Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning.

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

The Mex-Cog cognitive assessment battery: discriminant analysis of the cognitive performance profile in older adults.

OBJECTIVE: To analyze the cognitive profile of a clinical sample using the Mex-Cog cognitive battery and establish which cognitive measures and domains contribute most to group separation. MATERIALS AND METHODS: A group of 145 older adults previously diagnosed with dementia (n= 47), mild cognitive impairment MCI (n= 47), or as cognitively normal (n= 51) were assessed with the Mex-Cog cognitive battery. Six linear discriminant analyses (LDA) were estimated to compare dementia vs. cognitively normal, MCI vs. cognitively normal, and MCI vs. dementia, using ten individual measures and six cognitive domains. We used a leave-one-out cross-validation procedure to evaluate the predictive capacity of LDA models. RESULTS: Discriminant functions using individual measures and domains distinguished correctly 100% of dementia and cognitively normal groups showing a memory and executive function profile. The predictive group membership for MCI versus cognitively normal varied between 82 and 85%, with a cognitive profile associated with attention-executive function followed by memory. Group separation between MCI and dementia was between 80 and 87%, characterized by orientation, memory, and visuospatial abilities. CONCLUSIONS: The Mex-Cog cognitive battery is useful for identifying cognitive impairment in older adults.

Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics.

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach.

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Qualitative Histologic Assessment vs. Geomorphometric Analysis of Nerve Fiber Shape after the Intraneural Application of Liposomal Bupivacaine / Evaluación Histológica Cualitativa Versus Análisis Geomorfométrico de la Forma de la Fibra Nerviosa Después de la Aplicación Intraneural de Bupivacaína Liposomal

SUMMARY: The preserved form of all components of the nerve fiber is a prerequisite for the proper conduction of the nerve impulse. various factors can change the shape of nerve fibers. In everyday practice, qualitative histological analysis is the gold standard for detecting changes in shape. Geometric morphometry is an innovative method that objectively enables the assessment of changes in nerve fibers' shape after local anesthetics action. A total of sixty sciatic nerves were used as material, which was intraneural injected with saline solution in the control group (n=30), and a solution of 1.33 % liposomal bupivacaine (n=30) in the test group. After the animals were sacrificed, nerve samples were taken and histological preparations were made. The preparations were first described and examined using a qualitative histological method, after which digital images were made. The images were entered into the MorphoJ program and processed using the method of geometric morphometry. Qualitative histological examination revealed no differences in nerve fibers after intraneurally applied physiological solution and liposomal bupivacaine. Using the method of geometric morphometry, a statistically significant change in the shape of axons was found after intraneurally applied saline solution and liposomal bupivacaine (p=0.0059). No significant differences in histological changes were found after the qualitative histological analysis of nerve fiber cross-section preparations. A statistically significant change in the shape of nerve fiber axons was observed after geometric morphometric analysis of digital images after intraneural application of saline and liposomal bupivacaine.

La forma conservada de todos los componentes de la fibra nerviosa es un requisito previo para la conducción correcta del impulso nervioso. Varios factores pueden cambiar la forma de las fibras nerviosas. En la práctica diaria, el análisis histológico cualitativo es el estándar de oro para detectar cambios de forma. La morfometría geométrica es un método innovador que permite evaluar objetivamente los cambios en la forma de las fibras nerviosas después de la acción de los anestésicos locales. Se utilizó como material un total de sesenta nervios ciáticos, que se inyectaron intraneuralmente con solución salina en el grupo control (n=30), y una solución de bupivacaína liposomal al 1,33 % (n=30) en el grupo de prueba. Después de sacrificados los animales, se tomaron muestras de nervios y se realizaron preparaciones histológicas. Primero se describieron y examinaron las preparaciones utilizando un método histológico cualitativo, después de lo cual se tomaron imágenes digitales. Las imágenes fueron ingresadas al programa MorphoJ y procesadas mediante el método de morfometría geométrica. El examen histológico cualitativo no reveló diferencias en las fibras nerviosas después de la aplicación intraneural de solución fisiológica y bupivacaína liposomal. Usando el método de morfometría geométrica, se encontró un cambio estadísticamente significativo en la forma de los axones después de la aplicación intraneural de solución salina y bupivacaína liposomal (p = 0,0059). No se encontraron diferencias significativas en los cambios histológicos después del análisis histológico cualitativo de las preparaciones de secciones transversales de fibras nerviosas. Se observó un cambio estadísticamente significativo en la forma de los axones de las fibras nerviosas después del análisis de morfometría geométrica de imágenes digitales después de la aplicación intraneural de solución salina y bupivacaína liposomal.

Risk assessment based on dose-responsive and time-responsive genes to build PLS-DA models for exogenously induced lung injury.

Xenobiotics can easily harm human lungs owing to the openness of the respiratory system. Identifying pulmonary toxicity remains challenging owing to several reasons: 1) no biomarkers for pulmonary toxicity are available that might help to detect lung injury; 2) traditional animal experiments are time-consuming; 3) traditional detection methods solely focus on poisoning accidents; 4) analytical chemistry methods hardly achieve universal detection. An in vitro testing system able to identify the pulmonary toxicity of contaminants from food, the environment, and drugs is urgently needed. Compounds are virtually infinite, whereas toxicological mechanisms are countable. Therefore, universal methods to identify and predict the risks of contaminants can be designed based on these well-known toxicity mechanisms. In this study, we established a dataset based on transcriptome sequencing of A549 cells upon treatment with different compounds. The representativeness of our dataset was analyzed using bioinformatics methods. Artificial intelligence methods, namely partial least squares discriminant analysis (PLS-DA) models, were employed for toxicity prediction and toxicant identification. The developed model predicted the pulmonary toxicity of compounds with a 92 % accuracy. These models were submitted to an external validation using highly heterogeneous compounds, which supported the accuracy and robustness of our developed methodology. This assay exhibits universal potential applications for water quality monitoring, crop pollution detection, food and drug safety evaluation, as well as chemical warfare agent detection.

Familiar and unfamiliar speaker recognition assessment and system emulation for cochlear implant users.

In the area of speech processing, human speaker identification under naturalistic environments is a challenging task, especially for hearing-impaired individuals with cochlear implants (CIs) or hearing aids (HAs). Motivated by the fact that electrodograms reflect direct CI stimulation of input audio, this study proposes a speaker identification (ID) investigation using two-dimensional electrodograms constructed from the responses of a CI auditory system to emulate CI speaker ID capabilities. Features are extracted from electrodograms through an identity vector (i-vector) framework to train and generate identity models for each speaker using a Gaussian mixture model-universal background model followed by probabilistic linear discriminant analysis. To validate the proposed system, perceptual speaker ID for 20 normal hearing (NH) and seven CI listeners was evaluated with a total of 41 different speakers and compared with the scores from the proposed system. A one-way analysis of variance showed that the proposed system can reliably predict the speaker ID capability of CI (F[1,10] = 0.18, p = 0.68) and NH (F[1,20] = 0, p = 0.98) listeners in naturalistic environments. The impact of speaker familiarity is also addressed, and the results show a reduced performance for speaker recognition by CI subjects using their CI processor, highlighting limitations of current speech processing strategies used in CIs/HAs.

Sex assessment using the radius bone in a French sample when applying various statistical models.

Sex estimation of skeletal remains is one of the most important tasks in forensic anthropology. The radius bone is useful to develop standard guidelines for sex estimation across various populations and is an alternative when coxal or femoral bones are not available.The aim of the present study was to assess the sexual dimorphism from radius measurements in a French sample and compare the predictive accuracy of several modelling techniques, using both classical statistical methods and machine learning algorithms.A total of 78 left radii (36 males and 42 females) were used in this study. Sixteen measurements were made. The modelling techniques included a linear discriminant analysis (LDA), flexible discriminant analysis (FDA), regularised discriminant analysis (RDA), penalised logistic regression (PLR), random forests (RF) and support vector machines (SVM).The different statistical models showed an accuracy of classification that is greater than 94%. After selection of variables, the accuracies increased to 97%. The measurements made at the proximal part of the radius (sagittal and transversal diameters of the head, and sagittal diameter of the neck), at distal part (maximum width of the distal epiphysis) and of the entire bone (maximum length) stand out among the various models.The present study suggests that the radius bone constitutes a valid alternative for sex estimation of skeletal remains with comparable classification accuracies to the pelvis or femur and that the non-classical statistical models may provide a novel approach to sex estimation from the radius bone. However, the extrapolation of the current results cannot be made without caution because our sample was composed of very aged individuals.

Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer.

Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118-1052 cm-1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm-1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.

Evaluation of the obturator foramen as a sex assessment trait.

Correctly assessing sex from skeletal remains is one of the main elements of creating a biological profile. Many traits allow for this, the obturator foramen being one. However, research on its accuracy has provided mixed results. This study examines the obturator foramen using a 5-point grading scale to assess the degree of sexual dimorphism in four known age and sex skeletal collections from the UK and South Africa. Overall, sexual dimorphism was found in the obturator foramen when using the new scoring system; however, accuracies for correct sex classification ranged from ~ 46 to ~ 75%. Considering its wide range in accuracy rates across the four samples and difficulty in identifying the subtle changes in morphology, the obturator foramen should only be used as part of a multifactorial assessment of sex.

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications.

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

Assessment of emotional states in EEG signals using multi-frequency power spectrum and functional connectivity patterns.

In this work, an attempt has been made to characterize arousal and valence emotional states using Electroencephalogram (EEG) signals and Phase lag index (PLI) based functional connectivity features. For this, EEG signals are considered from a publicly available DEAP database. Signals are decomposed into four frequency bands, namely theta (θ, 4-7 Hz), alpha (a, 8-12 Hz), beta (ß, 13-30 Hz), and gamma (γ, 30-45 Hz). Two features, namely relative PSD and PLI, are calculated from each band of signals with Welch's periodogram. Four classifiers, namely Random Forest (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and K-Nearest Neighbor (KNN), are employed to discriminate the emotional states. Results show that the proposed approach can differentiate emotional states using EEG signals. It is observed that there is strong functional connectivity in Fp1-02 and Fp2-Pz in all emotional states for different frequency bands. SVM classifier yields the highest classification performance for arousal, and RF yields the highest performance for valence in the y band. The combination of all features performs the best for the valence dimension. Thus, the proposed approach could be extended for classifying various emotional states in clinical settings. Clinical Relevance- This establishes PLI based approach for improved classification (fl = 74.77% for Arousal fl = 74.94 for valence) of emotional states.

Biometric sex assessment from the femur and tibia in a modern Greek population.

Forensic anthropologists assess sex by analysing quantitative and qualitative characters of the human skeleton. In general, the pelvis and skull are the skeletal regions used most often, but in many cases, they are missing or fragmentary. In such circumstances, where only limb bones are present, it is necessary to use techniques based on other skeletal elements. Metric traits of the long bones of the lower extremities have been reported as reliable indicators of sex. This study was designed to determine whether the two main long bones of the leg, the femur and tibia, can be used for the assessment of sex on a Greek skeletal population. The skeletal sample used in this study comes from the modern human skeletal collection that is currently housed at the National and Kapodistrian University of Athens and is known as The Athens Collection. It consists of 371 femora and 372 tibiae corresponding to 200 adult individuals (111 males and 89 females). The age range is 19-96 years for males and 20-99 years for females. The maximum lengths and epiphyseal widths were measured for the present study, and it was found that the discriminant analysis of the metrical data of each long bone provided high sex discrimination accuracies. The rate of correct sex discrimination based on different long bones ranged from 91.50 % (left femur) to 93.40 % (left tibia). Our results suggest that lower limb bones can be used effectively for sexing in forensic contexts, in addition to other sex assessment techniques.

A fast and low-cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification.

BACKGROUND: Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets. RESULTS: The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%). CONCLUSION: Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method. © 2022 Society of Chemical Industry.

Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression.

The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLSρ s , PLSτ A , PLSτ B , PLSτ C , PLS D , PLSτ GK , PLS G , and PLS U . Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLSρ s is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity.

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring.

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today's advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X-Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

Quality assessment of fried licorice based on fingerprints and chemometrics.

Fried licorice is obtained by frying licorice without using any auxiliary materials, and it is widely used both as food and medicine in China. To explore the influence of licorice origin on the quality of fried licorice, a method based on fingerprinting and chemometrics was developed. Twenty batches of licorice were selected from four locations. The reference chromatograms of each location were established via similarity analysis. Chemometric methods, such as cluster, principal component, and orthogonal partial least squares discriminant analyses were used to evaluate the changes in the composition of fried licorice, predict its origin, and reflect its quality. Mass spectrometry was used to identify the chemical components. Finally, an origin prediction function was established via discriminant analysis to trace the origin of licorice. The model was demonstrated to be stable, reliable, and accurate in predicting licorice origin and to provide a reference for origin traceability.

Sex estimation through geometric morphometric analysis of the frontal bone: an assessment in pre-pubertal and post-pubertal modern Spanish population.

Sex estimates is a key step of biological profile assessment in a forensic or anthropologic context. In this study, the sexual dimorphism of the frontal bone was analyzed to assess the accuracy of sex estimates using a geometric morphometric approach in a pre-pubertal and post-pubertal sample. The shape of the frontal bone was digitized on the lateral cephalograms of 87 pre-pubertal subjects (42 males, mean age 10.14, SD ± 1.48 years; 45 females mean age 10.02, SD ± 1.11 years) and 103 post-pubertal ones (53 males, mean age 29.33 SD ± 11.88 years; 50 females, mean age 26.77 SD ± 11.07 years). A generalized Procrustes analysis (GPA) was performed for shape analyses, filtering the effects of position, rotation, translation, and size. A principal component analysis (PCA) was performed on the GPA transformed variables, and a multiple logistic regression model was used to assess the accuracy of sex estimates. In both age groups, the average size of the centroid was significantly larger in males. The females presented shapes with a shorter distance between P2 (glabella) and P1 (supratoral) and a general narrowing of the structure on the sagittal plane. In the pre-pubertal group, the shape difference was not statistically significant. In the post-pubertal group, the mean shape was significantly different between the sexes. The method displayed a high accuracy for sex estimates (88.7% males, 90.3% females) also when applied in a validation sample (82.6% males and 94.1% females). The described morphometric analysis of the frontal bone is based on a limited number of landmarks, which allows sex estimates with high accuracy in post-pubertal subjects, while it is not applicable in pre-pubertal ones.

Repeated measures discriminant analysis using multivariate generalized estimation equations.

Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which are characterized by binary, ordinal, or mixed types of response distributions. This study investigates the accuracy of repeated measures discriminant analysis (RMDA) based on the multivariate generalized estimating equations (GEE) framework for classification in multivariate repeated measures designs with the same or different types of responses repeatedly measured over time. Monte Carlo methods were used to compare the accuracy of RMDA procedures based on GEE, and RMDA based on maximum likelihood estimators (MLE) under diverse simulation conditions, which included number of repeated measure occasions, number of responses, sample size, correlation structures, and type of response distribution. RMDA based on GEE exhibited higher average classification accuracy than RMDA based on MLE especially in multivariate non-normal distributions. Three repeatedly measured responses namely severity of epilepsy, current number of anti-epileptic drugs, and parent-reported quality of life in children with epilepsy were used to demonstrate the application of these procedures.

Assessment of patients' understanding of inflammatory bowel diseases: Development and validation of a questionnaire.

BACKGROUND: Educating patients regarding thier inflammatory bowel disease (IBD) is important for their empowerment and disease management. We aimed to develop a questionnaire to evaluate patient understanding and knowledge of IBD. METHODS: We have developed the Understanding IBD Questionnaires (U-IBDQ), consisting of multiple-choice questions in two versions [for Crohn's disease (CD) and ulcerative colitis (UC)]. The questionnaires were tested for content and face validity, readability, responsiveness and reliability. Convergent validity was assessed by correlating the U-IBDQ score with physician's subjective assessment scores. Discriminant validity was assessed by comparison to healthy controls (HC), patients with chronic gastrointestinal (GI) conditions other than IBD, and to GI nurses. Multivariate analysis was performed to determine factors associated with a high level of disease understanding. RESULTS: The study population consisted of IBD patients (n = 106), HC (n = 35), chronic GI disease patients (n = 38) and GI nurses (n = 19). Mean U-IBDQ score among IBD patients was 56.5 ± 21.9, similar for CD and UC patients (P = 0.941), but significantly higher than that of HC and chronic GI disease patients and lower than that of GI nurses (P < 0.001), supporting its discriminant validity. The U-IBDQ score correlated with physician's subjective score (r = 0.747, P < 0.001) and was found to be reliable (intra-class correlation coefficient = 0.867 P < 0.001). Independent factors associated with high U-IBDQ scores included academic education (OR = 1.21, 95% CI 1.10-1.33, P < 0.001), biologic therapy experience (OR = 1.24, 95% CI 1.01-1.53, P = 0.046), and IBD diagnosis at <21 years of age (OR = 2.97, 95% CI 1.05-8.87, P = 0.050). CONCLUSIONS: The U-IBDQ is a validated, reliable and short, self-reported questionnaire that can be used for assessing understanding of disease pathophysiology and treatment by IBD patients.