Jaccard Index Research Articles

Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort.

A data-driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above-mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications. We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k-means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity. Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C-reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age-related diabetes and cluster 3 showed highest risk of genetic and obesity-related diabetes. T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity.

Improving Automatic Coronary Stenosis Classification Using a Hybrid Metaheuristic with Diversity Control

This study proposes a novel Hybrid Metaheuristic with explicit diversity control, aimed at finding an optimal feature subset by thoroughly exploring the search space to prevent premature convergence. Background/Objectives: Unlike traditional evolutionary computing techniques, which only consider the best individuals in a population, the proposed strategy also considers the worst individuals under certain conditions. In consequence, feature selection frequencies tend to be more uniform, decreasing the probability of premature convergent results and local-optima solutions. Methods: An image database containing 608 images, evenly balanced between positive and negative coronary stenosis cases, was used for experiments. A total of 473 features, including intensity, texture, and morphological types, were extracted from the image bank. A Support Vector Machine was employed to classify positive and negative stenosis cases, with Accuracy and the Jaccard Coefficient used as performance metrics. Results: The proposed strategy achieved a classification rate of 0.92 for Accuracy and 0.85 for the Jaccard Coefficient, obtaining a subset of 16 features, which represents a discrimination rate of 0.97 from the 473 initial features. Conclusions: The Hybrid Metaheuristic with explicit diversity control improved the classification performance of coronary stenosis cases compared to previous literature. Based on the achieved results, the identified feature subset demonstrates potential for use in clinical practice, particularly in decision-support information systems.

U-Net and Its Variants Based Automatic Tracking of Radial Artery in Ultrasonic Short-Axis Views: A Pilot Study

Background/Objectives: Radial artery tracking (RAT) in the short-axis view is a pivotal step for ultrasound-guided radial artery catheterization (RAC), which is widely employed in various clinical settings. To eliminate disparities and lay the foundations for automated procedures, a pilot study was conducted to explore the feasibility of U-Net and its variants in automatic RAT. Methods: Approved by the institutional ethics committee, patients as potential RAC candidates were enrolled, and the radial arteries were continuously scanned by B-mode ultrasonography. All acquired videos were processed into standardized images, and randomly divided into training, validation, and test sets in an 8:1:1 ratio. Deep learning models, including U-Net and its variants, such as Attention U-Net, UNet++, Res-UNet, TransUNet, and UNeXt, were utilized for automatic RAT. The performance of the deep learning architectures was assessed using loss functions, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). Performance differences were analyzed using the Kruskal–Wallis test. Results: The independent datasets comprised 7233 images extracted from 178 videos of 135 patients (53.3% women; mean age: 41.6 years). Consistent convergence of loss functions between the training and validation sets was achieved for all models except Attention U-Net. Res-UNet emerged as the optimal architecture in terms of DSC and JSC (93.14% and 87.93%), indicating a significant improvement compared to U-Net (91.79% vs. 86.19%, p < 0.05) and Attention U-Net (91.20% vs. 85.02%, p < 0.05). Conclusions: This pilot study validates the feasibility of U-Net and its variants in automatic RAT, highlighting the predominant performance of Res-UNet among the evaluated architectures.

Dadnet: dual-attention detection network for crack segmentation on tomb murals

Many tomb murals have punctate losses, cracks, and craquelure due to underground subsidence and changes in their physical support. Visual non-destructive detection techniques enable rapid assessment of how much tomb murals are affected by cracking, providing suggestions for their conservation. However, tomb murals are typically created by sketching outlines and then colored. Detailed sketches can easily interfere with crack detection work, requiring the use of deep learning network to better learn crack features and improve detection accuracy. At the same time the limited data of tomb mural presents a challenge to build a deep learning network. To address these issues, this paper introduces a novel dual-attention detection network (DADNet) for crack segmentation of tomb murals. In this work, a customized dataset is first constructed by collecting mural images from the Tang Dynasty tombs. Then the ConvNeXt framework serves as the basis for feature extraction, enhancing the process. Lastly, a dual-attention module utilizing neighborhood attention and biaxial attention is employed to accurately identify the crack regions. Neighborhood attention performs a local self-attention operation around the pixel point, addressing the limitations of self-attention. This approach significantly reduces computational demands as the image size increases. Biaxial attention performs attention calculations in the horizontal and vertical directions. This compensates for the limitation of neighborhood attention in capturing global dependencies. Our DADNet outperformed the competing methods, achieving the highest recorded scores of 78.95% for MIoU and 61.05% for the Jaccard index.

Constructing a picture of fatigue in the context of cancer: assessment of construct overlap in common fatigue scales.

Individuals diagnosed with cancer experience multiple inter-related short- and long-term side effects. Chief among such symptomology is cancer-related fatigue (CRF), which, if left unmanaged, can become chronic and result in increased disability and healthcare utilization. A growing number of self-report scales have been developed to measure CRF symptoms based on various theoretical conceptualizations with the aim of promoting targeted assessment and intervention efforts. It may be, however, unwise to assume that the various measures are conceptually similar (i.e., that they assess for the same constructs). Accordingly, we aimed to characterize item content among nine self-report scales, using a Jaccard index to quantify content overlap among scales. We characterized construct assessment among nine self-report scales recommended to assess CRF by a recent clinical practice guideline, and used a Jaccard index to quantify content overlap among scales. Analysis of 208 items across nine rating scales resulted in 20 distinct symptoms of CRF assessed. The most common symptoms were energy level (captured in all nine scales), cognitive function, impaired task performance (in eight scales), sleepiness, and physical function (in seven scales). Mean overlap among all scales was low (Jaccard index = 0.455). Only one construct (duration of fatigue; 5.0%) was captured by a single scale, and one symptom (energy level; 5.0%) was common across all scales. The PFS, MFSI, and BFI each captured at least one symptom from each of the NCCN domains of CRF. CRF scales are heterogeneousin the content they measure, critically impairing integration of knowledge across studies using disparate scales. Future work is urgently needed to build more integrated theoretical and/or computational models of CRF based on relevant mechanisms.

The importance of sharing: wild plant knowledge in three valleys of Northern Pakistan

Mountain communities are often defined by their distinctive flora and fauna, and the way local people utilize these natural resources. Our study aims to document the indigenous knowledge of wild flora among the local people of three valleys in Northern Pakistan. The ethnobotanical study was conducted in July and August of 2019 and 2020 in 21 hill villages across Shamshal, Hopper, and Phandar valleys in Gilgit-Baltistan, Northern Pakistan. Semi-structured interviews were conducted with 90 key informants. We collected data on traditional knowledge of wild food plants (WFPs), wild medicinal plants (WMPs), and wild veterinary plants (WVPs) among three selected linguistic and religious groups scattered in different villages of the valleys. We documented 79 wild plant taxa belonging to 68 genera and 33 families utilized in the study area, among which, 50% were used as food, 43% were used for veterinary purposes, and 24% for medicinal applications. The data analysis revealed significant differences among the studied valleys and communities. The highest overlap was recorded between the Wakhi and Burushaski communities with a Jaccard Similarity Index (JI) value of 0.265, while the lowest was between the Burushaski and Khowar communities with a JI value of 0.238.

Xylotrophic Agaricomycetes Monitoring in Some Types of Spruce and Birch Forests (Subzone of the Southern Taiga, Perm Territory, Russia)

We have summarized the results of xylotrophic agaricoid basidiomycetes long-term monitoring in some types of indigenous and derived forests in the southern taiga subzone of Perm Krai. We carried out the research by a stationary method on the test 50×20 m areas, one in each type of forest: spruce forest at the brook, sorrel spruce forest and birch forest. We did the work in three periods: I – 1975–1977, II – 1994–1996, III – 2010–2012. The collection of material was carried out annually: in August 3 times with an interval of 10 days (the species composition, number and air-dry biomass of basidiomes were taken into account), and in September once (only the species composition of fungi was taken into account). To date, we have established that the number of xylotrophic agaricoid fungi in the studied forest types varies from 60 (sorrel spruce forest) to 66 (birch forest). Most of the identified species belong to the Tricholomataceae family (37.7–43.3% of the total number of xylotrophic fungi species in each of the biogeocoenoses). There was an annual accumulation of detectable fungal species, with the largest number of species (67–75%) detected from 2 to 9 times, and 2–3% of them were permanent, occurring annually. There was relative stability of the species composition of higher vascular plants (Jaccard generality coefficient: J = 56–88) over time and more significant changes in the species composition of xylotrophic fungi (J = 36–50). The spruce forests had the most similarity of fungi species composition between the cenoses by periods (J = 44–52), and for the entire observation period, the sorrel spruce forest and birch forest had the maximum Jaccard index between cenoses (J = 56). The yield of xylotrophic agaricoid fungi in the studied cenoses varies by years of observation. The birch forest had the largest number and biomass of basidiomata for all the three periods of the research. A decrease in the number of basidiomes of xylotrophic fungi in the sorrel spruce forest was established with an increase in the average monthly air temperature in August (Spearman correlation coefficient: rs = −0.70). The sorrel spruce forest was the most favorable for the biota of xylotrophic agaricoid basidiomycetes, both in number (Shannon index: H = 1.23) and in biomass (H = 1.20), during all the research, since the biota of xylotrophes of the specified cenosis was more diverse and its components were most aligned.

Segmentation of choroidal area in optical coherence tomography images using a transfer learning-based conventional neural network: a focus on diabetic retinopathy and a literature review

BackgroundThis study aimed to evaluate the effectiveness of DeepLabv3+with Squeeze-and-Excitation (DeepLabv3+SE) architectures for segmenting the choroid in optical coherence tomography (OCT) images of patients with diabetic retinopathy.MethodsA total of 300 B-scans were selected from 21 patients with mild to moderate diabetic retinopathy. Six DeepLabv3+SE variants, each utilizing a different pre-trained convolutional neural network (CNN) for feature extraction, were compared. Segmentation performance was assessed using the Jaccard index, Dice score (DSC), precision, recall, and F1-score. Binarization and Bland-Altman analysis were employed to evaluate the agreement between automated and manual measurements of choroidal area, luminal area (LA), and Choroidal Vascularity Index (CVI).ResultsDeepLabv3+SE with EfficientNetB0 achieved the highest segmentation performance, with a Jaccard index of 95.47, DSC of 98.29, precision of 98.80, recall of 97.41, and F1-score of 98.10 on the validation set. Bland-Altman analysis indicated good agreement between automated and manual measurements of LA and CVI.ConclusionsDeepLabv3+SE with EfficientNetB0 demonstrates promise for accurate choroid segmentation in OCT images. This approach offers a potential solution for automated CVI calculation in diabetic retinopathy patients. Further evaluation of the proposed method on a larger and more diverse dataset can strengthen its generalizability and clinical applicability.

Logistics project risk response decision-making for global supply chain resilience and agility: an optimised case-based reasoning

ABSTRACT Logistics projects are critical to the functioning of the global supply chains (GSC), encountering various disruptions and risks. Sound Risk Response Decisions (RRDs) on logistics projects are imperative to ensure the GSC’s resilience and agility. Besides risk correlations, Project Interdependencies (PIs) and Limited Historical Information (LHI) impede RRDs. To address these challenges, an optimised Case-Based Reasoning combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Stratified Ordinal Priority Approach (SOPA) is proposed. Risks are categorised as referable and non-referable based on LHI using the Jaccard similarity. The strategies for referable risks are derived from similar cases retrieved through the weights of risks and project attributes and three similarities considering PIs. SOPA is developed to find weights with the Interdependent Uncertain Events (IUEs). DEMATEL is employed to access risk centrality degree similarity considering PIs and risk correlations. Expert input is sought for non-referable risk strategies. An optimisation model incorporating secondary risk correlations is built to generate strategies for all risks. The proposed approach is validated through a numerical example. Analysis informs that (1) PIs, LHI, and IUEs are necessary for sound RRDs; (2) decision outcomes are sensitive to logistics project managers who are risk-averse or exhibit moderate consideration of secondary risks.

A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean percentile Hausdorff distance (95HD) of . Whereas a mean 95HD of was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, Jaccard Index acquired from our pipeline, while was stated in their paper.

CURRENT STATUS OF SCABIOSA ISETENSIS POPULATIONS IN CENTRAL KAZAKHSTAN

The paper presents the results of studying the structure of 6 populations of Scabiosa isetensis growing in Central Kazakhstan. The floristic composition of communities, the ratio of life forms, ecological groups and age spectrum were evaluated. The floristic composition of the populations was 79 species from 56 genera and 23 families, of which the leading are the families of Asteraceae and Fabaceae. Herbaceous perennials predominate among life forms, mesophytes and xerophytes by ecological groups. Such a spectrum of families in populations with Scabiosa isetensis and ecological and biomorphic composition is a vivid indicator of the degree of habitat heterogeneity. Analysis of Jaccard's similarity indices showed similarity in species composition between populations 4 and 5; 1 and 2; 3 and 6, which is explained by similar habitat conditions. All studied populations are characterized by a single type of ontogenetic spectrum - left-handed type with predominance of juvenile individuals, i.e. populations with Scabiosa isetensis are young, developing, and suitable for practical use.

Biogeography of the Iranian snakes.

The events of the Cenozoic era such as mountain formation caused Iran to become one of the most amazing biodiversity hotspots in the world today. This pioneering study on Iranian snake biogeography integrates historical and ecological analyses. A phylogeographic review traces speciation and dispersal, while cluster analysis with a new snake checklist assesses faunistic similarities within Iran and its surroundings. Jaccard and Sorenson indices generate similarity dendrograms, Indicator Species Analysis pinpoints regional key species, and Endemism index calculates regional endemism rates, enriching our knowledge of Iran's species diversity. Phylogeographic analyses identify four biogeographical corridors for snake ingress into Iran: the Arabian region through southwestern Iran, the Western Asian mountainous transition zone via northwestern Iran, the Turanian region into northeastern Iran, and the Indus River Valley into southeastern and eastern Iran. Dendrogram analysis divides snake fauna into three groups. The first group associates western Zagros and Khuzestan fauna with the Sahara and Arabian regions. The second group links Kopet Dagh and Turkmen Steppe fauna with the Turanian region, and Central Plateau and Baluchistan fauna with the Iranian region. The third group connects northwest highlands, Alborz and Zagros mountains, and Caspian Sea coasts with the Western Asian Mountain transition zone. The study validates broad biogeographic patterns via ecoregional associations and indicator species analysis, providing finer resolution. Species like Platyceps najadum in Caspian Hyrcanian mixed forests exemplify ecoregional alignment, while Zagros and Alborz mountains exhibit unique faunal indicators, indicating species-level divergence. Shared indicators among widespread ecoregions reflect habitat continuity; exclusive indicators emphasize regional distinctiveness. Despite endemic species prevalence, they seldom act as significant indicators due to various factors. Our research confirms the Zagros Mountains, Khuzestan Plain, Alborz Mountains, and Persian Gulf coasts as snake diversity hotspots, marked by higher species richness compared to other Iranian regions.

How much is too much: A case study of local self-government units in Slovakia using absolute variability to determine the importance of financial criteria in MCDM analysis.

The performance evaluation of local self-government entities is very difficult, as their primary goal is not to make a profit, but to provide services to their residents that will contribute to an increase in their quality of life. In this context, it is necessary to evaluate their activity from the point of view of several available criteria, for which it is possible to find relevant and recognized sources. The presented research works with five criteria, identified by the Institute for Economic and Social Reforms, and aims to quantify the agreement of the results of the assessment of the financial health of territorial self-government entities in 2020 using the TOPSIS technique with a gradually decreasing number of criteria. For this purpose, a total of 26 combinations of criteria are created, with the number of 5, 4, 3 and 2 used criteria, the importance of which is determined based on their absolute variability using the standard deviation method. The results obtained in this way are interpreted using a wide range of mathematical and statistical methods including the Kolmogorov-Smirnov test, Levene test, Jaccard index and others. As a result, the multi-criteria evaluation of territorial self-government subjects (in our case, district cities) proved to be highly applicable. However, the result itself is largely determined by the structure and number of entry criteria. Based on the performed analyses, we can see that significant differences result from their reduction. Each such reduction has an impact on the overall results, but it is possible to find combinations that defy this conclusion.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

The Mycobiota of Library Books in Russia.

Abstract-Numerous studies of microorganisms isolated from the surface of cultural heritage objects, including library documents, are regularly carried out in different countries. Although the micromycete composition in each case varies, some species are constantly isolated. The structure of micromycete communities that inhabit library documents was studied in 57 cities of Russia located in seven federal districts (Northwestern, Central, Southern, Volga, Ural, Siberian, and Far Eastern). Micromycetes of 95 species from 32 genera were isolated and identified. The mycobiota of the library documents represented by Ascomycota has more than 90% of the species richness; Mucoromycota make up 3-9% and Basidiomycota, 3-4%. The Aspergillaceae family was the leading family: it accounted for 48.5-67.3% of the total species richness. In all regions, species diversity is moderate: the Shannon index ranged from 2.7 to 3.3. The Mcintosh species richness index is high everywhere (48-126), except in the Ural district (15.3). The McIntosh (0.76-0.84) and Pielow (0.80-0.91) dominance indices indicate a high level of species evenness in the mycobiota. The obtained values demonstrate the stability the document mycobiota in libraries from different regions. Significant species similarity between the districts was revealed by calculation of binary coefficients: the Jaccard coefficient was from 0.44 to 0.60; the Sørensen's qualitative measure of similarity was from 0.63 to 0.75; the quantitative similarity measure of Sørensen was from 0.44 to 0.71, and Morisita-Horn was from 0.66 to 1.0. The groups of dominant species in different regions are quite similar. The study of the ecological diversity of mycobiota on library books demonstrated the moderate diversity and the stability of the community. A high degree of similarity of taxonomic structures was established regardless of the climate conditions of the regions. Cosmopolitans characterized by high frequency of occurrence formed the major core of the library book mycobiota: Alternaria alternata, Aspergillus niger, Cladosporium cladosporioides, Mucor plumbeus, and Penicillium aurantiogriseum.

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.

Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture.

Gut Microbiota Alterations in Alzheimer's Disease: Relation with Cognitive Impairment and Mediterranean Lifestyle.

Recently, an association between dysbiosis of the gut microbiota (GMB) and the development of several diseases, such as Alzheimer's disease (AD), has been proposed. Dysbiosis involves changes in microbial diversity influenced by environmental factors, like diet or lifestyle. In this study, we investigated the role of GMB parameters in Spanish AD patients, assessing the impact of adherence to the Mediterranean lifestyle (ML), as well as to characterize these parameters in relation to neuropsychological, neuropsychiatric, emotional, and functionality parameters. A case-control study was conducted to investigate the association between the composition of the GMB and cognitive, emotional, neuropsychiatric, and functionality status in Spanish AD patients, along with a shotgun metagenomics approach. Richness and alpha-diversity were significantly lower in the AD group compared to the controls. PERMANOVA and ANOSIM tests of Bray-Curtis dissimilarity, Aitchison distance, and Jaccard similarity did not showed significant differences in beta-diversity between the two groups. Moreover, associations between various phyla of the AD group and orientation performance, food consumption, and activities of daily living were identified. Dysbiosis observed in Spanish AD patients is characterized by reductions in richness and alpha-diversity, alongside alterations in GMB composition, which may be linked to adherence to the ML and cognitive and functionality symptoms.

Clustering-based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks.

This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions. Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data. K-means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method. We identified two stable clusters representing high- and low-risk diabetes groups in both biobanks. The high-risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low-risk clusters, respectively. Notably, males were predominant in the high-risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high-risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high-risk group (P < 0.001). Kaplan-Meier curves indicated significant differences in diabetes complication incidences between clusters. UL effectively identified risk-specific groups within prediabetes populations, with high-risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.