COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier\'s performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier\'s performance in COVID-19 detection.

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

OBJECTIVE: The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values.
METHODS: We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability.
RESULTS: The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others.
CONCLUSIONS: The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.

UNASSIGNED: Two-dimensional cephalometric image analysis plays a crucial role in orthodontic diagnosis and treatment planning. While deep learning-based algorithms have emerged to automate the laborious task of anatomical landmark annotation, their effectiveness is hampered by the challenges of acquiring and labelling clinical data. In this study, we propose a model that leverages conventional machine learning techniques to enhance the accuracy of landmark detection using limited dataset.
UNASSIGNED: Our methodology involves coarse localization through region of interest (ROI) extraction and fine localization utilizing histogram-oriented gradient (HOG) feature. The image patch containing landmark pixels is classified using the light gradient boosting machine (LGBM) algorithm. To evaluate our model\'s performance, we conducted rigorous tests on the ISBI Cephalometric dataset and Dental Cepha dataset, aiming to achieve accuracy within a 2 mm radial precision range. We also employed cross-validation to assess our approach, providing a robust evaluation.
UNASSIGNED: Our model\'s performance on the ISBI Cephalometric dataset showed an accuracy rate of 77.11% within the desired 2 mm radial precision range. The cross-validation results further confirmed the effectiveness of our approach, yielding a mean accuracy of 78.17%. Additionally, we applied our model to the Dental Cepha dataset, where we achieved a remarkable landmark detection accuracy of 84%.
UNASSIGNED: The results demonstrate that traditional machine learning techniques can be effective for accurate landmark detection in cephalometric images, even with limited data. Our findings highlight the potential of these techniques for clinical applications, where large datasets of labelled images may not be available.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient\'s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

Mitogen-activated protein kinase (MAPK) pathways regulate multiple cellular behaviors, including the response to stress and cell differentiation, and are highly conserved across eukaryotes. MAPK pathways can be activated by the interaction between the small GTPase Cdc42p and the p21-activated kinase (Ste20p in yeast). By studying MAPK pathway regulation in yeast, we recently found that the active conformation of Cdc42p is regulated by turnover, which impacts the activity of the pathway that regulates filamentous growth (fMAPK). Here, we show that Ste20p is regulated in a similar manner and is turned over by the 26S proteasome. This turnover did not occur when Ste20p was bound to Cdc42p, which presumably stabilized the protein to sustain MAPK pathway signaling. Although Ste20p is a major component of the fMAPK pathway, genetic approaches here identified a Ste20p-independent branch of signaling. Ste20p-independent signaling partially required the fMAPK pathway scaffold and Cdc42p-interacting protein, Bem4p, while Ste20p-dependent signaling required the 14-3-3 proteins, Bmh1p and Bmh2p. Interestingly, Ste20p-independent signaling was inhibited by one of the GTPase-activating proteins for Cdc42p, Rga1p, which unexpectedly dampened basal but not active fMAPK pathway activity. These new regulatory features of the Rho GTPase and p21-activated kinase module may extend to related pathways in other systems.

Cephalopods are an essential component of marine ecosystems, which are of great significance for the development of marine resources, ecological balance, and human food supply. At the same time, the preservation of cephalopod resources and the promotion of sustainable utilization also require attention. Many studies on the classification of cephalopods focus on the analysis of their beaks. In this study, we propose a feature fusion-based method for the identification of beaks, which uses the convolutional neural network (CNN) model as its basic architecture and a multi-class support vector machine (SVM) for classification. First, two local shallow features are extracted, namely the histogram of the orientation gradient (HOG) and the local binary pattern (LBP), and classified using SVM. Second, multiple CNN models were used for end-to-end learning to identify the beaks, and model performance was compared. Finally, the global deep features of beaks were extracted from the Resnet50 model, fused with the two local shallow features, and classified using SVM. The experimental results demonstrate that the feature fusion model can effectively fuse multiple features to recognize beaks and improve classification accuracy. Among them, the HOG+Resnet50 method has the highest accuracy in recognizing the upper and lower beaks, with 91.88% and 93.63%, respectively. Therefore, this new approach facilitated identification studies of cephalopod beaks.

Detecting neurological abnormalities such as brain tumors and Alzheimer\'s disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

Alzheimer\'s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world\'s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

The ecology of infectious diseases involves wildlife, yet the wildlife interface is often neglected and understudied. Pathogens related to infectious diseases are often maintained within wildlife populations and can spread to livestock and humans. In this study, we explored the fecal microbiome of coyotes and wild hogs in the Texas panhandle using polymerase chain reactions and 16S sequencing methods. The fecal microbiota of coyotes was dominated by members of the phyla Bacteroidetes, Firmicutes, and Proteobacteria. At the genus taxonomic level, Odoribacter, Allobaculum, Coprobacillus, and Alloprevotella were the dominant genera of the core fecal microbiota of coyotes. While for wild hogs, the fecal microbiota was dominated by bacterial members of the phyla Bacteroidetes, Spirochaetes, Firmicutes, and Proteobacteria. Five genera, Treponema, Prevotella, Alloprevotella, Vampirovibrio, and Sphaerochaeta, constitute the most abundant genera of the core microbiota of wild hogs in this study. Functional profile of the microbiota of coyotes and wild hogs identified 13 and 17 human-related diseases that were statistically associated with the fecal microbiota, respectively (p < 0.05). Our study is a unique investigation of the microbiota using free-living wildlife in the Texas Panhandle and contributes to awareness of the role played by gastrointestinal microbiota of wild canids and hogs in infectious disease reservoir and transmission risk. This report will contribute to the lacking information on coyote and wild hog microbial communities by providing insights into their composition and ecology which may likely be different from those of captive species or domesticated animals. This study will contribute to baseline knowledge for future studies on wildlife gut microbiomes.