 18-55-year-old patients. The current evaluations show that it is possible to identify ICD 10 S diagnoses that have asignificant association with an ISS ≥ 16. According to the annual report of the trauma register of the German Society for Trauma Surgery (TR-DGU®), injuries with an abbreviated injury scale (AIS) ≥ 3 are particularly common in the head and thoracic regions.