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A novel method for optic disc localization using fast circlet transform and Chan-Vese segmentation
Abstract Accurate localization and segmentation of the optic disc (OD) are considered crucial for the early detection of ophthalmic diseases such as glaucoma and diabetic retinopathy. Challenges such as image quality variability, high background noise, and insufficient edge information are often encountered by existing methods. To address these issues, an adaptive framework is proposed in which Fast Circlet Transformation (FCT) is combined with entropy-based features derived from retinal blood vessels for robust OD localization. Minkowski weighted K-means clustering is utilized to dynamically assess feature importance, thereby enhancing resilience to dataset variations. Following localization, partial differential equation-based image inpainting is employed for blood vessel removal, and OD segmentation is refined using the Chan-Vese active contour model. The method’s localization efficacy is demonstrated through extensive evaluations across multiple public datasets (DRISHTI-GS, DRIONS-DB, IDRID, and ORIGA), and segmentation performance metrics, including Dice coefficients of 0.94–0.95 and Jaccard indices of 0.9, are achieved on the ORIGA and DRISHTI-GS datasets. Through these results, the robustness and generalizability of the proposed method for clinical applications in retinal image analysis are highlighted.
Acid-less direct gram scale exfoliation of graphite to partially oxidized graphene
Nature perception and positive emotions in urban forest parks enhance subjective well-being
Industry 5.0 paradigm transformation adoption in developing countries: an analytical game theoretic model
Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability
ALS/FTD-linked TBK1 deficiency in microglia induces an aged-like microglial signature and drives social recognition deficits in mice
Addition of longer wavelength absorbing chlorophylls into crops could increase their photosynthetic productivity by 26%
Self-healing Cu single-atom catalyst for high-performance electrocatalytic CO2 methanation
Response splicing quantitative trait loci in primary human chondrocytes identify putative osteoarthritis risk genes
Dopaminergic signaling regulates microglial surveillance and adolescent plasticity in the mouse frontal cortex
Abstract Adolescence is a sensitive period for frontal cortical development and cognitive maturation, marked by heightened structural plasticity in the dopaminergic (DA) mesofrontal circuit. However, the cellular and molecular mechanisms underlying this plasticity remain unclear. Here, we show that microglia, the brain’s innate immune cells, are highly responsive to mesofrontal DA signaling during adolescence. Longitudinal in vivo two-photon imaging in mice reveals that frontal cortical microglia increase their surveillance of the parenchyma and DA axonal boutons following rewarding experiences or optogenetic stimulation of DA axons. Microglial contacts with DA axons consistently precede bouton formation, and microglia-bouton interactions are regulated by D1- and D2-type DA receptors in adolescence and adulthood. Furthermore, microglial purinergic receptor P2RY12 signaling is necessary for enhanced microglial surveillance and DA bouton formation during adolescence. These results uncover bidirectional interactions between DA signaling and microglial surveillance that drive adolescent frontal plasticity and identify potential targets for restoring plasticity in adulthood.
Non-equilibrium critical scaling and universality in a quantum simulator
Helicobacter hepaticus promotes hepatic steatosis through CdtB-induced mitochondrial stress and lipid metabolism reprogramming
An open-source software for building and simulating ordinary differential equation models in biology
Mathematical and computational modeling are transforming biological research by enabling detailed exploration of complex systems. However, building computational models of biological phenomena often demands expertise in mathematical modeling and programming, creating barriers for researchers without such backgrounds. Existing software tools for computational modeling are frequently complex, overly general, or proprietary, limiting their accessibility and usability. To overcome these challenges, we present ODE-Designer, an open-source software tool that facilitates the construction and simulation of Ordinary Differential Equation (ODE) models in biology. A central feature of ODE-Designer is its intuitive visual interface, designed to be user-friendly and accessible. The software includes a graphical user interface with a node-based editor that allows users to create models without writing code. It supports model simulation and automatically generates the corresponding code, enabling efficient model exploration and aiding students in understanding core modeling principles. We propose that ODE-Designer serves as a valuable resource for both research and education in computational biology, improving accessibility and promoting a quantitative perspective in biological research. The software is freely available at: https://github.com/ufsj-dcomp/ode-designer-rs/.
Spatial isotope deep tracing deciphers inter-tissue metabolic crosstalk
Modelling in-hospital length of stay: A comparison of linear and ensemble models for competing risk analysis
Length of Stay (LoS) for in-hospital patients is a relevant indicator of efficiency in healthcare. Moreover, it is often related to the occurrence of hospital-acquired complications. In this work, we aim to explore time-to-event analysis for modelling LoS. We employed competing risk models (CR), as we considered two mutually exclusive outcomes: favorable discharge and deterioration. The explanatory variables included the patient’s sex, age, and longitudinal vital signs collected from a dataset comprising N=19,602 admissions. To address sparse measurements, we transformed longitudinal vital signs into cross-sectional statistics. Our approach involves data pre-processing, imputation of missing data, and variable selection. We proposed four types of CR models: Cause-specific Cox, Sub-distribution hazard, and two variants of Random Survival Forests, with both generalised Log-Rank test (cause-specific hazard estimates) and Gray’s test (cumulative incidences estimations) as node splitting rules. Performance in LoS CR models was evaluated over a time frame from 2 to 15 days. Additionally, we considered baselines with two well-established clinical early warning scores the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS). The best model was Random Survival Forest using Gray’s test split, with Integrated Brier Score[×100] of 0.386, C-Index above 99%, and Brier Score below 0.006, along the entire time frame. Employing cross-sectional statistics derived from vital signs, along with rigorous data pre-processing, outperformed the degree of correctness of modelling LoS, compared to NEWS and MEWS.
Five reasons why Nepal struggles to attract women into science
Efficient conversion of polyethylene to light olefins by self-confined cracking and reforming
Temporal trend in the national and sub-national burden of cancers attributable to risk factors in Iran from 1990 to 2021: Findings from the global burden of disease study 2021
Background Cancer is among leading causes of death globally and in Iran. However, studies exploring cancer risk factors trends in Iran are scarce. In this study, we provide estimations of risk-attributable cancer burden at the national and subnational levels in Iran from 1990 to 2021. Methods This study utilized data from the Global Burden of Disease (GBD) 2021 Study to estimate cancer-related years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life years (DALYs), and deaths attributable to behavioral, metabolic, and environmental/occupational risks in Iran nationally and subnationally, from 1990 to 2021. Summary exposure values (SEV) were given to assess the level of exposure. All estimations were reported along with 95% uncertainty intervals (UI). Results In 2021, 29.2% (95% UI: 22.9%–35.7%) of cancer deaths, equaling 16,893 (13,332–20,914) deaths and age-standardized rate of 22.66 (17.90–28.14), were attributable to risk factors in Iran. Since 1990, the number of risk-attributable cancer deaths increased by 192% (146% to 242%). Regarding attributable DALYs and deaths, the key risk factors were tobacco, dietary risks, and high body-mass index (BMI), with high BMI and high fasting plasma glucose increasing by two-fold in DALYs. Tracheal, bronchus, and lung cancer, followed by colorectal cancer and stomach cancer, had the highest risk-attributable number of DALYs and deaths in both sexes. The risk-attributable age-standardized DALY rates for ovarian cancer [207% (87%–382%)], thyroid cancer [198% (74%–294%)], and multiple myeloma [192% (98%–349%)] showed the most significant increases. Conclusions The all-age number of cancer deaths attributable to risk factors have increased in Iran. The age-standardized DALY rates attributable to high BMI and high FPG doubled from 1990 to 2021, indicating the emerging role of metabolic risk factors in cancer burden. These insights will guide effective cancer prevention strategies in Iran.