Chroma-dependence of CIEDE2000 acceptability thresholds for dentistry

OBJECTIVES

Determine visual 50:50% color difference acceptability thresholds (AT) for regions of the dental color space with varying chromaticity.

METHODS

A 40-observer panel belonging to two different groups (dentists and laypersons) evaluated 144 dental resin composites pairs (divided in three different sets of 48 pairs according to chroma value: Low Chroma (LC), Medium Chroma (MC) and High Chroma (HC) placed 40 cm away and inside of a viewing cabinet (D65 Standard light source; diffuse/0° geometry). A Takagi-Sugeno-Kang (TSK) fuzzy approximation was used for fitting the data points and calculate the 50:50% acceptability thresholds in CIEDE2000. A paired t-test was used to evaluate the statistical significance between thresholds differences and Bonferroni correction was applied.

RESULTS

The CIEDE2000 50:50% AT were ∆E00  = 2.84, ∆E00  = 2.31 and ∆E00  = 1.80 for LC, MC and HC sets of sample pairs, respectively. The 50:50% AT values were statistically significant between the different sets of sample pairs, as well as the 50:50% AT values obtained for different observer groups.

CONCLUSIONS

50:50% CIEDE2000 acceptability thresholds for dentistry are significantly different depending on the chromaticity of the samples. Observers show higher acceptability for more achromatic samples (low chroma value) than for more chromatic samples.

CLINICAL SIGNIFICANCE

The difference in the AT for distinct regions of the dental color space can assist professionals as a quality control tool to assess clinical performance and interpret visual and instrumental findings in clinical dentistry, dental research, and subsequent standardization processes.

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches

Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.

Comparison of Global Evaluative Assessment of Robotic Surgery with objective performance indicators for the assessment of skill during robotic-assisted thoracic surgery

BACKGROUND

The Global Evaluative Assessment of Robotic Skills is a popular but ultimately subjective assessment tool in robotic-assisted surgery. An alternative approach is to record system or console events or calculate instrument kinematics to derive objective performance indicators. The aim of this study was to compare these 2 approaches and correlate the Global Evaluative Assessment of Robotic Skills with different types of objective performance indicators during robotic-assisted lobectomy.

METHODS

Video, system event, and kinematic data were recorded from the robotic surgical system during left upper lobectomy on a standardized perfused and pulsatile ex vivo porcine heart-lung model. Videos were segmented into steps, and the superior vein dissection was graded independently by 2 blinded expert surgeons with Global Evaluative Assessment of Robotic Skills. Objective performance indicators representing categories for energy use, event data, movement, smoothness, time, and wrist articulation were calculated for the same task and compared to Global Evaluative Assessment of Robotic Skills scores.

RESULTS

Video and data from 51 cases were analyzed (44 fellows, 7 attendings). Global Evaluative Assessment of Robotic Skills scores were significantly higher for attendings (P < .05), but there was a significant difference in raters' scores of 31.4% (defined as >20% difference in total score). The interclass correlation was 0.44 for 1 rater and 0.61 for 2 raters. Objective performance indicators correlated with Global Evaluative Assessment of Robotic Skills to varying degrees. The most highly correlated Global Evaluative Assessment of Robotic Skills domain was efficiency. Instrument movement and smoothness were highly correlated among objective performance indicator categories. Of individual objective performance indicators, right-hand median jerk, an objective performance indicator of change of acceleration, had the highest correlation coefficient (0.55).

CONCLUSION

There was a relatively poor overall correlation between the Global Evaluative Assessment of Robotic Skills and objective performance indicators. However, both appear strongly correlated for certain metrics such as efficiency and smoothness. Objective performance indicators may be a potentially more quantitative and granular approach to assessing skill, given that they can be calculated mathematically and automatically without subjective interpretation.

Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.

Performance comparison between multi-center histopathology datasets of a weakly-supervised deep learning model for pancreatic ductal adenocarcinoma detection

BACKGROUND

Pancreatic ductal carcinoma patients have a really poor prognosis given its difficult early detection and the lack of early symptoms. Digital pathology is routinely used by pathologists to diagnose the disease. However, visually inspecting the tissue is a time-consuming task, which slows down the diagnostic procedure. With the advances occurred in the area of artificial intelligence, specifically with deep learning models, and the growing availability of public histology data, clinical decision support systems are being created. However, the generalization capabilities of these systems are not always tested, nor the integration of publicly available datasets for pancreatic ductal carcinoma detection (PDAC).

METHODS

In this work, we explored the performace of two weakly-supervised deep learning models using the two more widely available datasets with pancreatic ductal carcinoma histology images, The Cancer Genome Atlas Project (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). In order to have sufficient training data, the TCGA dataset was integrated with the Genotype-Tissue Expression (GTEx) project dataset, which contains healthy pancreatic samples.

RESULTS

We showed how the model trained on CPTAC generalizes better than the one trained on the integrated dataset, obtaining an inter-dataset accuracy of 90.62% ± 2.32 and an outer-dataset accuracy of 92.17% when evaluated on TCGA + GTEx. Furthermore, we tested the performance on another dataset formed by tissue micro-arrays, obtaining an accuracy of 98.59%. We showed how the features learned in an integrated dataset do not differentiate between the classes, but between the datasets, noticing that a stronger normalization might be needed when creating clinical decision support systems with datasets obtained from different sources. To mitigate this effect, we proposed to train on the three available datasets, improving the detection performance and generalization capabilities of a model trained only on TCGA + GTEx and achieving a similar performance to the model trained only on CPTAC.

CONCLUSIONS

The integration of datasets where both classes are present can mitigate the batch effect present when integrating datasets, improving the classification performance, and accurately detecting PDAC across different datasets.

Development of Thickness-Dependent Predictive Methods for the Estimation of the CIEL*a*b* Color Coordinates of Monolithic and Layered Dental Resin Composites

Usually, dentin and enamel shades are layered in dental restorations with the purpose of mimicking the natural appearance of teeth. The main objective of this study was to develop and assess accuracy of a color-prediction method for both monolithic and layered dental resin-based composites with varying shades and under different illuminants. A total of 15 different shades of VITAPAN Excell, VITAPAN Dentine and VITA Physiodens as well as VITA Enamel of five different thicknesses (0.5-2.5 mm range) were used to manufacture monolithic and layered samples. A non-contact spectroradiometer with CIE 45∘/0∘ geometry was used to measure the color of all samples over a standard ceramic black background. Second-degree polynomial regression was used as predictive method for CIE-L*a*b* color coordinates. Performance of predictive models was tested using the CIEDE2000 total color difference formula (ΔE00), while accuracy was evaluated by comparative assessment of ΔE00 with corresponding 50:50% acceptability (AT00) and perceptibly (PT00) thresholds for dentistry. A mean color difference between measured (real) and predicted color of ΔE00=1.71, with 62.86% of the color differences below AT00 and 28.57% below PT00, was registered for monolithic samples. For bi-layered samples, the mean color difference was roughly ΔE00=0.50, with generally 100% and more than 85% of the estimations below AT00 and PT00, respectively. The predictive method allowed highly accurate color estimations for both monolithic and layered dental resin-based composites with varying thicknesses and under different illuminations. These results could be useful to maximize the clinical success of dental restorations.

Validation of a Hyperspectral Imaging System for Color Measurement of In-Vivo Dental Structures

A full comprehension of colorimetric relationships within and between teeth is key for aesthetic success of a dental restoration. In this sense, hyperspectral imaging can provide point-wise reliable measurements of the tooth surface, which can serve for this purpose. The aim of this study was to use a hyperspectral imaging system for the colorimetric characterization of 4 in-vivo maxillary anterior teeth and to cross-check the results with similar studies carried out with other measuring systems in order to validate the proposed capturing protocol. Hyperspectral reflectance images (Specim IQ), of the upper central (UCI) and lateral incisors (ULI), were captured on 30 participants. CIE-L*a*b* values were calculated for the incisal (I), middle (M) and cervical (C) third of each target tooth. ΔEab* and ΔE00 total color differences were computed between different tooth areas and adjacent teeth, and evaluated according to the perceptibility (PT) and acceptability (AT) thresholds for dentistry. Non-perceptible color differences were found between UCIs and ULIs. Mean color differences between UCI and ULI exceeded AT (ΔEab* = 7.39-7.42; ΔE00 = 5.71-5.74) in all cases. Large chromatic variations between I, M and C areas of the same tooth were registered (ΔEab* = 5.01-6.07 and ΔE00 = 4.07-5.03; ΔEab* = 5.80-8.16 and ΔE00 = 4.37-5.15; and ΔEab* = 5.42-5.92 and ΔE00 = 3.87-4.16 between C and M, C and I and M and I, respectively). The use of a hyperspectral camera has proven to be a reliable and effective method for color evaluation of in-vivo natural teeth.

Predicting COVID-19 Severity integrating RNA-Seq Data using Machine Learning Techniques

Abstract:

A fundamental challenge in the fight against COVID -19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear.

Methods:

A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID -19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps.

Results:

CD93, RPS24, PSCA, and CD300E were identified as a COVID -19 severity gene signature. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discriminating between control, outpatient, inpatient, and ICU COVID -19 patients was optimized, achieving an accuracy of 97.5%.

Conclusion:

In summary, during this research, a new intelligent pipeline was implemented with the goal of developing a specific gene signature that can detect the severity of patients suffering COVID -19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID -19.

Comprehensive PanCancer Gene Signature Assessment Through the Implementation of a Cascade Machine Learning System

Background:

Despite all the medical advances introduced for personalized patient treatment and the research supported in search of genetic patterns inherent to the occurrence of its different manifestations on the human being, the unequivocal and effective treatment of cancer unfortunately remains as an unresolved challenge within the scientific panorama. Until a universal solution for its control is achieved, early detection mechanisms for preventative diagnosis are increasingly avoiding therapeutic treatments which result in unreliable effectiveness. The discovery of unequivocal gene patterns allowing us to discern between multiple pathological states, could help to shed light on those patients with suspicion of oncological disease but with uncertainty in the histological and immunohistochemical results.

Methods:

This study presents an approach for pancancer diagnosis based on gene expression analysis that determines a reduced set of 12 genes, which makes it possible to distinguish between the main 14 cancer diseases.

Results:

Our cascade machine learning process has been robustly designed, obtaining a mean F1-score of 92% and a mean AUC of 99.37% in the test set. Our study showed heterogeneous over-or underexpression of the analyzed genes, which are able to act as oncogenes or tumor suppressor genes. Upregulation of LPAR5 and PAX8 was demonstrated in thyroid cancer samples. KLF5 was highly expressed in the majority of cancer types.

Conclusion:

Our model constituted a useful tool for pancancer gene expression evaluation. In addition to providing biological clues about a hypothetical common origin of cancer, the scalability of this study promises to be very useful for future studies to reinforce, confirm, and extend the biological observations presented here. Code availability and datasets are stored in the following GitHub repository to aim the research reproducibility: https://github.com/CasedUgr/PanCancerClassification.

Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis

Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background:

SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.

Methods:

The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.

Results:

The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.

Conclusion:

This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

Applications of artificial intelligence in dentistry: A comprehensive review

OBJECTIVE

To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.

MATERIALS AND METHODS

The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years.

RESULTS

Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling.

CONCLUSIONS

The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations.

CLINICAL SIGNIFICANCE

The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

Non-small-cell lung cancer classification via RNA-Seq and histology imaging probability fusion

BACKGROUND

Adenocarcinoma and squamous cell carcinoma are the two most prevalent lung cancer types, and their distinction requires different screenings, such as the visual inspection of histology slides by an expert pathologist, the analysis of gene expression or computer tomography scans, among others. In recent years, there has been an increasing gathering of biological data for decision support systems in the diagnosis (e.g. histology imaging, next-generation sequencing technologies data, clinical information, etc.). Using all these sources to design integrative classification approaches may improve the final diagnosis of a patient, in the same way that doctors can use multiple types of screenings to reach a final decision on the diagnosis. In this work, we present a late fusion classification model using histology and RNA-Seq data for adenocarcinoma, squamous-cell carcinoma and healthy lung tissue.

RESULTS

The classification model improves results over using each source of information separately, being able to reduce the diagnosis error rate up to a 64% over the isolate histology classifier and a 24% over the isolate gene expression classifier, reaching a mean F1-Score of 95.19% and a mean AUC of 0.991.

CONCLUSIONS

These findings suggest that a classification model using a late fusion methodology can considerably help clinicians in the diagnosis between the aforementioned lung cancer cancer subtypes over using each source of information separately. This approach can also be applied to any cancer type or disease with heterogeneous sources of information.

KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge

KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).

Influence of background color on color perception in dentistry

OBJECTIVE

To evaluate the influence of neutral color backgrounds on the perception of color differences in dentistry.

METHODS

A software was developed for this study that calculated the perceptibility (PT) and acceptability (AT) thresholds of color differences between a pair of computer-simulated incisor samples (n = 60 pairs) over three neutral color (white, gray and black) backgrounds. CIELAB and CIEDE2000 color difference formulas were used. Five groups of volunteer observers (N = 100) participated in the psychophysical experiment (n = 20): Dentists; Dental students; Dental auxiliaries; Dental technicians; and Laypersons. The psychophysical experiment was performed in a dark environment on a calibrated high-resolution screen. To determine PT and AT values, the 60 pairs of samples were randomly presented to each observer over the different backgrounds. The data were adjusted (TSK Fuzzy) and analyzed statistically using Student t-test and ANOVA (α = 0.05).

RESULTS

Regardless of the metric and the background used, the PT values showed no difference among different observers (p > 0.05). Dentists showed the lowest PT values. Dental technicians showed the lowest AT (p ≤ 0.05) and similar values for the three backgrounds (p > 0.05), regardless of the metric used. The other groups of observers showed the lowest and the highest AT values when using black and white backgrounds, respectively (p ≤ 0.05).

CONCLUSIONS

The lowest AT values using a black background indicates that the use of this background allows for the evaluation of slight color differences, and should be used for challenging color differences in esthetic dentistry. This study showed the influence of the observer experience on color evaluation in dentistry.

CLINICAL SIGNIFICANCE

There was no influence of the background color on the perceptibility threshold. However, dentists and dental technicians showed greater ability to perceive slight color differences compared to other groups of observers.

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.

Assessing university students' perception of academic quality using machine learning

Purpose

The aim of this research is to assess the influence of the underlying service quality variable, usually related to university students' perception of the educational experience. Another aspect analysed in this work is the development of a procedure to determine which variables are more significant to assess students' satisfaction.

Design/methodology/approach

In order to achieve both goals, a twofold methodology was approached. In the first phase of research, an assessment of the service quality was performed with data gathered from 580 students in a process involving the adaptation of the SERVQUAL scale through a multi-objective optimization methodology. In the second phase of research, results obtained from students were compared with those obtained from the teaching staff at the university.

Findings

Results from the analysis revealed the most significant service quality dimensions from the students' viewpoint according to the scores that they provided. Comparison of the results with the teaching staff showed noticeable differences when assessing academic quality.

Originality/value

Significant conclusions can be drawn from the theoretical review of the empirical evidences obtained through this study helping with the practical design and implementation of quality strategies in higher education especially in regard to university education.

Composition Classification of Ultra-High Energy Cosmic Rays

The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.

Gingival shade guides: Colorimetric and spectral modeling

OBJECTIVE

To design colorimetric and spectral models of gingival shade guides that adequately represent the color of human gingiva.

MATERIALS AND METHODS

A previously compiled database on the spectral reflectance of healthy keratinized gingiva was used for optimization. Coverage Error (CE) and Maximal Error (ME) were optimized using CIELAB and CIEDE2000 color difference formulas. A two-phase process included an FCM algorithm and a nonlinear optimization. A t test was used to compare the performance of the different numbers of clusters/tabs in gingival shade guide models (α = .05).

RESULTS

CIELAB CE and ME for shade guide models with 3 to 6 clusters ranged from 3.1 to 3.9 (P = .028 for 3 vs. 4; and P = .033 for 5 vs. 6 cluster/tab comparison), while the corresponding CIEDE2000 range was from 2.1 to 2.8 (P < .001 for 3 vs. 4 tabs; P < .025 for 4 vs. 5; and P = 0.029 for 5 vs. 6 tab comparisons). The percentage of data points exhibiting a CIELAB color difference lower than the acceptability threshold ranged from 48.7% to 71.4%, and from 52.9% to 82.4%. for CIEDE2000.

CONCLUSIONS

An increase in the number of clusters in the gingival shade guide models was associated with a decrease in coverage error (better match) to human gingiva. Gingival shade guide models with only 4 tabs provided a CIELAB and CIEDE2000 coverage error lower than the acceptability threshold for gingival color. Spectral clustering of human gingiva was determined to be valid. CIEDE2000 color difference formula outperformed the CIELAB formula in the optimization process.

CLINICAL SIGNIFICANCE

Providing a shade guide model with a small number of tabs and a coverage error lower than the 50:50% acceptability threshold would be an optimal solution for shade matching in dentistry. However, no actual gingival or tooth shade guide complies with this. The clustering method, with optimization of both Coverage Error and Maximal Error and spectral clustering that enables more reliable color formulation of cluster representatives of shade guide models, represents an advance when it comes to computer modeling in dentistry.

Whiteness difference thresholds in dentistry

OBJECTIVE

To determine the visual whiteness thresholds for esthetic dentistry using the whiteness index for dentistry based on CIELAB color space (WI_D).

METHODS

A total of 60 observers (Dentists and Laypersons; n=30) from three research sites participated in the study. A psychophysical experiment based on visual assessments of simulated images of teeth on a calibrated display was performed. Images of simulated upper central incisors (SUCI) were consecutively displayed in pairs (60) and the whiteness of each SUCI pair was compared. WI_D was used to calculate the visual thresholds (WPT- perceptibility threshold; and WAT- acceptability threshold) with 95% confidence intervals (CI) and a Takagi-Sugeno-Kang (TSK) Fuzzy Approximation model was used as fitting procedure. Data was statistical analyzed using paired t-test (α=0.05).

RESULTS

WPT and WAT were 0.72 (CI: 0.0-2.69; r²=0.52) and 2.62 (CI: 0.2-7+; r²=0.57) WI_D units, respectively. Significant differences (p<0.05) were found between WPT and WAT, and between dentist (WPT=0.46WI_D units; WAT=2.20 WI_D units) and layperson (WPT=0.94 WI_D units; WAT=2.95 WI_D units).

SIGNIFICANCE

The visual whiteness difference thresholds determined with WI_D index can serve as reference values for research and manufacturing of dental materials, and for clinical practice situations such as assessing the effectiveness of bleaching treatments.

Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling

BACKGROUND

Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis.

RESULTS

The proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis.

CONCLUSIONS

This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Predictive algorithms for determination of reflectance data from quantity of pigments within experimental dental resin composites

Background

Being able to estimate (predict) the final spectrum of reflectance of a biomaterial, especially when the final color and appearance are fundamental for their clinical success (as is the case of dental resin composites), could be a very useful tool for the industrial development of these type of materials. The main objective of this study was the development of predictive models which enable the determination of the reflectance spectrum of experimental dental resin composites based on type and quantity of pigments used in their chemical formulation.

Methods

49 types of experimental dental resin composites were formulated as a mixture of organic matrix, inorganic filler, photo activator and other components in minor quantities (accelerator, inhibitor, fluorescent agent and 4 types of pigments). Spectral reflectance of all samples were measured, before and after artificial chromatic aging, using a spectroradiometer. A Multiple Nonlinear Regression Model (MNLR) was used to predict the values of the Reflectance Factors values in the visible range (380 nm-780 nm), before and after aging, from % Pigment (%P1, %P2, %P3 and %P4) within the formulation.

Results

The average value of the prediction error of the model was 3.46% (SD: 1.82) across all wavelengths for samples before aging and 3.54% (SD: 1.17) for samples after aging. The differences found between the predicted and measured values of the chromatic coordinates are smaller than the acceptability threshold and, in some cases, are even below the perceptibility threshold.

Conclusions

Within the framework of this pilot study, the nonlinear predictive models developed allow the prediction, with a high degree of accuracy, of the reflectance spectrum of the experimental dental resin composites.

Primary embryonal rhabdomyosarcoma of the breast in an adolescent female: a case report

Rhabdomyosarcoma is the most common soft tissue sarcoma in children. Primary breast location has been reported rarely in the literature. Most rhabdomyosarcomas encountered in the breast more commonly are metastatic disease from some primary foci in another part of the body. This report addresses the case of an adolescent girl who had primary embryonal rhabdomyosarcoma of the breast with no evidence of local invasion or metastatic disease within the spectrum of Li-Fraumeni syndrome.