Percentage Of Classification Research Articles

Optimisation of an Industrial Optical Sorter of Legumes for Gluten-Free Production Using Hyperspectral Imaging Techniques

The market demand for gluten-free food is increasing due to the growing gluten sensitivity and coeliac disease (CD) in the population. The market requires grass-free cereals to produce gluten-free food. This requires sorting methods that guarantee the perfect separation of gluten contaminants from the legumes. The objective of the research was the development of an optical sorting system based on hyperspectral image processing, capable of identifying the spectral characteristics of the products under investigation to obtain a statistical classifier capable of enabling the total elimination of contaminants. The construction of the statistical classifier yielded excellent results, with a 100% correct classification rate of the contaminants. Tests conducted subsequently on an industrial optical sorter validated the result of the preliminary tests. In fact, the application of the developed classifier was able to correctly select the contaminants from the mass of legumes with a correct classification percentage of 100%. A small proportion of legumes was misclassified as contaminants, but this did not affect the scope of the work. Further studies will aim to reduce even this small share of waste with investigations into optimising the seed transport systems of the optical sorter.

Neck circumference as an additional predictor of cardiovascular disease mortality: A multi-center prospective population-based study in northeastern China

Background and aimsThis study aimed to assess the potential of neck circumference (NC) and neck-to-height ratio (NHR) as predictors of future cardiovascular disease (CVD) mortality in a general population from Northeastern China. MethodsA multi-center prospective study was conducted in Northeastern China, involving 18, 796 participants. The associations between NC or NHR and the incidence of overall CVD mortality, stroke mortality, and coronary heart disease (CHD) mortality were examined using multivariate Cox regression models. Hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs) were calculated. Reclassification analyses were conducted to determine the incremental predictive value of NC or NHR. ResultsNC was significantly associated with the risk of CVD mortality, independent of other anthropometric measurements for obesity. Individuals in the highest quartile of NC had a 1.83-fold (95% CI 1.29 to 2.61) and a 2.40-fold (95% CI 1.45 to 4.00) higher risk of overall CVD mortality and CHD mortality, respectively. Larger NC was significantly related to a heightened risk of ischemic stroke mortality, although no such association was observed with hemorrhagic stroke mortality. Furthermore, the risk of overall CVD mortality, stroke mortality, and CHD mortality increased by approximately 1.21 to 1.25 times per 1-SD change in NC. Similar findings were observed for NHR. The percentages of correct classification of overall CVD mortality improved by 12.1% and 16.3% after the addition of NC or NHR into established models, respectively. ConclusionsNC and NHR might be promising predictors of CVD mortality, with higher values indicating greater risk.

PS-Merge operator in the classification of gait biomarkers: A preliminary approach to eXplainable Artificial Intelligence

Background: Many neurodegenerative diseases affect human gait. Gait analysis is an example of a non-invasive manner to diagnose these diseases. Nevertheless, gait analysis is difficult to do because patients with different neurodegenerative diseases may have similar human gaits. Machine learning algorithms may improve the correct identification of these pathologies. However, the problem with many classification algorithms is a lack of transparency and interpretability for the final user. Methods: In this study, we implemented the PS-Merge operator for the classification, employing gait biomarkers of a public dataset. Results: The highest classification percentage was 83.77%, which means an acceptable degree of reliability. Conclusions: Our results show that PS-Merge has the ability to explain how the algorithm chooses an option, i.e., the operator can be seen as a first step to obtaining an eXplainable Artificial Intelligence (XAI).

Characterizing Diploid and Tetraploid Potato Cultivars with Reflectance Spectroscopy

Characterizing potato cultivars is essential for diagnosing the specific production and management requirements. Employing techniques that facilitate rapid, objective, and cost-effective identification of various aspects of plant physiology is crucial for generating this valuable information. In this study, our goal was to identify different potato cultivars across three distinct phenological phases utilizing reflectance spectroscopy within the visible and near-infrared ranges. Reflectance spectra were measured using a portable spectrometer on leaves obtained from five different potato cultivars. We constructed classification models to determine the accurate classification percentages for each cultivar within specific phenological stages. General trends in the reflectance spectra were observed, which were consistent across all five cultivars within the measured range. During the vegetative phase, there was a uniform reflectance of around 5% in the green region. This value experienced a slight decrease during the flowering phase and subsequently rose to approximately 12% during the senescence phase. In the near-infrared (NIR) range, a reduction in reflectance from 50 to 30% was noted during the flowering phase, followed by a cultivar-dependent increase towards senescence. The specific wavelengths identified in the spectra enabled the accurate classification of plants from each cultivar across the vegetative, flowering, and senescence phases. The classification rates were notably high, with a success rate of 87% for Var. Paola, 96% for Var. Paysandú, 93% for Var. Violeta, and 100% for cv. 448 and 440. The Var. Paola exhibited superior correct classification rates, ranging from 85% for Paysandú and Violeta, 90% for cv. 440, to 100% for cv. 448. The characterization of potato cultivars using spectroscopy techniques yields valuable information that serves as fundamental input for precision agriculture systems.

Using automated text classification to explore uncertainty in NICE appraisals for drugs for rare diseases.

This study examined the application, feasibility, and validity of supervised learning models for text classification in appraisals for rare disease treatments (RDTs) in relation to uncertainty, and analyzed differences between appraisals based on the classification results. We analyzed appraisals for RDTs (n=94) published by the National Institute for Health and Care Excellence (NICE) between January 2011 and May 2023. We used Naïve Bayes, Lasso, and Support Vector Machine models in a binary text classification task (classifying paragraphs as either referencing uncertainty in the evidence base or not). To illustrate the results, we tested hypotheses in relation to the appraisal guidance, advanced therapy medicinal product (ATMP) status, disease area, and age group. The best performing (Lasso) model achieved 83.6 percent classification accuracy (sensitivity=74.4 percent, specificity=92.6 percent). Paragraphs classified as referencing uncertainty were significantly more likely to arise in highly specialized technology (HST) appraisals compared to appraisals from the technology appraisal (TA) guidance (adjusted odds ratio=1.44, 95 percent CI 1.09, 1.90, p=0.004). There was no significant association between paragraphs classified as referencing uncertainty and appraisals for ATMPs, non-oncology RDTs, and RDTs indicated for children only or adults and children. These results were robust to the threshold value used for classifying paragraphs but were sensitive to the choice of classification model. Using supervised learning models for text classification in NICE appraisals for RDTs is feasible, but the results of downstream analyses may be sensitive to the choice of classification model.

Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance

The article describes a method of image classification based on the estimation of the distance to the etalon class. The implementation of estimates gives a significant gain in classification speed compared to linear search while maintaining a decent level of accuracy. The methodology is based on the use of the triangle inequality for images given by a set of binary vectors as descriptors of the image key points. The evaluation is applied to the "object descriptor – etalon" classification method, which is based on the descriptor voting procedure. An analysis of evaluation options is carried out using the parameters of the etalon sets in the form of a medoid and the closest or farthest points from it. The gain in classification time compared to the traditional method proportionally depends on the number of descriptors in the etalon description. Software simulation of classifiers with the implementation of evaluation shows a gain in speed of 350-450 times for the description of 500 descriptors while maintaining one hundred percent classification accuracy on the training set of similar NFT images. A control sample experiment shows that the classifier with estimation can respond better to image details compared to the traditional method.

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

Early detection of bacterial wilt in bananas caused by Ralstonia solanacearum using reflectance spectroscopy

Bacterial wilt is one of the most important vascular diseases that generate high worldwide losses. The main strategy to control it involves early detection of infected plants that mitigate its spread in the field. This research characterized the spectral response of healthy plants, infected with Ralstonia solanacearum E.F. Smith race 2 and subjected to water stress, in two banana cultivars by spectroscopy reflectance. The spectral data were used to detect vascular wilt during the incubation period of the disease in Gros Michel and Cavendish banana cultivars. The reflectance data were collected from the leaves using a portable spectrometer. Both varieties of healthy plants showed a typical low reflectance in the visible range, with a peak of green of around 12% in Williams and 5% in Gros Michel. In the measured infrared range, the two varieties presented values between 60 and 70% in the latest days post-infection measurements, decreasing to ~ 50% after 12 dpi. The results obtained indicate that plants infected with R. solanacearum have no initial increase in reflectance in the visible (Vis) range, whereas decrease rapidly after 6 dpi in the 700–1000 nm range. This methodology identifies three wavelengths (710, 770, and 965 nm) that allow differentiation between ill and healthy plants after 6 days post-inoculation, with a percentage of correct classification that ranges from 96 to 100%. The spectral response characterization in healthy plants and those subjected to various types of stress is a fundamental input for the development of early disease detection systems based on spectroscopy techniques.

A comparative study of fluorescence and Raman spectroscopy for discrimination of virgin olive oil categories: Chemometric approaches and evaluation against other techniques

Spectroscopic techniques are presented as a reliable and economical alternative for the discrimination of olive oil quality categories. In this work, fluorescence and Raman spectroscopic techniques have been compared by evaluating different characteristics focused on sample preparation and analysis time, as well as classification percentages by chemometric models (PLS-DA) to classify olive oils into three categories: extra virgin (EVOO), virgin (VOO), and lampante (LOO). A statistical evaluation of the quality of the chemometric models was performed by comparing the parameters of sensitivity in terms of the true positive rate (%TPR), specificity (or true negative rate %TNR), and classification error in calibration, cross-validation and prediction. Fluorescence spectroscopy was the screening technique with the best overall accuracy in the ternary models for prediction (78.60%), with higher results than those obtained by Raman (66.03%), the latter being below the average error in the tasting panels. Regarding the classification obtained in binary models (EVOO/Non-EVOO and LOO/Non-LOO), fluorescence spectroscopy continued to present better results than Raman, with classifications of 92.5% and 88.5 % for both models, respectively (Raman obtained percentages of 70.7% and 66.6%). A comparison of the results obtained with confirmation techniques (headspace-gas chromatography-ion mobility spectrometry, HS-GC-IMS) and other spectroscopic techniques (NIR and MIR) studied in previous research was carried out, where it was observed that fluorescence spectroscopy obtained the closest classification percentage to the HS-GC-IMS technique (86.4%) in ternary models. On the other hand, the NIR technique obtained the highest classification percentage in binary models, with 92.6% for EVOO/Non-EVOO and 88.9% for LOO/Non-LOO compared to the value obtained using GC-IMS (95.4 and 86.4 %). Evaluating the rest of the parameters of interest for the olive industry, IR (both NIR and MIR) was the most interesting technique because it is a cheap, simple sensor with fast chemometric treatments and is environmentally-friendly. The results of this comparative study can be used to provide solutions and alternatives for applying new analytical strategies to support the work of tasting panels.

Machine Learning in the Hyperspectral Classification of Glycaspis brimblecombei (Hemiptera Psyllidae) Attack Severity in Eucalyptus

Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In order to classify these levels, the use of machine learning (ML) algorithms can help process the data faster and more accurately. The objectives were: (I) to evaluate the spectral behavior of the G. brimblecombei attack levels; (II) find the most accurate ML algorithm for classifying pest attack levels; (III) find the input configuration that improves performance of the algorithms. Data were collected from a clonal eucalyptus plantation (clone AEC 0144—Eucalyptus urophilla) aged 10.3 months old. Eighty sample evaluations were carried out considering the following severity levels: control (no shells), low infestation (N1), intermediate infestation (N2), and high infestation (N3), for which leaf spectral reflectances were obtained using a spectroradiometer. The spectral range acquired by the equipment was 350 to 2500 nm. After obtaining the wavelengths, they were grouped into representative interval means in 28 bands. Data were submitted to the following ML algorithms: artificial neural networks (ANN), REPTree (DT) and J48 decision trees, random forest (RF), support vector machine (SVM), and conventional logistic regression (LR) analysis. Two input configurations were tested: using only the wavelengths (ALL) and using the spectral bands (SB) to classify the attack levels. The output variable was the severity of G. brimblecombei attack. There were differences in the hyperspectral behavior of the leaves for the different attack levels. The highest attack level shows the greatest distinction and the highest reflectance values. LR and SVM show better accuracy in classifying the severity levels of G. brimblecombei attack. For the correct classification percentage, the RL and SVM algorithms performed better, both with accuracy above 90%. Both algorithms achieved F-score values close to 0.90 and above 0.8 for Kappa. The entire spectral range guaranteed the best accuracy for both algorithms.

ANALYSIS OF STUDENTS' MATHEMATICS LEARNING OUTCOMES IN THE TOPIC OF MATRICES, SPECIFICALLY DETERMINANTS AND MATRIX INVERSES, BASED ON BLOOM'S TAXONOMY

This study is to classify daily mathematics tests on matrix subject with determinant and matrix inverse as sub-material and describe the mathematics learning outcomes of students in class XI MIPA 3 at SMAN 5 Pekanbaru for the 2022/2023 academic year based on the cognitive domain of Bloom's Taxonomy. The research method used was a descriptive method with a qualitative approach. The subject of this research is the answer sheets for the daily mathematic test on matrix subject of the students in class XI MIPA 3. Based on the results of research, the percentage of item classification was 60% for the cognitive level of understanding (C2) and 40% for the cognitive level of application (C3). The results of learning mathematics for class XI MIPA 3 students has been reached the level of applied cognitive ability (C3), with the following average percentages: cognitive understanding level (C2) of 82.43% and applied cognitive level (C3) of 48.65%.

Abstract 15667: The Transvalvular Pressure Gradient to Valve Area Ratio: A Novel, Simple Doppler Echocardiographic Parameter to Adjudicate Low-Gradient Aortic Stenosis Severity

Background: In patients with low-gradient (LG) aortic stenosis (AS), dobutamine stress echocardiography (DSE) or aortic valve calcium scoring (AVCS) by computed tomography is required to confirm AS severity and the indication for intervention. Hypothesis: We hypothesized that the mean transvalvular gradient to effective orifice area ratio (MG/EOA, in mmHg/cm 2 ) measured during rest echocardiography predicts true-severe AS (TSAS) and clinical outcomes in LG-AS patients. The present study aimed to assess the diagnostic accuracy and prognostic value of the MG/EOA ratio in LG-AS patients. Methods: The diagnostic accuracy of the MG/EOA ratio to predict TSAS was assessed in: i) an in vitro dataset obtained in a circulatory model, and ii) an in vivo dataset collected in 198 patients recruited in the prospective observational cohort TOPAS study (NCT01835028), using receiver operating characteristic (ROC) curve analysis. The prognostic value of this parameter was assessed using spline curve analysis and multivariate Cox regression, using a composite of aortic valve replacement or all-cause mortality at 2-years as clinical endpoint. Results: The MG/EOA ratio at rest predicted TSAS with an area under the ROC curve of 0.99 [0.98-1.00] in the in vitro dataset and 0.92 [0.87-0.95] in the in vivo dataset of LG-AS patients. In the in vivo dataset predominantly composed of patients with a low flow state, the optimal cut-off of MG/EOA ratio was 29 mmHg/cm 2 with a normal flow state (percentage of correct classification: 95%) and 25 mmHg/cm 2 with a low flow state (85%). In LG-AS patients, a MG/EOA ratio ≥ 23 mmHg/cm 2 was associated with increased risk of the composite endpoint (unadjusted HR=2.16 [1.58-3.06], p<0.001; adjusted HR=2.80 [1.94-4.03], p<0.001). The net reclassification index (NRI) of the resting parameter MG/EOA was 0.57 (p<0.001), which was similar to projected EOA (NRI=0.61, p<0.001) measured by DSE. Conclusion: We propose a new, easily obtainable parameter from standard echocardiography that outperforms other rest echocardiographic parameters and is non-inferior to DSE with respect to diagnostic accuracy and prognostic value. The MG/EOA ratio can be useful in LG-AS to confirm AS severity and may thus avoid the use of additional tests such as DSE or AVCS.

Detecting Faking on Self-Report Measures Using the Balanced Inventory of Desirable Responding

We compared three methods for scoring the Balanced Inventory of Desirable Responding (BIDR) to detect faked responses on self-report measures: (1) polytomous, (2) dichotomous emphasizing exaggerating endorsement of socially desirable behaviors, and (3) dichotomous emphasizing exaggerating denial of such behaviors. The results revealed that respondents on average were able to fake good or fake bad and that faking markedly affected score distributions, subscale score intercorrelations, and overall model fits. When using the Impression Management scale, polytomous and dichotomous exaggerated endorsement scoring were best for detecting faking good, whereas polytomous and dichotomous exaggerated denial scoring were best for detecting faking bad. When using the Self-Deceptive Enhancement scale, polytomous and dichotomous exaggerated endorsement scoring again were best for detecting faking good, but dichotomous exaggerated denial scoring was best for detecting faking bad. Percentages of correct classification of honest and faked responses for the most effective methods for any given scale ranged from 85% to 93%, with accuracy on average in detecting faking bad greater than in detecting faking good and greater when using the Impression Management than using the Self-Deceptive Enhancement scale for both types of faking. Overall, these results best support polytomous scoring of the BIDR Impression Management scale as the single most practical and efficient means to detect faking. Cut scores that maximized classification accuracy for all scales and scoring methods are provided for future use in screening for possible faking within situations in which relevant local data are unavailable.

Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records.

Lung cancer remains a significant public health concern, accounting for a considerable number of cancer-related deaths worldwide. Neural networks have emerged as a promising tool that can aid in the diagnosis and treatment of various cancers. Consequently, there has been a growing interest in exploring the potential of artificial intelligence (AI) methods in medicine. The present study aimed to evaluate the effectiveness of a neural network in predicting lung cancer recurrence. The study employed retrospective data from 2,296 medical records of patients diagnosed with lung cancer and admitted to the Warmińsko-Mazurskie Center for Lung Diseases in Olsztyn, Poland. The statistical software STATISTICA 7.1, equipped with the Neural Networks module (StatSoft Inc., Tulsa, USA), was utilized to analyze the data. The neural network model was trained using patient information regarding gender, treatment, smoking status, family history, and symptoms of cancer. The study employed a multilayer perceptron neural network with a two-phase learning process. The network demonstrated high predictive ability, as indicated by the percentage of correct classifications, which amounted to 87.5%, 89.1%, and 89.9% for the training, validation, and test sets, respectively. The findings of this study support the potential usefulness of a neural network-based predictive model in assessing the risk of lung cancer recurrence. Further research is warranted to validate these findings and to explore AI's broader implications in cancer diagnosis and treatment.

Wing morphometrics of biting midges (Diptera: Culicoides) of veterinary importance in Madagascar.

Biting midges are vectors of arboviruses such as bluetongue virus, bovine ephemeral fever virus, Akabane virus, African horse sickness virus, epizootic haemorrhagic disease virus and Schmallenberg virus. Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. Morphological identification of biting midges has revealed the presence of cryptic species. A total of 20 species are reported in Madagascar. In this study, we assessed wing morphometric analysis for identification of seven species namely C. dubitatus Kremer, Rebholtz-Hirtzel and Delécolle, C. enderleini Cornet and Brunhes, C. kibatiensis Goetghebuer, C. miombo Meiswinkel, C. moreli Clastrier, C. nevilli Cornet and Brunhes, and C. zuluensis de Meillon. Culicoides enderleini, C. miombo, C. moreli, C. nevilli and C. zuluensis are vectors diseases. A molecular approach, based on the cytochrome oxidase I gene (Cox1), was used for species delimitation. The molecular analysis presented seven different clades grouped two-by-two according to morphological characters. A total of 179 wing images were digitised. We found morphometric variation among seven species based on 11 landmarks and two outlines. Wing shape variation plots showed that species overlapped with species belonging to the same group. The cross-validation revealed a relatively high percentage of correct classification in most species, ranging from 91.3% to 100% for landmarks; 60% to 82.6% for outlines-1 and 77.1% to 91.3% for outlines-2. Our study suggests that wing geometric morphometric analysis is a robust tool for reliable "Moka Fohy" identification in Madagascar. This inexpensive and simple method is a precise supplement to morphological identification, with reaches the accuracy of Cox1 barcoding.

Detection of Mycobacterium tuberculosis complex field infections in cattle using fecal volatile organic compound analysis through gas chromatography-ion mobility spectrometry combined with chemometrics.

Bovine tuberculosis is considered a re-emerging disease caused by different species from the Mycobacterium tuberculosis complex (MTC), important not only for the livestock sector but also for public health due to its zoonotic character. Despite the numerous efforts that have been carried out to improve the performance of the current antemortem diagnostic procedures, nowadays, they still pose several drawbacks, such as moderate to low sensitivity, highlighting the necessity to develop alternative and innovative tools to complement control and surveillance frameworks. Volatilome analysis is considered an innovative approach which has been widely employed in animal science, including animal health field and diagnosis, due to the useful and interesting information provided by volatile metabolites. Therefore, this study assesses the potential of gas chromatography coupled to ion mobility spectrometry (GC-IMS) to discriminate cattle naturally infected (field infections) by MTC from non-infected animals. Volatile organic compounds (VOCs) produced from feces were analyzed, employing the subsequent information through chemometrics. After the evaluation of variable importance for the projection of compounds, the final discriminant models achieved a robust performance in cross-validation, as well as high percentages of correct classification (>90%) and optimal data of sensitivity (91.66%) and specificity (99.99%) in external validation. The tentative identification of some VOCs revealed some coincidences with previous studies, although potential new compounds associated with the discrimination of infected and non-infected subjects were also addressed. These results provide strong evidence that a volatilome analysis of feces through GC-IMS coupled to chemometrics could become a valuable methodology to discriminate the infection by MTC in cattle. IMPORTANCE Bovine tuberculosis is endemic in many countries worldwide and poses important concerns for public health because of their zoonotic condition. However, current diagnostic techniques present several hurdles, such as low sensitivity and complexity, among others. In this regard, the development of new approaches to improve the diagnosis and control of this disease is considered crucial. Volatile organic compounds are small molecular mass metabolites which compose volatilome, whose analysis has been widely employed with success in different areas of animal science including animal health. The present study seeks to evaluate the combination of fecal volatilome analysis with chemometrics to detect field infections by bovine tuberculosis (Mycobacterium tuberculosis complex) in cattle. The good robust performance of discriminant models as well as the optimal data of sensitivity and specificity achieved highlight volatilome analysis as an innovative approach with huge potential.

Dietary adaptations and tooth morphology in squirrels: Insights from extant and extinct species

An important part of the ecological evolution of many lineages consists of adaptations to new diets as climate and available resources changed. In this study, we inferred when squirrels adapted morphologically to new diets by analyzing data from both extant and extinct species. We relied on the relationship between tooth morphology in extant squirrels and their dietary preferences to infer the diets of extinct species. To achieve this, we employed two approaches, namely geometric morphometrics and Elliptic Fourier analyses, to measure the outline of the fourth lower premolar (p4) in occlusal view. We then used both datasets to infer the diets of extinct species. Additionally, we used phylogenetic comparative methods on extant data to explore the evolution of diet and tooth size, identifying potential drivers of squirrel morphological evolution in the context of past climatic changes. Our findings indicate that the morphology of squirrel teeth has been maintained through the evolutionary history of most lineages, but that it also has changed in some lineages as a result of adaptations to particularly demanding diets, sometimes mediated by climatic factors, which have played a crucial role in shaping tooth morphology. Furthermore, we demonstrated that both geometric morphometrics and Elliptic Fourier analyses can yield a similar percentage of correct classifications and congruent dietary inferences. This study provides valuable insights into the macroevolutionary changes in squirrels and provides further evidence of the usefulness of tooth morphology for inferring the diets of extinct mammalian species.

Identification of Risk Factors for Scoliosis in Elementary School Children Using Machine Learning

Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis.

Preliminary Feasibility of Near-Infrared Spectroscopy to Authenticate Grazing in Dairy Goats through Milk and Faeces Analysis.

Consumers are increasingly prone to request information about the production systems of the food they buy. For this purpose, certification and authentication methodologies are necessary not only to protect the choices of consumers, but also to protect producers and production systems. The objective of this preliminary work was to authenticate the grazing system of dairy goats using Near-Infrared Spectroscopy (NIRS) analyses of milk and faeces of the animals. Spectral information and several mathematical pre-treatments were used for the development of six discriminant models based on different algorithms for milk and faeces samples. Results showed that the NIRS spectra of both types of samples had some differences when the two feeding regimes were compared. Therefore, good discrimination rates were obtained with both strategies (faeces and milk samples), with classification percentages of up to 100% effectiveness. Discrimination of feeding regime and grazing authentication based on NIRS analysis of milk samples and an alternative sample such as faeces is considered as a potential approach for dairy goats and small ruminant production.

Electronic periodontal tool integration to enrich clinical diagnosis.

The objective of this study was to evaluate the effectiveness of an Electronic-Periodontal-Diagnosis-Tool (EPDT) to facilitate the formulation of a correct periodontal diagnosis and analyze students' perceptions of the use of the EPDT. Fifty Year-3 students who recently started their clinical training, were randomly assigned to two groups. Two clinical scenarios involving challenging periodontal diagnoses, each one with unique components, variables, and categories were distributed with specific instructions. The cases were analyzed to determine the correct periodontal diagnosis-half without the use of the EPDT and half with the use of the EPDT. A post-exercise discussion conducted by the faculty explained the answer rationales. The students completed an anonymous/voluntary survey to evaluate their perceptions. Statistical analysis using likelihood ratio chi-square tests and a generalized linear model evaluated whether the use of the EPDT resulted in higher percentages of correct diagnoses. EPDT use resulted in a three times higher percentage of correct classifications than no tool use (48% versus 16%), which the investigators considered an important effect of the EPDT. The generalized-linear-model assessment confirmed that EPDT resulted in better classifications (p<0.0001). The feedback about the perceptions of the EPDT was favorable. Students using the EPDT resulted in higher percentages of correct diagnoses. The EPDT provided students with a useful framework to determine the correct periodontal diagnoses, which is essential in providing appropriate treatments.