Craniometric Variation in a Regional Sample from Antioquia, Medellín, Colombia

Population affinity estimation is frequently assessed from measurements of the cranium. Traditional models place individuals into discrete groups―such as Hispanic―that often encompass very diverse populations. Current research, including this study, challenges these assumptions using more refined population affinity estimation analyses. We examine craniometric data for a sample of individuals from different regions in Antioquia, Colombia. We first assessed the sample to understand intraregional variation in cranial shape as a function of birthplace or a culturally constructed social group label. Then, pooling the Colombian data, we compare cranial variation with global contemporary and prehistoric groups. Results did not indicate significant intraregional variation in Antioquia; classification models performed poorly (28.6% for birthplace and 36.6% for social group). When compared to other groups (American Black, American White, Asian, modern Hispanic, and prehistoric Native American), our model correctly classified 75.5% of the samples. We further refined the model by separating the pooled Hispanic sample into Mexican and Guatemalan samples, which produced a correct classification rate of 74.4%. These results indicate significant differences in cranial form among groups commonly united under the classification “Hispanic” and bolster the addition of a refined approach to population affinity estimation using craniometric data.

The Effect of Artificial Amalgamates on Identifying Pathogenesis

The purpose of this research was to define acceleration in diagnostic procedures for airborne diseases. Airborne pathogenicity can be troublesome to diagnose due to intrinsic variation and overlapping symptoms. Coronavirus testing was an instance of a flawed diagnostic biomarker. The levels of independent variables (IV) were vanilla, sparse, and dense amalgamates formed from multilayer perceptrons and image processing algorithms. The dependent variable (DV) was the classification accuracy. It was hypothesized that if a dense amalgamate is trained to identify Coronavirus, the accuracy would be the highest. The amalgamates were trained to analyze the morphological patches within radiologist-verified medical imaging retrieved from online databanks. Using generative cross-validations, the DV was consulted for each amalgamate. Self-calculated t-tests supported the research hypothesis, with the dense amalgamate achieving 85.37% correct classification rate. The null hypothesis was rejected. Flaws within the databanks were possible sources of error. A new algorithm developed here identified Coronavirus, Mycobacterium, Carcinoma, and Pneumonia from 96-99% accuracy. Future enhancements involve tracking osteopenia/osteoporosis with the algorithm.

The Diagnostic Efficacy of Three-Dimensional Reconstruction System-Based Methods for Predicting Axillary Lymph Node Metastasis in Breast Cancer Patients: An Exploratory Study

Background: The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy.Methods: This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods.Results: A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%).Conclusion: By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

Identifying Gender of Internet Users Based on Access History

The use of activities and internet access differs between men and women. On average, men spend more time on the Internet a day. Men also have some of the same online activities as women. However, there are specific differences such as men’s tendency to access features such as breaking news, football, or games and men’s products. On the contrary, women are more interested in shopping, e-commerce, chatting and participating in social networking sites and blogs. The study aims to identify and predict gender of internet users based on their access history. With SVM method, the correct classification rate is the highest compared to the other two models Accuracy = 87.67%, in addition, the Precision, Recall, and F-Score parameters also give outstanding rates. This result allows us to believe in the ability of the SVM machine learning model to effectively handle the classification and gender identification problem with large-dimensional data.

Geometric morphometrics of mandibles for dietary differentiation of Bovidae (Mammalia: Artiodactyla)

The mammalian family Bovidae has been widely studied in ecomorphological research, with important applications to paleoecological and paleohabitat reconstructions. Most studies of bovid craniomandibular features in relation to diet have used linear measurements. In this study, we conduct landmark-based geometric-morphometric analyses to evaluate whether different dietary groups can be distinguished by mandibular morphology. Our analysis includes data for 100 species of extant bovids, covering all bovid tribes and two dietary classifications. For the first classification with three feeding categories, we found that browsers (including frugivores), mixed feeders, and grazers are moderately well separated using mandibular shape. A finer dietary classification (frugivore, browser, browser-grazer intermediate, generalist, variable grazer, obligate grazer) proved to be more useful for differentiating dietary extremes (frugivores and obligate grazers) but performed equally or less well for other groups. Notably, frugivorous bovids, which belong in tribe Cephalophini, have a distinct mandibular shape that is readily distinguished from all other dietary groups, yielding a 100% correct classification rate from jackknife cross-validation. The main differences in mandibular shape found among dietary groups are related to the functional needs of species during forage prehension and mastication. Compared to browsers, both frugivores and grazers have mandibles that are adapted for higher biomechanical demand of chewing. Additionally, frugivore mandibles are adapted for selective cropping. Our results call for more work on the feeding ecology and functional morphology of frugivores and offer an approach for reconstructing the diet of extinct bovids.

Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

Two-dimensional mid and near infrared correlation spectroscopy for bacterial identification

In recent years, near infrared (NIR) spectroscopy has gained interest as a tool for bacteria strain identification. Although some promising results suggest good applicability of the technique, a better interpretation of the NIR bacterial spectra is still needed. In order to analyze the NIR spectrum of biological samples, a correlation analysis between the NIR and the mid-infrared (mid-IR) spectra was performed. In total, 28 spectra of 8 bacterial strains were acquired and correlated in the NIR and the mid-IR spectral ranges. Some molecular bands (Amide I, P = O stretching, C-H stretching/deformation of polysaccharides) were well correlated, and the effect of concentration changes in these molecules were investigated. Moreover, a model for the NIR spectra classification was created with an overall 85% correct classification rate. Subsequently, only NIR wavelengths with high correlation to important mid-IR peaks were selected. This led to an increase in the correct classification rate to 94%. By correlation between well-established mid-IR peaks and NIR spectra, some relationships in the NIR spectra of biological samples were revealed, which was a step towards better understanding and interpretation of the NIR spectra of biological samples.

Fatty Acid Composition of Cosmetic Argan Oil: Provenience and Authenticity Criteria

In this work, fatty-acid profiles, including trans fatty acids, in combination with chemometric tools, were applied as a determinant of purity (i.e., adulteration) and provenance (i.e., geographical origin) of cosmetic grade argan oil collected from different regions of Morocco in 2017. The fatty acid profiles obtained by gas chromatography (GC) showed that oleic acid (C18:1) is the most abundant fatty acid, followed by linoleic acid (C18:2) and palmitic acid (C16:0). The content of trans-oleic and trans-linoleic isomers was between 0.02% and 0.03%, while trans-linolenic isomers were between 0.06% and 0.09%. Discriminant analysis (DA) and orthogonal projection to latent structure—discriminant analysis (OPLS-DA) were performed to discriminate between argan oils from Essaouira, Taroudant, Tiznit, Chtouka-Aït Baha and Sidi Ifni. The correct classification rate was highest for argan oil from the Chtouka-Aït Baha province (90.0%) and the lowest for oils from the Sidi Ifni province (14.3%), with an overall correct classification rate of 51.6%. Pairwise comparison using OPLS-DA could predictably differentiate (≥0.92) between the geographical regions with the levels of stearic (C18:0) and arachidic (C20:0) fatty acids accounting for most of the variance. This study shows the feasibility of implementing authenticity criteria for argan oils by including limit values for trans-fatty acids and the ability to discern provenance using fatty acid profiling.

Deep learning versus traditional methods for parking lots occupancy classification

<span>Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the same database. On the Pklot database the alexnet and our deep learning network have a close resutls, overcome <br /> the mean method (greater than 95%).</span>