Percentage Of Correct Classification Research Articles

An Electronic Nose Analysis of the Headspace from Extra-Virgin Olive Oil–Saliva Interactions and Its Ability to Differentiate Between Individuals Based on Body Mass Index

The interaction between fatty foods and saliva in individuals of different body weights may lead to differences in the release of volatile compounds in the mouth. This study investigates the ability of an electronic nose (E-nose) to discriminate between the headspace profiles of extra-virgin olive oil (EVOO) mixed with the saliva of 55 subjects of different body mass indices (BMI). The resulting data were analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the E-nose’s ability to discriminate between groups. W5S, W1S, W2S, and W2W sensors exhibited the greatest variation in response intensity; in particular, they highlighted differences between obese and non-obese subjects. The LDA plot demonstrated a clear separation of samples corresponding to three BMI groups, with the first and second components accounting for 61.25% and 23.97% of the variance, respectively. Overall, the percentage of correct classification in the cross-validation results was 87.3%. These results highlight the potential of an electronic nose for use as a rapid and objective tool for screening olfactory profiles associated with food matrix–saliva interaction in different BMI groups, providing valuable insight for further research on food–saliva interactions.

Sleep stages classification based on feature extraction from music of brain.

Sleep stages classification one of the essential factors concerning sleep disorder diagnoses, which can contribute to many functional disease treatments or prevent the primary cognitive risks in daily activities. In this study, A novel method of mapping EEG signals to music is proposed to classify sleep stages. A total of 4.752 selected 1-min sleep records extracted from the capsleep database are applied as the statistical population for this assessment. In this process, first, the tempo and scale parameters are extracted from the signal according to the rules of music, and next by applying them and changing the dominant frequency of the pre-processed single-channel EEG signal, a sequence of musical notes is produced. A total of 19 features are extracted from the sequence of notes and fed into feature reduction algorithms; the selected features are applied to a two-stage classification structure: 1) the classification of 5 classes (merging S1 and REM-S2-S3-S4-W) is made with an accuracy of 89.5% (Cap sleep database), 85.9% (Sleep-EDF database), 86.5% (Sleep-EDF expanded database), and 2) the classification of 2 classes (S1 vs. REM) is made with an accuracy of 90.1% (Cap sleep database),88.9% (Sleep-EDF database), 90.1% (Sleep-EDF expanded database). The overall percentage of correct classification for 6 sleep stages are 88.13%, 84.3% and 86.1% for those databases, respectively. The other objective of this study is to present a new single-channel EEG sonification method, The classification accuracy obtained is higher or comparable to contemporary methods. This shows the efficiency of our proposed method.

Classification of speech nasality of individuals with cleft lip and palate with distinct ordinal scales.

To investigate whether there is a difference in the classification of speech hypernasality by inexperienced listeners using different ordinal scales; to verify the agreement of the listeners in the analyses when using these scales; and to verify whether the order in which the scales are presented influences the results. Twenty Speech-Language Pathology students classified the degrees of hypernasality of 40 (oral) samples from patients with cleft lip and palate. Ten performed the classifications using a 4-point scale (absent, mild, moderate, and severe) and, after two weeks, using a 3-point scale (absent, slightly hypernasal, and very hypernasal). Other ten students performed the same classifications, but in reverse order. The classifications were made remotely and documented on a form. The average percentage of correct responses by the students, in relation to the gold standard, was significantly higher for the 3-point scale. There was no significant interaction between the order of presentation and the scale for the percentage of correct classifications. The students' agreement with the gold standard assessment was fair (3-point scale) and moderate (4-point scale). The mean percentage of agreement of the intra-rater analyses was significantly higher for the 3-point scale. There was no significant interaction between presentation order and scale for the percentage of intra-rater classifications. The Kappa coefficient index showed more favorable intra-rater agreement for the reduced scale. The reduced scale favored the classification of speech hypernasality by listeners and can be considered an important strategy to favor the initial evaluations of students in Speech Therapy during their training.

Classificação da nasalidade de fala de indivíduos com fissura labiopalatina com escalas ordinais distintas

ABSTRACT Purpose To investigate whether there is a difference in the classification of speech hypernasality by inexperienced listeners using different ordinal scales; to verify the agreement of the listeners in the analyses when using these scales; and to verify whether the order in which the scales are presented influences the results. Methods Twenty Speech-Language Pathology students classified the degrees of hypernasality of 40 (oral) samples from patients with cleft lip and palate. Ten performed the classifications using a 4-point scale (absent, mild, moderate, and severe) and, after two weeks, using a 3-point scale (absent, slightly hypernasal, and very hypernasal). Other ten students performed the same classifications, but in reverse order. The classifications were made remotely and documented on a form. Results The average percentage of correct responses by the students, in relation to the gold standard, was significantly higher for the 3-point scale. There was no significant interaction between the order of presentation and the scale for the percentage of correct classifications. The students' agreement with the gold standard assessment was fair (3-point scale) and moderate (4-point scale). The mean percentage of agreement of the intra-rater analyses was significantly higher for the 3-point scale. There was no significant interaction between presentation order and scale for the percentage of intra-rater classifications. The Kappa coefficient index showed more favorable intra-rater agreement for the reduced scale. Conclusion The reduced scale favored the classification of speech hypernasality by listeners and can be considered an important strategy to favor the initial evaluations of students in Speech Therapy during their training.

Exploring environmental risk in soils: Leveraging open data for non-sampling assessment?

Soil contamination by heavy metals (HM) is a critical area of research. Traditional methods involving sample collection and lab analysis are effective but costly and time-consuming. This study explores whether geostatistical analysis with GIS and open data can provide a faster, more precise, and cost-effective alternative for HM contamination assessment without extensive sampling. Concentrations of nine HMs (Cu, Pb, Ni, Co, Mn, As, Cd, Sb, Cr) were analysed from 498 soil samples collected in two mining areas in Portugal: the Panasqueira and Aljustrel mines. Corresponding data were extracted from the Lucas TOPSOIL 1km raster maps. Several contamination indices, Contamination Factor (Cf), Modified Contamination Degree (mCd), Geoaccumulation Index (Igeo), Nemerow Pollution Index (Pn), Potential Ecological Risk Index (PERI), and Pollution Load Index (PLI) were calculated for both datasets. A confusion matrix was used to evaluate the percentage of correct classifications, while a concordance analysis assessed the alignment of accurately classified points between the two data sources. In the soil samples, very high contamination levels for As were observed in 42% of the samples, according to the Cf, with high levels for Sb found in approximately 30% of the samples. The mCd revealed that approximately 11% of soil samples exhibited very high levels of contamination, while the Pn indicated that 78.9% of the soil samples fell within the seriously polluted domain. Similar contamination trends were observed for the other indices. In contrast, the results for the LUCAS points showed significant discrepancies. No high contamination levels were found for any metal. The misclassification rates for mCd, Pn, PERI, and PLI were 84.25%, 97.55%, 95%, and 82%, respectively, when compared to the field data. This study concludes that while open data raster maps offer rapid overviews, they fall short of providing the detailed precision required for reliable contamination assessments. The significant misclassification rates observed highlight the limitations of relying solely on these tools for critical environmental decisions. Consequently, traditional sampling and laboratory analysis remain indispensable for accurate risk assessments of HM contamination, ensuring a more reliable foundation for decision-making and environmental management.

Relación entre consumo de riesgo de alcohol y síntomas depresivos en estudiantes universitarios de México

Introduction: alcohol is a legal drug in most parts of the world, it is mostly accepted among different cultures. Therefore, alcohol consumption typically does not carry the same stigma associated with the use of other drugs, such as marijuana or methamphetamines. Previous research in Mexico, the United States, Ecuador, and England has shown a positive and significant association between alcohol consumption and depressive symptoms among university students. Objective: to determine the magnitude of the relationship between alcohol consumption and depressive symptoms, and whether this relationship is independent of sex, age, and socioeconomic status in first-year university students. Method: the sample consisted of 2,377 students aged 18 to 25 years (M = 18.82, SD = 1.30), 66.5% of whom were women. Results: multiple logistic regression analyses demonstrated a relationship between risk alcohol consumption and depressive symptoms, independent of sex, age, and socioeconomic status (adjusted OR = 1.88 [1.14, 3.10] with a percentage of correct classification of the 67.2% and a R2 Nagelkerke = .07). Discussion and Conclusions: the evidence of the relationship between alcohol consumption and depressive symptoms emphasizes that limiting or reducing alcohol consumption among young people could prevent the onset of depressive symptoms and their consequences.

A Cross-Biomeasure Study to Optimize Antiretroviral Adherence Estimation.

Incomplete adherence to daily tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) reduces effectiveness. Adherence biomeasures (i.e. drug concentrations in biological specimen) are more accurate than self-report. TDF/FTC's intracellular active metabolites (tenofovir-diphosphate; TFVdp and FTC-triphosphate; FTCtp) can be quantified in different types of blood samples to estimate adherence. To optimize adherence estimation, we investigated approaches to measure TFVdp and FTCtp in four blood matrices. Twelve HIV-negative, healthy volunteers were enrolled in a single center, open-label, 3-phase, directly observed therapy study. LC-MS/MS methods quantified TFVdp/FTCtp in dried blood spots, volumetrically accurate microsampling, upper layer packed cells and peripheral blood mononuclear cells (PBMCs). Non-compartmental analysis estimated half-lives and accumulation ratios. Correlations characterized relationships between clinical variables and exposure. Regression models were fit to determine concentrations associated with <4 and ≥4 doses/week; correct classification percentages were determined. Terminal half-life estimates of 3-4 vs 15-22 days distinguished between moderate-term (FTCtp in all samples; TFVdp in PBMCs) versus long-term (TFVdp in red blood cell-containing matrices) measures. Model-derived thresholds accurately categorized <4 and ≥4 doses/week when including both metabolites for 14- and 28-day dosing periods (81-91% and 82-85%, respectively). Within each classification and regression trees analyses containing both moderate- and long-term measures, dried blood spots exhibited highest accuracy to predict stable (74-94%) and changing (42-47%) adherence patterns. We demonstrate higher accuracy of moderate-term biomeasures to classify adherence over a 14-day period compared to long-term biomeasures to classify adherence over a 28-day period. Combined moderate- and long-term biomeasures predicted stable and changing adherence patterns, with dried blood spots exhibiting highest accuracy.

Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

The Gastroscopy RAte of Cleanliness Evaluation (GRACE) Scale: an international reliability and validation study.

Mucosal visualization during upper gastrointestinal (UGI) endoscopy can be impaired by the presence of foam, bubbles, and mucus. Some UGI endoscopy visibility scales have been proposed but have not undergone multicenter validation. This study aimed to develop and validate the Gastroscopy RAte of Cleanliness Evaluation (GRACE) scale. A multicenter, international, cross-sectional study was conducted. The GRACE scale is based on a score from 0 (worst) to 3 (excellent) for esophagus, stomach, and duodenum, for a total ranging from 0 to 9. In phase 1, four expert endoscopists evaluated 60 images twice, with a 2-week interval between rounds; in phase 2, the same 60 images were scored twice by one expert and one nonexpert endoscopist from 27 endoscopy departments worldwide. For reproducibility assessment and real-time validation, the scale was applied to consecutive patients undergoing gastroscopy at each center. On internal validation, interobserver agreement was 0.81 (95 %CI 0.73-0.87) and 0.80 (95 %CI 0.72-0.86), with reliability of 0.73 (95 %CI 0.63-0.82) and 0.72 (95 %CI 0.63-0.81), in the two rounds, respectively. On external validation, overall interobserver agreement was 0.85 (95 %CI 0.82-0.88) and reliability was 0.79 (95 %CI 0.73-0.84). In real-time evaluation, the overall proportion of correct classifications was 0.80 (95 %CI 0.77-0.82). The GRACE scale showed good interobserver agreement, reliability, and validity. The widespread use of this scale could enhance quality and standardize the assessment of mucosal cleanliness during UGI endoscopy, pushing endoscopists to strive for excellent visibility and reducing the risk of missed lesions.

Enhancing internet of things security using entropy-informed RF-DNA fingerprint learning from Gabor-based images

Internet of Things (IoT) deployments are anticipated to reach 29.42 billion by the end of 2030 at an average growth rate of 16% over the next 6 years. These deployments represent an overall growth of 201.4% in operational IoT devices from 2020 to 2030. This growth is alarming because IoT devices have permeated all aspects of our daily lives, and most lack adequate security. IoT-connected systems and infrastructures can be secured using device identification and authentication, two effective identity-based access control mechanisms. Physical Layer Security (PLS) is an alternative or augmentation to cryptographic and other higher-layer security schemes often used for device identification and authentication. PLS does not compromise spectral and energy efficiency or reduce throughput. Specific Emitter Identification (SEI) is a PLS scheme capable of uniquely identifying senders by passively learning emitter-specific features unintentionally imparted on the signals during their formation and transmission by the sender’s radio frequency (RF) front end. This work focuses on image-based SEI because it produces deep learning (DL) models that are less sensitive to external factors and better generalize to different operating conditions. More specifically, this work focuses on reducing the computational cost and memory requirements of image-based SEI with little to no reduction in performance by selecting the most informative portions of each image using entropy. These image portions or tiles reduce memory storage requirements by 92.8% and the DL training time by 81% while achieving an average percent correct classification performance of 91% and higher for SNR values of 15 dB and higher with individual emitter performance no lower than 87.7% at the same SNR. Compared with another state-of-the-art time-frequency (TF)-based SEI approach, our approach results in superior performance for all investigated signal-to-noise ratio conditions, the largest improvement being 21.7% at 9 dB and requires 43% less data.

Use of whistles for acoustic classification of delphinids (odontoceti: Delphinidae) in the Western South Atlantic Ocean.

This study focuses on the acoustic classification of delphinid species at the southern continental slope of Brazil. Recordings were collected between 2013 and 2015 using towed arrays and were processed using a classifier to identify the species in the recordings. Using Raven Pro 1.6 software (Cornell Laboratory of Ornithology, Ithaca, NY), we analyzed whistles for species identification. The random forest algorithm in R facilitates classification analysis based on acoustic parameters, including low, high, delta, center, beginning, and ending frequencies, and duration. Evaluation metrics, such as correct and incorrect classification percentages, global accuracy, balanced accuracy, and p-values, were employed. Receiver operating characteristic curves and area-under-the-curve (AUC) values demonstrated well-fitting models (AUC ≥ 0.7) for species definition. Duration and delta frequency emerged as crucial parameters for classification, as indicated by the decrease in mean accuracy. Multivariate dispersion plots visualized the proximity between acoustic and visual match data and exclusively acoustic encounter (EAE) data. The EAE results classified as Delphinus delphis (n = 6), Stenella frontalis (n = 3), and Stenella longirostris (n = 2) provide valuable insights into the presence of these species between approximately 23° and 34° S in Brazil. This study demonstrates the effectiveness of acousting classification in discriminating delphinids through whistle parameters.

Comparative Study of Statistical Techniques for Preliminary Diagnosis of Cancer Risk

The purpose of the is to elaborate models for preliminary diagnosis using statistical techniques. We compare two models for the estimation of cervical cancer risks.This article aims to compare predictive models for cervical cancer using machine learning techniques. We set up classification tables to compare the overall correct prediction rates.The data used comes from 30 cases of cervical cancer. We fitted a Logistic Regression (LR) model and trained Artificial Neural Networks (ANNs). The multicollinearity problem, usually present in modeling with numerous predictive variables, was addressed with factor analysis and Pearson Correlations.The LR model and ANN model were evaluated based on their percentage of correct classifications. The LR model achieved an accuracy of 33.33%, while the ANN model achieved an accuracy of 16.67%.Based on the percentage of correct classification, the Logistic Regression model was superior to the Neural Networks for the cervical cancer dataset. This highlights the need for further exploration of different machine learning approaches and data preprocessing techniques to improve predictive performance for cervical cancer risks.

Comparison of the predictive validity of the Braden, Munro and 3S scales in surgical patients

Aim of the studyThis research is a descriptive and cross-sectional study aimed at examining the use of three different scales to assess the risk of pressure injury (PI) in surgical patients. Materials and methodsThis study was conducted between February 1 and July 1, 2022. The study included patients who had planned surgery in general surgery clinic. The sample size was 388 patients. Patients who agreed to participate in the study were asked to fill out the " Introductory Information Form " and perform a risk assessment using the Braden, Munro and 3S Scales. The statistics program IBM SPSS Statistics 25.0 packaged software was used in the analyses of data. ResultsIt is evident that the relationship between the scales and the diagnosis of PI, as well as the strength of this relationship, are statistically significant (p < 0.05). The Munro Scale exhibited the highest value of Phi value and validity coefficient c, indicating a stronger association with the diagnosis of PI, thus suggesting its greater effectiveness in discrimination. According to the validity coefficients found, it can be stated that the correct classification percentages for Munro, Braden, and 3S Scales were 91 %, 71 %, and 66 %, respectively. ConclusionThe findings of this study indicate that both the Braden, Munro, and 3S Scales can be used for PI risk assessment in surgical patients, but the Munro Scale exhibits superior predictive validity compared to the Braden and 3S Scales in terms of overall sensitivity and specificity.

Feature Selection Information Gain pada Klasifikasi Pasien Penyakit Jantung (Heart Disease)

Heart disease, also known as cardiovascular disease, is a condition where there is a blockage or narrowing of blood vessels that can lead to heart attack, chest pain, or stroke. It needs appropriate medical treatment because this disease can be the cause of death. Data mining methods are helpful in diagnosing and treating heart disease. Data mining methods can play a major role in the process of improving the quality of care for heart disease patients, providing valuable information for informed decision-making regarding prevention and treatment. The data analysis process uses classification algorithms, namely Decision Tree (C4.5), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) combined with feature selection information gain method. The results show that data mining methods are very useful in diagnosing and treating heart disease. The highest percentage of correct classifications for both models before and after the implementation of feature selection information gain was obtained by the RF algorithm, which amounted to 95.71%. However, the implementation of the feature selection information gain method in this study did not contribute significantly to improving the classification quality of each algorithm used.

Influence of soil type and ripening degree on the intensity and sensory dominance of Mexican habanero chili (Capsicum chinense Jacq.)

AbstractThe objective of this research was to determine the intensity and sensory dominance of habanero peppers grown in different types of soils and with different degrees of maturation. Chili samples grown in red soil (Mayan name: K'ankab lu'um) and black soil (Mayan name: Box lu'um) were evaluated in an immature state (green coloration) and intermediate ripening (green‐orange coloration) using descriptive analysis (DA) and temporal dominance of sensations (TDS). Sensory representation and classification percentage of chili peppers were evaluated using principal component analysis and discriminant factor analysis. The sensory results demonstrated that intermediate ripening chili peppers presented higher color intensities associated with pungency. Immature chili peppers were characterized by having high odor intensities. The TDS results showed that the dominant attributes were burn feeling, chili flavor, heat, and numbness. Immature chili peppers grown in black soil tend to be burn feeling dominant and chili peppers grown in red soil were characterized as chili flavor. The ripe habanero peppers were chili flavor burn feeling and heat. The correct classification percentage was 89.81%.Practical ApplicationsThis research exposes the sensory differences of habanero peppers with different degrees of maturation and grown in different types of soils. The findings can serve as a reference for future research on the sensory characterization of pungent and for the industry dedicated to the production of habanero chili and for the gastronomy area in charge of the preparation of different dishes that use habanero chili as an ingredient.

Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.

The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19. Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization. Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747. C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics.

Predicting the development of vulvar lichen sclerosus

Relevance. The issue of timely diagnosis and treatment of vulvar lichen sclerosus has become especially acute in recent years due to the “rejuvenation” of the disease and the risk of its malignancy. In this regard, it is urgent to search for effective methods for predicting and early detection of the disease. The aim of the study - to develop amodel for predicting vulvar lichen sclerosus based on established clinical and anamnestic risk factors. Materials and Methods. The prospective case-control study included 404 women aged 20 to 70 years, of which 344 were patients with vulvar lichen sclerosus and 60 were women without vulvar diseases. At the first stage, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), Chi-square tests, Phi and Cramer statistics, the Mann-­Whitney U test and the Student t test (p 0.05). The data obtained were used to develop aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-­gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural network model for predicting vulvar lichen sclerosus was developed (the percentage of correct classification on the test sample is the maximum possible value - 100%) and acomputer program was written that automates the procedure for predicting the disease. Conclusion. The neural network model for predicting the disease, developed on the basis of reliably (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter of minutes to identify the patient at risk for the development of vulvar lichen sclerosus and give she needs preventive recommendations aimed at preventing or early detection of the disease.

Geometry of posterior larval spiracles to identify medically and forensically important calliphorids in Thailand

Fly identification is the primary step of analysis in forensic entomology. Although morphology and molecular techniques are considered satisfactory methods, some constraints may arise from a financial or even human point of view. Over the past decade, the geometric morphometric approach has been increasingly advocated for the classification and identification of arthropods. This study explored the method for species identification of 800 third-instar larvae of eight blow fly species of medical and forensic importance: Chrysomya chani Kurahashi, Chrysomya megacephala (Fabricius), Chrysomya (Ceylonomyia) nigripes Aubertin, Chrysomya pinguis (Walker), Chrysomya (Achoetandrus) rufifacies (Macquart), Hemipyrellia ligurriens (Wiedemann), Lucilia cuprina (Wiedemann), and Lucilia porphyrina (Walker). Based on the posterior spiracles geometry, the cross-validation revealed a relatively high percentage of correct classification in most species, ranking from 86% to 100%. The results of this study confirmed that the geometric morphometric (GM) analysis of posterior spiracles might be utilized as a larva identification tool. Therefore, this GM method represents one way of overcoming difficulties with the identification of blow fly larvae and can support further studies of these flies.

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.