Segmentation Classification Research Articles

Analyzing Consumer Shopping Interest via Social Media Ads with K-Means and C4.5 Algorithm

It is increasingly important to understand how advertisements affect consumers' propensity to shop as social media becomes the primary medium for advertising. This study uses the C4.5 algorithm for classification and K-Means Clustering for data segmentation to examine the level of consumer shopping interest driven by Facebook and Instagram ads. This strategy utilizes information collected from user interactions with ads on these two social media platforms to determine consumer interest trends more precisely. The research findings show that, compared to conventional methods, this combination of techniques can increase the accuracy of predicting consumer purchase intention by as much as 85%. These results not only validate the usefulness of clustering and classification methods in digital advertising data analysis, but also offer insights that companies can apply to optimize their marketing strategies. By understanding more specific consumer segments, companies can target their ads more precisely, thereby increasing conversions and the effectiveness of advertising campaigns. This research makes a significant contribution to the field of data analysis and digital marketing and opens up opportunities for further research in the integration of more sophisticated analysis methods

Chasing the authoritarian spectre: Detecting authoritarian discourse with large language models

AbstractThe paper introduces a deep‐learning model fine‐tuned for detecting authoritarian discourse in political speeches. Set up as a regression problem with weak supervision logic, the model is trained for the task of classification of segments of text for being/not being associated with authoritarian discourse. Rather than trying to define what an authoritarian discourse is, the model builds on the assumption that authoritarian leaders inherently define it. In other words, authoritarian leaders talk like authoritarians. When combined with the discourse defined by democratic leaders, the model learns the instances that are more often associated with authoritarians on the one hand and democrats on the other. The paper discusses several evaluation tests using the model and advocates for its usefulness in a broad range of research problems. It presents a new methodology for studying latent political concepts and positions as an alternative to more traditional research strategies.

Radiofrequency Ablation Therapy versus Stereotactic Body Radiation Therapy for Naive Hepatocellular Carcinoma (≤5cm): A Retrospective Multi-Center Study.

Radiofrequency ablation (RFA) is a micro-invasive treatment for early-stage HCC patients. Stereotactic body radiation therapy (SBRT) has also been proven an effective and safe treatment for HCC patients. This multi-center study is to compare the efficacy of computed tomography (CT)-guided RFA and CT-based SBRT in naïve HCC patients with tumor diameters ≤5 cm. This retrospective cohort study included 1001 treatment-naïve HCC patients from three hospitals or medical centers. The patients received RFA (n = 481) or SBRT (n = 520) treatment between December 2011 and May 2019. Furthermore, subgroup analyses of all patients were conducted based on Couinaud's classification of liver segments. After matching, the local control (LC) rates of the SBRT group were better than those of the RFA group (p=0.024*), which mainly referred to the patients whose tumors were located in the S7/S8 (p=0.006*). Among patients with tumors located in S1, nineteen patients (19/21) underwent SBRT. The 1-, 3- and 5-year LC rates were 100%, 87.8% and 87.8% in the SBRT group, and the 1-, 3- and 5-year OS rates were 100%, 69.8% and 69.8%, respectively. Moreover, the OS rates in S5/S6 group in RFA were higher than those in SBRT group. The LC rates were better in the SBRT group than in the RFA group for the patients with lesions localized in S7/S8, and SBRT could also be a therapeutic option for patients with lesions in S1. Moreover, patients with tumors located in S5/S6 were better candidates for RFA treatment than SBRT.

The Validity and Reliability of CT Scan Based Tibial Plateau Mapping for Tibial Plateau Fractures

Abstract Background Tibial plateau fractures are intra-articular fractures presume a threat to knee function and represent great challenge to treat. Treatment with open reduction and internal fixation is variable because fractures vary from simple to complex, with little or extensive articular involvement. Hence, recognition of the fracture features will help orthopedic surgeons to understand the injury mechanism better and manage these fractures by planning optimal surgical procedures. Aim of the Work To assess the inter and intra-observer reliability of the new CT scan based tibial plateau classification (the ten segment tibial plateau mapping) and the secondary aim is to clarify its validity in clinical practice assisting the surgeons in choosing their approach and fixation implant during open reduction and internal fixation of such fractures. Our hypothesis is that this more sophisticated classification system may facilitate the choice of surgical approach and implants used and that this classification could be widely accepted among trauma surgeons. Patients and Methods A total of 30 patients with tibial plateau fractures diagnosed by the department of orthopedic surgery at el Demerdash hospital, Ain Shams university were collected in the time period between January 2020 and December 2022. Results in our study, we compared the inter and intra observer reliability of Schatzker, three column, and ten-segment classification using Kappa values. The results showed that schatzker and three column classification systems have higher inter-observer mean kappa values (0.749± 0.07, 0.692± 0.08) (0.639± 0.06, 0.667± 0.08 after 2 weeks‟ time interval) respectively representing good interobserver agreement in relation to the ten-segment classification which showed mean Kappa value 0.409± 0.30 representing moderate interobserver agreement when the cases were observed for the first time. The mean Kappa value decreased (0.081± 0.17) when the cases were observed after 2 weeks‟ time interval representing poor interobserver agreement. As regard to intra-observer reliability, both schatzker and three column classifications have higher mean Kappa values (0.766± 0.22, 0.697± 0.17 respectively) representing good intra-observer agreement in relation to ten segment classification which showed mean Kappa value (0.342± 0.10) representing poor intra-observer agreement. Conclusion tibial plateau fractures represent great challenge for orthopedic surgeons to treat and adequate preoperative planning is mandatory. The use CT scan with 3D reconstruction can help surgeons to describe and classify all fracture patterns thus reaching the optimal plane for fracture fixation. Our study concluded that ten segment classification has moderate to poor reliability compared to the conventional widely used schatzker classification and three column classification. We think that this because ten segment classification still not familiar to orthopedic surgeon. More research is needed on ten segment classification so that it can be applied in the future in clinical practice.

Long-term tracking for high-frequency surface wave radar based on heading constraint filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

Differential public support for disposable plastic bag charge in Singapore: Insights from latent class segmentation

Disposable plastic bag charge is a proposed strategy to mitigate their use, but it may significantly impact daily life due to their frequent use. Understanding the segmentation of public support for charge is crucial for informed policymaking. This study conducted a questionnaire survey among a Singaporean population, collecting data from 1953 respondents on their demographic profiles as well as their behaviour, attitudes, and willingness to pay for disposable plastic bags. The survey data suggest a supportive environment in Singapore for introducing charges on disposable plastic bags with most respondents preferred a charge of SGD 0.10 or below. By employing Cramér's V test and latent class analysis, we have affirmed the existence of differential policy support and willingness-to-pay, identifying five distinct classes of respondents: Non-supporters, Ideal supporters, Cost-sensitive supporters, Moderate Pricing Supporters, and High Pricing Supporters, with some classes showing distinct profiles in terms of age, education level, and monthly income. Further analysis of the effects of imposed charges revealed that a charge that is too low appears to have a limited impact on reducing the overall usage of disposable plastic bags, whereas a charge that is too high is likely to yield diminishing returns and could potentially lead to an increase in public complaints. These results could provide crucial policy implications for addressing plastic issues and highlight that charging for disposable plastic bags is not just an end goal but a means to promote reduced consumption.

Multi-spectral image transformer descriptor classification combined with molecular tools for early detection of tomato grey mould

Accurate early detection of plant diseases is a critical milestone in the development of self-navigating robots for precision agriculture and still remains one of the most significant challenges. Plant pathogens, like Botrytis cinerea causing grey mould disease, pose significant threats to agriculture and food safety. This study focuses on the early detection of B. cinerea in tomato crops using Artificial Intelligence. Specifically, the Deep Learning (DL) YOLOv8 architecture was employed to apply single class segmentation on plant captures to extract the tomato leaves locational information. Each plant capture includes five images from hyperspectral wavelengths (at 460, 540, 640, 775 and 875 nm) and one RGB images. The leaf segmentation achieved 81.7 % Mean Average Precision (mAP) at Intersection over Union (IoU) threshold 0.5 (mAP50). Then the for each leaf segment, and array of descriptors is extracted through various Transformer models. Finally, the descriptors are classified through a KNN or an LSTM solution and the results of all the descriptors for each leaf from each of the Transformer models, for each of the six images of the capture, are ensembled to yield the class of each leaf. The best ensemble results were produced by only accumulating results from some of the Transformer and specifically the MaxViT-L, the ViT-B (P:16 × 16 – C), the VOLO (D:5) and the XCIT-L (L:24 – P:16 × 16), by only using the results provided by the images at the hyperspectral wavelengths at 540, 640 nm and the RGB image, and by applying the LSTM solution. The process achieved a 79.41 % classification accuracy and 71.12 % F1-Score for all classes. Additionally, early, rapid and accurate detection of grey mould at the early stages of the infection or at latent infections when symptoms are not visible was assessed by comparative qPCR-based methods (qRT-PCR) for fungal biomass estimation in plant tissues and the rest of this study's methodology. The findings of this study represent a significant advancement in crop disease detection and management, and highlight the potential for integrating these methods into on-site digital systems (robots, mobile apps, etc.) under real-world settings. The results, demonstrate the effectiveness of combining segmentation and transformer-based classification models for early and accurate detection of grey mould disease.

Low CD8+ Density Variation and R1 Surgical Margin as Independent Predictors of Early Post-Resection Recurrence in HCC Patients Meeting Milan Criteria.

Our study included 41 patients fulfilling the Milan criteria preoperatively and aimed to identify individuals at high risk of post-resection HCC relapse, which occurred in 18 out of 41 patients (43.9%), retrospectively. We analyzed whole slide images of CD8 immunohistochemistry with automated segmentation of tissue classes and detection of CD8+ lymphocytes. The image analysis outputs were subsampled using a hexagonal grid-based method to assess spatial distribution of CD8+ lymphocytes with regards to the epithelial edges. The CD8+ lymphocyte density indicators, along with clinical, radiological, post-surgical and pathological variables, were tested to predict HCC relapse. Low standard deviation of CD8+ density along the tumor edge and R1 resection emerged as independent predictors of shorter recurrence-free survival (RFS). In particular, patients presenting with both adverse predictors exhibited 100% risk of relapse within 200 days. Our results highlight the potential utility of integrating CD8+ density variability and surgical margin to identify a high relapse-risk group among Milan criteria-fulfilling HCC patients. Validation in cohorts with core biopsy could provide CD8+ distribution data preoperatively and guide preoperative decisions, potentially prioritizing liver transplantation for patients at risk of incomplete resection (R1) and thereby improving overall treatment outcomes significantly.

Developing Relational Pedagogical Techniques in Comparative Christian–Muslim Courses

ABSTRACTThe article acknowledges the limits of the categories of similarity and difference in undergraduate comparative religion courses. To challenge these limitations, including the potential for dualistic or “us/them” thinking, several pedagogical attempts to increase relationality in Christian–Muslim courses are explored. Relational methods include emphasizing living and public interreligious conversations during class segments on scriptural figures as well as humanizing perpetrators of religiously motivated violence.

The integrated methodology for assessing the safety of the road network infrastructure in Romania.

Abstract Romania has the highest road fatality rate in the EU and the lowest perception of road quality. This study presents the implementation of a preliminary procedure and tools provided by the European Commission for the safety assessment of the DN2-E85 road infrastructure, which covers 472.23 km. This road was selected due to the high number of road deaths between 2017 and 2022. The research focuses on the application of both proactive and reactive methodologies as required by Directive (EU) 2019/1936, which requires full road network safety assessments by 2024, with subsequent assessments every five years. The proactive methodology assesses the built-in road safety of the road infrastructure based on design parameters, while the reactive methodology is based on historical data on accidents and traffic flows. The integration of these methodologies allows the classification of road segments into five risk levels. Preliminary results indicate that approximately 59% of the length of the DN2-E85 road is classified as “very high priority”, highlighting significant safety deficiencies, which require actions to immediately improve traffic safety. This study highlights the need for systematic road infrastructure safety assessments and prioritisation of high-risk segments to reduce road fatalities and improve overall road safety. The findings serve as crucial information for road safety auditors and the Romanian Road Authority - ARR, supporting the identification of high-risk areas and the allocation of resources to improve safety. In addition, this research provides a basic basis for future adaptations of road infrastructure safety assessment guidelines, considering the specific characteristics of national and county roads. By aligning with EU directives and national standards, the study aims to increase road safety and reduce the incidence of serious accidents in Romania. The insights gained from this research are essential for developing effective road safety strategies and implementing remedial measures that address the most dangerous segments of the road network.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network

Background and Objective:Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body. Methods:This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task. The final segmentation results are obtained through two distinct approaches. In the first approach, the coarsely generated superpixels are subjected to classification using Random Forest (RF) and Support Vector Machine (SVM) classifiers to classify whether each superpixel belongs to a tendon class or not (resulting in tendon segmentation). In the second approach, the arrangements of superpixels are converted to graphs instead of being treated as conventional image grids. This classification process uses a graph-based convolutional network (GCN) to determine whether each superpixel corresponds to a tendon class or not. Results:All experiments are conducted on a custom-made ankle MRI dataset. The dataset comprises 76 subjects and is divided into two sets: one for training (Dataset 1, trained and evaluated using leave-one-group-out cross-validation) and the other as unseen test data (Dataset 2). Using our first approach, the final test AUC (Area Under the ROC Curve) scores using RF and SVM classifiers on the test data (Dataset 2) are 0.992 and 0.987, respectively, with sensitivities of 0.904 and 0.966. On the other hand, using our second approach (GCN-based node classification), the AUC score for the test set is 0.933 with a sensitivity of 0.899. Conclusions:Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.

BCNet: Bronchus Classification via Structure Guided Representation Learning.

CT-based bronchial tree analysis is a key step for the diagnosis of lung and airway diseases. However, the topology of bronchial trees varies across individuals, which presents a challenge to the automatic bronchus classification. To solve this issue, we propose the Bronchus Classification Network (BCNet), a structure-guided framework that exploits the segment-level topological information using point clouds to learn the voxel-level features. BCNet has two branches, a Point-Voxel Graph Neural Network (PV-GNN) for segment classification, and a Convolutional Neural Network (CNN) for voxel labeling. The two branches are simultaneously trained to learn topology-aware features for their shared backbone while it is feasible to run only the CNN branch for the inference. Therefore, BCNet maintains the same inference efficiency as its CNN baseline. Experimental results show that BCNet significantly exceeds the state-of-the-art methods by over 8.0% both on F1-score for classifying bronchus. Furthermore, we contribute BronAtlas: an open-access benchmark of bronchus imaging analysis with high-quality voxel-wise annotations of both anatomical and abnormal bronchial segments. The benchmark is available at link1.

Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG

Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.

DIGITALIZATION OF IRRIGATION SYSTEMS FROM WATER MANAGEMENT MAPS

With the increasing intensity of evapotranspiration caused by the changing climate, there is a growing need for water. This is especially true in locations where water-intensive vegetables are grown in intensive agriculture. Historically, irrigation systems were built in many intensive agriculture areas in Czechia, but they fell out of use, and evidence of their location was lost. However, Water Management Maps, which were only issued in paper form and have never been fully digitized, can provide evidence about the location of these large-scale irrigation systems. In this paper, we present a method for digitizing irrigation systems using the segmentation and classification of individual segments in the ArcGIS environment. The resulting raster is converted to polygons and is blended with the Land Parcel Identification System layer, resulting in a layer of irrigated land. Two statistical analyses were performed on this layer: statistics of the areas corresponding to the individual source watercourses, and statistics of the type of source.

School choice and parenting strategies among Czech middle‐class parents: Implications for social segregation in public schools

AbstractResearch examining middle‐class parents' school choices often overlooks parents in post‐socialist nations, where social reproduction may occur differently than in Western urban contexts. To bridge this gap, our study illuminates the intersection of Czech middle‐class parents' school choices and parenting strategies. Drawing on 26 in‐depth interviews with parents, we depict “cultivation for wellbeing” as a distinct parenting approach prevalent within a subset of the Czech middle class that challenges prevailing Western depictions of middle‐class child‐rearing strategies in relation to school choice. We describe five areas in which the parenting approach contrasts with concerted cultivation and intensive parenting: the promotion of unstructured free time, autonomous socialization with selected peers, a propensity to avoid confronting teachers, gentle support for children's interests, and a focus on emotional and physical wellbeing. We posit that “cultivation for wellbeing” serves as a mechanism for social distinction among a segment of middle‐class parents, enabling them to distinguish themselves from other class segments by selecting and seamlessly accessing exclusive public school settings tailored to their nuanced educational preferences. The study underscores the need to reassess the dominant portrayals of middle‐class parenting strategies, highlighting the complex interplay between child‐rearing, school choice, and social reproduction in diverse cultural contexts.

Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke.

To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction, which is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction, using multisite datasets involving 8,421 patients with acute ischemic stroke. We included 8,421 stroke patients from 9 centers in Korea. 2D UNet and SE-Unet models were trained using 2,408 FLAIR MRI from 3 hospitals and validated using 6,013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating DSC, correlation coefficient, and concordance correlation coefficient compared to a human-segmented gold standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence. In the training dataset, the mean age was 67.4±13.0 years and 60.4% were men. The mean (95% CI) DSCs for Unet in internal testing and external validation were respectively 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC=0.744; 95% CI=0.738-0.751; P=.031). Compared with the Unet, the SE-Unet demonstrated better performance, achieving a mean DSC of 0.675 (0.666-0.685; P<.001) in the internal testing and 0.722 (0.719-0.726; P<.001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation datasets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the Unet (r=0.917, P<.0001) and even stronger for the SE-Unet (r=0.933, P<.0001). The SE-Unet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in external test datasets. In addition, the uncertainty indices in the majority of patients (86%) in the external datasets were below 0.35, with an average DSC of 0.744 in these patients. We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI datasets. In addition, we showed that the uncertainty index can be used to identify cases where automatic WMH segmentation is less accurate and requires human review. WMH = white matter hyperintensity; CNN = convolutional neural networks; SE = squeeze-and-excitation; KL = Kullback-Leibler; ReLU = rectified linear unit; LKW = last known well; mRS = modified Rankin Scale; NIHSS = National Institute of Health Stroke Scale; LAA = large artery atherosclerosis; SVO = small vessel occlusion; CE = cardioembolism.

Polygenic TB control and the sequence of innate/adaptive immune responses to infection: MHC-II alleles determine the size of the S100A8/9-producing neutrophil population.

Among several quantitative trait loci involved in tuberculosis (TB) control in mice, one was mapped within the chromosome 17 segment occupied by the H2 complex and another within the chromosome 3 segment comprising the S100A8/9 genes, which encode neutrophil inflammatory factor S100A8/9. Previously, we developed a panel of H2-congenic mouse strains differing by small segments of the major histocompatibility complex Class II (MHC-II) region from TB-susceptible H2j mice transferred onto the genetic background of the TB-resistant C57BL/6 (H2b) strain. Susceptible B6.I-9.3 mice differ from B6 progenitors by the alleles of their only classical MHC-II H2-Aβ gene. The goals of the present study were to: (i) comprehensively characterise the differences in TB-related phenotypes between mice of the two strains and (ii) decipher interactions between the H2-Aβ and S100A8/9 genes. Here, we describe the dynamics of TB-related phenotypes differentiating B6.I-9.3 and B6 mice (colony forming unitscounts, histopathology, lung immune cell infiltration and cytokine profiles). We show that disproportionally diminished CD4+ T-cell population, an enlarged S100A8/9-positive neutrophil population and higher S100A8/9 serum levels in B6.I-9.3 mice collectively form the 'susceptible' phenotype before infection. An increase in IL-17 and a decrease in intrferon-gamma production by CD4+ T-cells in these mice provide a mechanistic explanation of this phenotype. Using F2 segregation analysis, we show that the number of S100A8/9-producing neutrophils in lungs and spleens and the proportion of Th17 CD4+ T-cells in lungs are significantly lower in the presence of the MHC-II dominant 'resistant' b allele compared to the recessive 'susceptible' j/j genotype. This provides direct genetic evidence that MHC-II-regulated CD4+ T-cell landscapes determine neutrophil abundance before infection, an important pathogenic factor in TB immunity.

Demystifying Mental Health by Decoding Facial Action Unit Sequences

Mental health is indispensable for effective daily functioning and stress management. Facial expressions may provide vital clues about the mental state of a person as they are universally consistent across cultures. This study intends to detect the emotional variances through facial micro-expressions using facial action units (AUs) to identify probable mental health issues. In addition, convolutional neural networks (CNN) were used to detect and classify the micro-expressions. Further, combinations of AUs were identified for the segmentation of micro-expressions classes using K-means square. Two benchmarked datasets CASME II and SAMM were employed for the training and evaluation of the model. The model achieved an accuracy of 95.62% on CASME II and 93.21% on the SAMM dataset, respectively. Subsequently, a case analysis was done to identify depressive patients using the proposed framework and it attained an accuracy of 92.99%. This experiment revealed the fact that emotions like disgust, sadness, anger, and surprise are the prominent emotions experienced by depressive patients during communication. The findings suggest that leveraging facial action units for micro-expression detection offers a promising approach to mental health diagnostics.

Multimodal risk classifier for cardiorespiratory diseases taking into account concomitant diseases and synergy effect

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. A method for classifying the contours of the boundaries of segments of a complexly structured image is described. The method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. The method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. To classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. The method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Experimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. The purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". The training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. The quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Conclusion. The use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.